Description:
TECHNICAL FIELD
[001] The disclosed subject matter relates generally to the field of cloud computing and cloud resource management. More particularly, the present invention pertains to an automated system and method for optimizing cloud resource allocation and cost management across multiple users and instances.

BACKGROUND
[002] Cloud computing has revolutionized the way organizations, including educational institutions, handle their computing and storage needs. The advent of cloud computing platforms, such as Amazon Web Services (AWS), has offered a more flexible, scalable, and cost-effective alternative to traditional on-premises data centers.

[003] However, the utility-based model of cloud computing comes with its own set of challenges, particularly concerning resource management and cost optimization. Each cloud account has the capability to deploy an unlimited number of servers or instances across various regions, making it difficult to control resource utilization efficiently.

[004] In educational settings, such as universities and colleges, the problem is exacerbated. Students are often required to create and use multiple instances for learning and assignments. Due to lack of oversight or understanding, students frequently create instances in unassigned regions and fail to terminate them after use. This results in a phenomenon often referred to as 'EC2 Swarming,' where a plethora of unused or idle instances continue to accrue costs.

[005] Traditional methods for controlling this resource sprawl and associated costs have proven to be ineffective. Manual oversight is practically impossible given the scale, and piecemeal automation solutions are inadequate and often result in poor user experience. When the monthly or quarterly bills arrive, educational institutions find themselves faced with significant, unexpected costs that run into thousands, if not millions, of dollars.

[006] The current market lacks a comprehensive solution tailored for managing cloud resources in a manner that is both user-friendly and cost-effective. Existing solutions are either too complex, requiring specialized knowledge to operate, or are not designed to meet the specific needs and challenges faced by educational institutions.

[007] Hence, there is a need for an automated system and method for cloud resource management and cost optimization that can efficiently control resource allocation, prevent wasteful practices like EC2 swarming, and provide a streamlined, cost-effective cloud computing experience.

SUMMARY
[008] The following invention presents a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure and it does not identify key/critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

[009] The exemplary embodiments of the present disclosure pertain to a system and method for automated cloud resource management and cost optimization.
[0010] The objective of the present disclosure is to provide an automated system and method for managing cloud resources, particularly focusing on Amazon Web Services (AWS) EC2 instances, in a manner that is both efficient and cost-effective.

[0011] Another objective of the present disclosure is to eliminate the complexities and efforts required in manually controlling cloud resources, thereby creating a streamlined and user-friendly experience.

[0012] Another objective of the present disclosure is to offer purpose-based access to cloud resources for users, thereby avoiding the risks associated with unrestricted access, such as the creation of excessive or unneeded instances, often referred to as 'EC2 Swarming.

[0013] Another objective of the present disclosure is to provide educators with a tool for creating tailor-made lab environments for each student, enabling governance over the entire lab environment in terms of usage time, resource allocation, and security aspects.

[0014] Another objective of the present disclosure is to automate the task of creating and assigning access keys, selecting instance types and regions, and other complexities that users typically face.

[0015] Another objective of the present disclosure is to provide a policy-governed environment wherein students have access to AWS resources strictly in accordance with the labs assigned to them, thereby minimizing the risks of creating unauthorized or unnecessary EC2 instances in undesired regions or availability zones.

[0016] Another objective of the present disclosure is to provide a comprehensive management portal suitable for different levels of users, such as administrators, educators, and students, whose roles are assigned based on the nature of activities they perform.

[0017] Another objective of the present disclosure is to integrate Lab extension capabilities, allowing for flexible adjustment of lab timings based on specific educational or organizational needs.

[0018] Another objective of the present disclosure is to implement a feature for bulk start/stop actions for EC2 instances, thereby facilitating large-scale cloud operations without requiring manual intervention for each instance.

[0019] Another objective of the present disclosure is to incorporate timer-based EC2 lab scheduling, enabling pre-set times for labs to automatically start and stop, reducing idle time and associated costs.

[0020] Another objective of the present disclosure is allowing instructors or administrators to govern cloud resources without being physically present at the educational or organizational location.

[0021] Another objective of the present disclosure is to provide pre-cost estimation capabilities for lab provisioning, offering users insight into potential costs before commencing any lab session.

[0022] Another objective of the present disclosure is to monitor the runtime cost of lab resources thereby allowing for real-time budgetary control and adjustments.

[0023] Another objective of the present disclosure is to establish a unique workflow for Students, Instructors, and Super Admins/Admins, each having distinct permissions and capabilities.

[0024] Another objective of the present disclosure is to provide an end-to-end performance proof of work for assessment modules, ensuring that all resources used in the lab contribute to educational or organizational objectives effectively.

[0025] Another objective of the present disclosure is to support immutable hardware profiles with custom Amazon Machine Images (AMIs), thereby allowing organizations to create standardized, unchangeable hardware configurations for specific tasks or operations.

[0026] Another objective of the present disclosure is One-click access automation to EC2 instances using PEM key injection to simplify the authentication process.

[0027] Another objective of the present disclosure is to offer a solution that could result in significant cost savings—potentially up to 77% reduction in monthly cloud bills for organizations—and improve resource allocation and governance in cloud computing environments.

[0028] In an exemplary embodiment of the present disclosure, the Cloud Resource Cost Optimization Engine in the First Computing Device may include a User Interaction Module and a Resource Consumption Module. These modules may collaborate with the Backend Services Module of the Cloud Server, specifically with the Policy Management Layer and the Resource Allocation Layer. The objective may be to enable students to engage with cloud resources in an optimal manner while potentially conforming to institutional policies. The Resource Consumption Module, in particular, may interact with the Resource Allocation Layer to ensure efficient usage and allocation of resources like EC2 instances, thereby contributing to the goal of cost optimization.

[0029] Another exemplary embodiment of the present disclosure involves the Second Computing Device, designed for the Super Admin. The Governance Engine Module in this device may be responsible for policy execution, smart tagging, and event logging. It may work in coordination with the Policy Management Layer in the Cloud Server. Moreover, the User Management Module may manage bulk user configurations, email notifications, and account privileges, fulfilling the objective of robust administrative governance.

[0030] In a further exemplary embodiment of the present disclosure, the Third Computing Device, configured for the Admin role, may include a Monitoring Module and a Report Generation Module. These modules may interact with the Analytics and Monitoring Layer in the Backend Services Module of the Cloud Server. This integration may achieve real-time monitoring and reporting, thereby addressing the need for actionable insights into resource utilization and costs.

[0031] Yet another exemplary embodiment of the present disclosure involves the Fourth Computing Device, potentially designed for Instructors. It may include an Instruction Management Module and a Lab Management Module that may cooperate with the Resource Allocation Layer and the License Management Layer in the Cloud Server. These modules may facilitate the scheduling, resource allocation, and assessment for educational labs, contributing to the objectives of effective educational resource management and assessment.

[0032] In another exemplary embodiment of the present disclosure, the Cloud Server may contain multiple layers within its Backend Services Module. These could include Policy Management Layer, Analytics and Monitoring Layer, Resource Allocation Layer, Billing and Cost Management Layer, Audit and Logging Layer, Security Compliance Layer, Data Backup and Recovery Layer, License Management Layer, Event-Trigger Management Layer and Disaster Recovery Layer. These layers may work collectively and in conjunction with modules in the various Computing Devices to achieve the overarching objectives of automated cloud resource management and cost optimization.

BRIEF DESCRIPTION OF THE DRAWINGS
[0033] FIG. 1 presents a block diagram that schematically illustrates the architecture of the Cloud Resource Cost Optimization Engine, serving as the central module in an exemplary system engineered for automated cloud resource management and cost optimization.

[0034] FIG. 2 presents a block diagram that outlines the architecture of the Student Interface, highlighting its sub-functional modules within the framework of the main Cloud Resource Cost Optimization Engine, in an exemplary system designed for automated cloud resource management and cost optimization.

[0035] FIG. 3 illustrates a block diagram detailing the architecture of the Super Admin Interface, which incorporates various sub-functional modules governed by the main Cloud Resource Cost Optimization Engine, in an exemplary system aimed at automated cloud resource management and cost optimization.

[0036] FIG. 4 presents a block diagram that outlines the architecture of the Admin Interface, featuring a range of sub-functional modules directed by the main Cloud Resource Cost Optimization Engine, in an exemplary system designed for automated cloud resource management and cost optimization.

[0037] FIG. 5 illustrates a block diagram detailing the architecture of the Instructor Interface, which encompasses various sub-functional modules governed by the central Cloud Resource Cost Optimization Engine, in an exemplary system aimed at automated cloud resource management and cost optimization.

[0038] FIG. 6 presents a block diagram that outlines the structure of the Cloud Server, featuring the Backend Services Module as a critical component operating within the framework of the overarching Cloud Resource Cost Optimization Engine, in an exemplary system designed for automated cloud resource management and cost optimization.

[0039] FIG. 7 provides a block diagram that delves into the finer architecture of the Backend Services Module, illustrating multiple sub-functional modules that work cohesively within this module, as part of the larger Cloud Resource Cost Optimization Engine, in an exemplary system dedicated to automated cloud resource management and cost optimization.

[0040] FIG. 8 is a block diagram illustrating the details of a digital processing system in which various aspects of the present disclosure are operative by execution of appropriate software instructions.

[0041] FIG. 9 provides a block diagram of an exemplary system layout for automated cloud resource management and cost optimization.

[0042] FIG. 10 illustrates a block diagram detailing the roles and hierarchy among multiple users in an exemplary system for automated cloud resource management and cost optimization.

[0043] FIG. 11 presents a block diagram that outlines the exemplary architecture of the system for automated cloud resource management and cost optimization.

[0044] FIG. 12 illustrates a screen diagram showcasing the dashboard view as seen by the Super Admin. This view is central to the Super Admin Interface and provides a comprehensive overview of cloud resource status, user activity, and cost metrics, enabling efficient management and optimization of cloud resources.

[0045] FIG. 13 displays a screen diagram that presents the dashboard view as experienced by the Student. This dashboard serves as the central hub within the Student Interface, offering real-time insight into active cloud resources, ongoing lab assignments, and resource consumption statistics, thereby facilitating streamlined interaction with the cloud environment.

[0046] FIG. 14 illustrates a screen diagram that shows the dashboard view tailored for the Admin. This dashboard acts as the control panel within the Admin Interface, providing an at-a-glance overview of system-wide resource utilization, user activity, and critical alerts. It serves as the operational hub for administrators, allowing for efficient management of cloud resources and configurations.

[0047] FIG. 15 presents a screen diagram that reveals the dashboard view specifically designed for the Instructor. This dashboard forms the core of the Instructor Interface, offering a comprehensive summary of lab activities, student performance, and available resources. It functions as the centralized platform for instructors, facilitating the effective coordination and management of educational content and lab environments.

[0048] FIG. 16 is a flow diagram depicting an exemplary method for automated cloud resource management and cost optimization.

[0049] FIG. 17 is a flow diagram depicting another exemplary method for automated cloud resource management and cost optimization.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS
[0050] It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

[0051] The use of “including”, “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item. Further, the use of terms “first”, “second”, and “third”, and so forth, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

[0052] Referring to FIG. 1, the diagram may present a block diagram that schematically illustrates the architecture of the Cloud Resource Cost Optimization Engine. The overall image, labeled as 100, may serve as a comprehensive view of the system's layout, featuring multiple computing devices and their interfaces, all of which may be interconnected to form an integrated cloud management system. Label 102 may represent the First Computing Device, which is designed to potentially host the Student Interface, denoted by label 104. This interface may be a part of the Cloud Resource Cost Optimization Engine, labeled as 106, and may primarily focus on facilitating user interaction with cloud resources, allowing students to manage, start, and stop EC2 instances among other functionalities.

[0053] Moving on to label 108, it may denote the Second Computing Device, configured to possibly host the Super Admin Interface, indicated by label 110. Similar to the Student Interface, the Super Admin Interface may also operate under the Cloud Resource Cost Optimization Engine, allowing for comprehensive governance and policy execution. This may include features like smart tagging, event logging, and advanced user management functionalities. Label 112 may signify the Third Computing Device, which could be responsible for hosting the Admin Interface, marked by label 114. This interface may also be a component of the Cloud Resource Cost Optimization Engine and may provide functionalities for monitoring and report generation, in addition to resource configuration capabilities like AMI and EC2 management.

[0054] The Fourth Computing Device, potentially labeled as 116, may host the Instructor Interface, depicted by label 118. Like the other interfaces, it may operate within the confines of the Cloud Resource Cost Optimization Engine. This interface may provide unique functionalities focused on educational content management, lab management, and student assessment. Lastly, label 120 may represent the Cloud Server, which could serve as the backbone of the entire system. The Cloud Server may also contain a subset of the Cloud Resource Cost Optimization Engine, designed to execute backend tasks that may ensure seamless operations and governance across all computing devices and interfaces. In summary, FIG. 1 may serve as an overarching illustration of how the Cloud Resource Cost Optimization Engine integrates various interfaces and functionalities across multiple computing devices, contributing to efficient cloud resource management and cost optimization.

[0055] In addition to the various computing devices and their associated interfaces, FIG. 1 may also depict a network framework that interconnects all the elements, facilitating communication and data exchange among them. This network, not explicitly labeled in FIG. 1 but integral to the overall architecture, may operate on various possible networking protocols and methodologies to ensure seamless functionality of the Cloud Resource Cost Optimization Engine across different interfaces.

[0056] According to the non-limiting exemplary embodiment of the invention, the network may be implemented using a variety of configurations without limiting the scope of the invention. For instance, the network may be a Local Area Network (LAN), Wide Area Network (WAN), Virtual Private Network (VPN), or even a global network like the Internet. Other variants may include, but are not limited to, intranets, extranets, cellular networks, satellite networks, and hybrid networks that incorporate elements of the aforementioned types. Furthermore, the network may utilize various communication standards like TCP/IP, HTTP/HTTPS, FTP, or other suitable protocols that facilitate robust, secure, and efficient data exchange.

[0057] The network may also support different security measures, such as firewalls, encryption algorithms, and secure socket layers, to ensure the integrity and confidentiality of the data being transferred. Additionally, it may provide the necessary infrastructure for real-time data analytics, event logging, and other backend services integral to the Cloud Resource Cost Optimization Engine's functionality. In summary, the network may play a crucial role in integrating the different components depicted in FIG. 1, making it possible for the Cloud Resource Cost Optimization Engine to manage cloud resources and optimize costs effectively across multiple computing devices.

[0058] Referring to FIG. 2, the diagram presents a block diagram that outlines the architecture of the Student Interface, highlighting its sub-functional modules within the framework of the main Cloud Resource Cost Optimization Engine. Label 200 designates the overall image of this architecture, while label 104 specifically marks the Student Interface, which operates under the overarching Cloud Resource Cost Optimization Engine.

[0059] Label 202 identifies the User Interaction Module, which may serve as the first point of contact for students interacting with the cloud resources. This module may facilitate various user activities such as login, resource selection, and navigation through the interface. It may also provide real-time feedback, notifications, or even tutorials to guide the students in making the most out of the cloud resources available to them. Label 204 points to the Resource Consumption Module, which may be responsible for managing the actual usage of cloud resources by the student. This could include the initiation, monitoring, and termination of cloud resources like EC2 instances, databases, or storage units. The module may also integrate with billing or cost-tracking services to provide students with real-time estimates or summaries of their resource consumption, thereby aiding in cost optimization.

[0060] Both of these sub-functional modules—User Interaction and Resource Consumption—may closely collaborate to offer a cohesive and user-friendly experience for the students. They may also connect to the network for data exchange, cloud resource management, and other backend functionalities, thereby operating harmoniously within the larger ecosystem of the Cloud Resource Cost Optimization Engine. In summary, FIG. 2 may encapsulate the detailed architecture of the Student Interface and its key modules, all designed to operate seamlessly within the context of the main Cloud Resource Cost Optimization Engine.

[0061] Referring to FIG. 3, the diagram illustrates a block diagram detailing the architecture of the Super Admin Interface, which incorporates various sub-functional modules governed by the main Cloud Resource Cost Optimization Engine. Label 300 designates the overall image of this particular architecture, while label 110 specifically represents the Super Admin Interface, operating under the auspices of the Cloud Resource Cost Optimization Engine. Label 302 identifies the Governance Engine Module, a critical component that may be tasked with enforcing policies, managing smart tagging, and logging events for audit and compliance. This module may play an essential role in the overall health and security of the cloud resources, setting standards and protocols that are to be followed by other connected interfaces and computing devices.

[0062] Next, Label 304 points to the User Management Module. This module may be responsible for managing the bulk configuration of users, including but not limited to account creation, role assignments, and email notifications. It may also control access privileges, ensuring that users have the right level of access to resources based on their roles and responsibilities. Lastly, Label 306 represents the Config Management Module, which may handle various types of configurations within the system such as maximum allowable resource limits, license management, and other global settings that may apply across multiple users or even entire departments.
[0063] All these sub-functional modules—Governance Engine, User Management, and Config Management—may be intricately linked to perform their roles in a cohesive manner. They are designed to interact through a network for real-time data exchange, policy propagation, and other vital backend operations. These interactions may be enabled by various types of networks, which could include but are not limited to, LAN, WAN, cloud-based networks, or even a hybrid of these. To sum up, FIG. 3 may offer a comprehensive look into the architecture and functionalities of the Super Admin Interface, emphasizing its pivotal role in cloud resource management and cost optimization, all while operating within the larger framework of the Cloud Resource Cost Optimization Engine.

[0064] Referring to FIG. 4, the diagram presents a block diagram that outlines the architecture of the Admin Interface, featuring a range of sub-functional modules directed by the main Cloud Resource Cost Optimization Engine. Label 400 represents the overall image of this unique architecture, while Label 114 specifically denotes the Admin Interface, which operates under the guidance of the Cloud Resource Cost Optimization Engine. Label 402 identifies the Monitoring Module, which may serve a crucial role in overseeing the health and performance of cloud resources. This module may include capabilities like real-time tracking, event logging, and the display of essential metrics on a user-friendly dashboard.

[0065] Next, Label 404 refers to the Report Generation Module. This submodule may be tasked with generating and managing various reports such as usage statistics, cost breakdowns, and compliance checks. It may collect and analyze data from different components and user activities to provide insights that can inform future resource allocation and optimization strategies. Label 406 represents the Resource Configuration Module, which may focus on managing specific cloud resource configurations like Amazon Machine Images (AMIs), EC2 instances, and regional settings. This module may allow admins to set up, modify, or terminate cloud resources based on requirements, budget constraints, or other operational policies.

[0066] All these sub-functional modules—Monitoring, Report Generation, and Resource Configuration—may work in tandem, interlinked through a network for real-time communication and operational efficiency. These interactions may be facilitated by a variety of network types, which may include but are not limited to, Local Area Networks (LAN), Wide Area Networks (WAN), cloud-based networks, or even a combination of these. In summary, FIG. 4 may offer an in-depth view into the Admin Interface, highlighting its role in maintaining operational efficiency, compliance, and cost-effectiveness, all governed by the main Cloud Resource Cost Optimization Engine.

[0067] Referring to FIG. 5, the diagram illustrates a block diagram detailing the architecture of the Instructor Interface, which may encompass various sub-functional modules governed by the central Cloud Resource Cost Optimization Engine. The overall image is represented by the label 500, and it aims to offer a comprehensive view of how the Instructor Interface integrates into the larger system. Label 118 identifies the Instructor Interface, a key component of the Cloud Resource Cost Optimization Engine. This interface may serve as the principal portal for instructors to manage various aspects of cloud resource and educational content.

[0068] Within the scope of the Instructor Interface, three primary sub-functional modules are outlined: Label 502 refers to the Instruction Management Module, which may allow instructors to curate and distribute educational content, manage syllabi, and control other instructional resources. This module could provide capabilities like uploading lessons, tracking student progress, and offering real-time feedback. Label 506 is tied to the Assessment Module, where instructors may have the ability to create and manage assessment metrics, including the end-to-end performance proof of work. This module could facilitate exams, quizzes, and other evaluative criteria to gauge student learning and engagement. These sub-functional modules may work in concert, each contributing specific functionalities but unified under the primary objective of the Cloud Resource Cost Optimization Engine to optimize cloud resource allocation and cost. Through a network, which may be of various forms including but not limited to wired, wireless, local area, or wide-area networks, the Instructor Interface is designed to communicate effectively with other computing devices and cloud servers for seamless cloud management.

[0069] Referring to FIG. 6, the diagram presents a block diagram that outlines the structure of the Cloud Server, featuring the Backend Services Module as a critical component that may operate within the framework of the overarching Cloud Resource Cost Optimization Engine. The Overall Image is represented by the label 600, providing a holistic view of how the Cloud Server integrates into the larger system architecture. The Cloud Server, denoted by label 106, may serve as the central hub for processing, data storage, and network coordination. It may be designed to work in unison with multiple computing devices, each catering to different user roles such as students, administrators, and instructors, to ensure optimized cloud resource allocation and cost management.

[0070] Label 602 identifies the Backend Services Module, a crucial part of the Cloud Resource Cost Optimization Engine situated within the Cloud Server. This module may be responsible for executing various backend tasks that facilitate and govern the operations of all other modules situated in the computing devices. The Backend Services Module could carry out functions like data analytics, policy management, and resource allocation to support the objectives of cloud resource and cost optimization. Like other modules, the Backend Services Module may communicate with other computing devices and user interfaces through a network. This network could include various forms including but not limited to wired, wireless, local area, or wide-area networks, providing flexibility and scalability for the entire system. Overall, FIG. 6 aims to delineate how the Cloud Server and its Backend Services Module may play a pivotal role in the comprehensive strategy for automated cloud resource management and cost optimization.

[0071] Referring to FIG. 7, the diagram provides a block diagram that delves into the finer architecture of the Backend Services Module, illustrating multiple sub-functional modules that may work cohesively within this module, as part of the larger Cloud Resource Cost Optimization Engine. The Overall Image, labeled 700, offers a comprehensive view of the Backend Services Module and its various sub-modules designed to support and facilitate cloud resource and cost optimization.

[0072] The Backend Services Module, identified by label 602, serves as a cornerstone of the Cloud Resource Cost Optimization Engine. It may comprise several sub-modules or layers, each catering to a specific functionality necessary for the seamless operation of the system. Policy Management Layer, indicated by 702, may be responsible for storing, updating, and distributing global policies that govern resource usage across all connected devices. Analytics and Monitoring Layer, denoted by 704, may focus on real-time data collection, analysis, and alert generation based on predefined triggers or thresholds. Resource Allocation Layer, labeled as 706, may manage the allocation and deployment of cloud resources across different labs, users, and computing devices based on global policies. Billing and Cost Management Layer, signified by 708, may handle centralized billing, cost estimation, and financial reporting, providing detailed insights into cloud expenditure.
[0073] Audit and Logging Layer, marked by 710, may maintain a centralized, secure, and immutable log of all activities for compliance and auditing purposes. Security Compliance Layer, identified by 712, may manage security policies, perform automated compliance scans, and flag non-compliant activities for immediate remediation. Data Backup and Recovery Layer, denoted by 714, may oversee automated data backup processes and recovery protocols, ensuring data integrity and availability. License Management Layer, indicated by 716, may manage the procurement, allocation, and compliance of software licenses used across various labs and interfaces.

[0074] Event-Trigger Management Layer, labeled as 718, may be responsible for setting up and managing event-based triggers to initiate actions or alerts based on specific conditions. Finally, the Disaster Recovery Layer, represented by 720, may orchestrate and manage disaster recovery protocols to ensure quick restoration of services in the event of system failures or other catastrophic events. In summary, FIG. 7 may serve as a detailed architectural blueprint for the Backend Services Module, illustrating how it may play a critical role in the overall strategy for cloud resource management and cost optimization.

[0075] Referring to FIG. 8 is a block diagram 800 illustrating the details of a digital processing system 800 in which various aspects of the present disclosure are operative by execution of appropriate software instructions. The Digital processing system 800 may correspond to the computing devices (or any other system in which the various features disclosed above can be implemented).

[0076] Digital processing system 800 may contain one or more processors such as a central processing unit (CPU) 810, random access memory (RAM) 820, secondary memory 830, graphics controller 860, display unit 870, network interface 880, and input interface 890. All the components except display unit 870 may communicate with each other over communication path 850, which may contain several buses as is well known in the relevant arts. The components of Figure 8 are described below in further detail.

[0077] CPU 810 may execute instructions stored in RAM 820 to provide several features of the present disclosure. CPU 810 may contain multiple processing units, with each processing unit potentially being designed for a specific task. Alternatively, CPU 810 may contain only a single general-purpose processing unit.

[0078] RAM 820 may receive instructions from secondary memory 830 using communication path 850. RAM 820 is shown currently containing software instructions, such as those used in threads and stacks, constituting shared environment 825 and/or user programs 826. Shared environment 825 includes operating systems, device drivers, virtual machines, etc., which provide a (common) run time environment for execution of user programs 826.

[0079] Graphics controller 860 generates display signals (e.g., in RGB format) to display unit 870 based on data/instructions received from CPU 810. Display unit 870 contains a display screen to display the images defined by the display signals. Input interface 890 may correspond to a keyboard and a pointing device (e.g., touch-pad, mouse) and may be used to provide inputs. Network interface 880 provides connectivity to a network (e.g., using Internet Protocol), and may be used to communicate with other systems (such as those shown in Figure 1) connected to the network.

[0080] Secondary memory 830 may contain hard drive 835, flash memory 836, and removable storage drive 837. Secondary memory 830 may store the data software instructions (e.g., for performing the actions noted above with respect to the Figures), which enable digital processing system 800 to provide several features in accordance with the present disclosure.

[0081] Some or all of the data and instructions may be provided on removable storage unit 840, and the data and instructions may be read and provided by removable storage drive 837 to CPU 810. Floppy drive, magnetic tape drive, CD-ROM drive, DVD Drive, memory, removable memory chip (PCMCIA Card, EEPROM) are examples of such removable storage drive 837.

[0082] Removable storage unit 840 may be implemented using medium and storage format compatible with removable storage drive 837 such that removable storage drive 837 can read the data and instructions. Thus, removable storage unit 840 includes a computer readable (storage) medium having stored therein computer software and/or data. However, the computer (or machine, in general) readable medium can be in other forms (e.g., non-removable, random access, etc.)

[0083] In this document, the term "computer program product" is used to generally refer to removable storage unit 840 or hard disk installed in hard drive 835. These computer program products are means for providing software to digital processing system 1400. CPU 810 may retrieve the software instructions, and execute the instructions to provide various features of the present disclosure described above.

[0084] The term “storage media/medium” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical disks, magnetic disks, or solid-state drives, such as storage memory 830. Volatile media includes dynamic memory, such as RAM 820. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid-state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip or cartridge.

[0085] Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fibre optics, including the wires that comprise bus (communication path) 1450. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.

[0086] Referring to FIG. 9, the diagram provides a block diagram of an exemplary system layout for automated cloud resource management and cost optimization. The Overall Image, labeled 900, encapsulates the various elements that may interact in a coordinated manner to facilitate automated cloud resource management and cost-effective optimization. Users (Students), as indicated by label 902, represent the end-users who may interface with the system to utilize cloud resources for educational or training purposes. These users may interact directly with the Cloud Resource Cost Optimization Engine, marked by 904, to request resources, view resource consumption metrics, and adjust settings to optimize costs. Admins, marked by 906, may represent system administrators or IT professionals responsible for managing and maintaining the entire ecosystem. They may have access to a specialized Admin Interface that interfaces with the Cloud Resource Cost Optimization Engine to provide more advanced control and reporting features.

[0087] The AWS Console, also known as the Command Center, is represented by 908. This may serve as the central hub for managing AWS resources and services, connecting with the Cloud Resource Cost Optimization Engine to enact policies and make adjustments based on real-time data. Region 1 AWS Console Software, labeled 910, and Region 2 AWS Console Software, labeled 912, may signify software instances deployed in different geographical locations. These may serve to manage localized resource allocation and cost-optimization strategies, feeding data back to the central AWS Console and Cloud Resource Cost Optimization Engine for a more comprehensive view. EC2 Instances, depicted by 914, symbolize the virtual computing environments that may be provisioned and managed by the system. These instances can be monitored, modified, or terminated based on policies set by the Cloud Resource Cost Optimization Engine and Admins, as well as requests made by the Users. In summary, FIG. 9 may illustrate a high-level architecture that showcases how various roles and components may work together to achieve automated cloud resource management and cost optimization. The configuration presented may offer an exemplary way to understand the integrations and operations of the system components.

[0088] Referring to FIG. 10, the diagram illustrates a block diagram detailing the roles and hierarchy among multiple users in an exemplary system for automated cloud resource management and cost optimization. The Overall Image, denoted by label 1000, provides a visual depiction of the hierarchy, demonstrating how the various roles may interact and relate to one another within the framework of the system. Starting with Level 1, labeled 1002, the role of Super Admin, marked by 1004, may serve as the highest authority in the management hierarchy. The Super Admin may have overarching control and decision-making power concerning system-wide configurations, user management, and cost optimization strategies. Proceeding to Level 2, indicated by label 1006, we encounter the role of the Admin, as represented by 1008. Admins may function under the Super Admin and might be responsible for tasks like resource allocation, reporting, and localized management. They may interface directly with the Super Admins for policy directions and can further delegate tasks to the subsequent levels.

[0089] At Level 3, marked by 1010, the role of Instructor is introduced, labeled 1012. Instructors may focus on creating and managing educational content, including labs and assessments. They might have the ability to view resource consumption metrics for their specific courses but may not have as much control over system-wide settings as Admins or Super Admins. Lastly, Level 4, denoted by 1014, represents the Student role, indicated by 1016. Students may be the end-users of the system, consuming resources for educational purposes and interacting mainly through a student interface. They may have the least administrative control but are nonetheless essential for the system’s main objective of educational training in a cloud environment. In summary, FIG. 10 may effectively outline the hierarchical roles and responsibilities of various users in an automated cloud resource management and cost optimization system. The structure portrayed may be exemplary and serve to guide the understanding of role-based access and functionalities within the system.

[0090] Referring to FIG. 11, the block diagram outlines the exemplary architecture of the system for automated cloud resource management and cost optimization. The Overall Image, labeled as 1100, sets the stage for understanding the interconnected elements that make up the system's architecture. Starting with the Application Infrastructure, designated by label 1102, this segment may act as the main operational ground where the application logic resides. Within this infrastructure, Start Labs, marked by 1104, may handle the initiation of various lab environments. EC2 Labs, designated by 1106, may be a specific type of lab environment where cloud resources are allocated. For seamless interaction with cloud services, the architecture may incorporate REST API, labeled 1108, and AWS SDK, labeled 1110. These components may facilitate the communication between the application and the deployed infrastructure, enabling tasks like resource provisioning, monitoring, and adjustments to be made in an automated manner.

[0091] Focusing on the Deployed Infrastructure, marked by 1112, it serves as the physical or virtual space where the system's cloud resources are allocated. Within this domain, the architecture may feature a VPC (Virtual Private Cloud), indicated by 1114, connected to an Internet Gateway, as labeled by 1116. This setup may provide the necessary network connectivity for the system to function efficiently. For execution and real-time adjustments, a Runtime Engine, marked by 1118, may interact with various services such as Route 53 (1120) for DNS services, Cloud Watch (1122) for monitoring, and SNS (1124) for notifications. EMAIL Notifications, as indicated by 1126, can also be dispatched based on specific triggers or conditions. Lastly, the architecture may include Users, marked by 1128, who could be anyone interacting with the system, such as administrators, instructors, or students. They may use the system in accordance with their roles and permissions, as outlined in the hierarchical structure of FIG. 10. In summary, FIG. 11 may offer a detailed view of the system’s architecture, revealing the interconnection and potential functionalities of each component in an exemplary manner. The architecture is designed to achieve the objectives of cloud resource management and cost optimization, although the exact implementation details may vary.

[0092] Referring to FIG. 12, the screen diagram illustrates the dashboard view as seen by the Super Admin. This view is central to the Super Admin Interface and provides a comprehensive overview of cloud resource status, user activity, and cost metrics, enabling efficient management and optimization of cloud resources. The Overall Image, designated by label 1200, serves as the backdrop for the various screen elements that are critical to the dashboard's functionality. The Dashboard, one of the primary screen elements, may act as the hub for an array of critical data points and functionalities. It may provide real-time or periodic updates that can be customized to fit the needs of the Super Admin.

[0093] Total Quota, another key screen element, may display the aggregated quota limits set for cloud resources across various dimensions such as compute, storage, and network. This information may be crucial for monitoring resource allocation and for preventing overutilization that could lead to increased costs. The Total Instances element may show the number of currently active or reserved instances across all lab environments and projects. This snapshot could be important for understanding the current workload and for making immediate or future adjustments. Moving on to the Labs section, this screen element may provide an overview of all active labs, either on a project or organizational level. Within this context, Labs Quota may indicate the quota assigned to each lab, allowing the Super Admin to make adjustments as necessary to ensure efficient resource usage.

[0094] Last but not least, the Labs Progress may offer insight into the real-time or historical utilization of each lab. This can include metrics such as completion rates, active instances, and other customizable indicators. This information can be vital for the Super Admin to monitor ongoing projects and to predict future resource requirements. In summary, FIG. 12 may furnish the Super Admin with a panoramic view of key metrics and functionalities, all aimed at achieving optimal management and cost-effectiveness in cloud resource usage. The specifics may vary based on the exact implementation, but the overarching goal remains the same: efficient management and optimization of cloud resources.

[0095] Referring to FIG. 13, the screen diagram displays the dashboard view as experienced by the Student. This dashboard serves as the central hub within the Student Interface, offering real-time insight into active cloud resources, ongoing lab assignments, and resource consumption statistics, thereby facilitating streamlined interaction with the cloud environment. The Overall Image, represented by label 1300, serves as the comprehensive setting for the various screen elements integral to the dashboard's utility.

[0096] The Dashboard screen element may act as the main interface from which the Student can navigate to different functionalities and information points. It may offer the Student a snapshot of the current state of cloud resources and academic projects, customized according to their individual needs or course requirements. Total Quota, a significant screen element, may display the collective resource limits allocated to the Student for tasks like computing, storage, and networking. This quota could be pivotal for self-monitoring and ensuring that resource utilization stays within permissible limits. The Instances element may provide a real-time or near-real-time view of the virtual machines or other resources the Student has initiated. It could be invaluable for keeping track of active instances, their statuses, and the corresponding costs. Moving to My Labs, this element may offer a detailed list or overview of ongoing or completed lab assignments. It could help the Student manage time and resources efficiently by providing insights into deadlines, required resources, and current progress.

[0097] Within My Labs, the Labs Quota element may indicate specific resource allocations for each lab assignment. This allows the Student to manage resource utilization better and avoid over-consumption that could result in penalties or additional costs. Similarly, Labs Progress may show real-time updates or historical data about the Student’s progression through each lab assignment. This could help the Student in better understanding the completion status and what steps may be needed to finalize the project or assignment. The Access Key screen element may serve as a secure gateway for the Student to interact with specific cloud resources. This key can offer an additional layer of security and control, ensuring that only authorized users gain access to allocated resources. In summary, FIG. 13 may offer the Student a comprehensive interface for interacting with cloud resources and academic projects. The dashboard is designed to provide a cohesive user experience, albeit the specific features and functionalities may vary based on the implementation and the educational institution's requirements.

[0098] Referring to FIG. 14 illustrates a screen diagram that shows the dashboard view tailored for the Admin. This dashboard acts as the control panel within the Admin Interface, providing an at-a-glance overview of system-wide resource utilization, user activity, and critical alerts. It serves as the operational hub for administrators, allowing for efficient management of cloud resources and configurations. The Overall Image, labeled as 1400, encapsulates all the key screen elements that form this dashboard. The Dashboard screen element may serve as the primary interface for the Admin, from where various functionalities and modules can be accessed. This includes everything from resource allocation to user management and reporting tools. Total Quota may be a vital screen element displaying the total amount of cloud resources allocated across the system. This would include computing power, storage, and other services, providing the Admin with a high-level view of current resource commitments and remaining availability.

[0099] The Total Instances screen element may provide the Admin with real-time data on the number and types of instances running across the system. This could be crucial for monitoring load, performance, and the operational health of the cloud environment. The Labs screen element may show an overview of all ongoing, pending, or completed lab activities system-wide. This information could help the Admin in keeping track of how resources are being utilized for educational projects and may aid in future resource planning. Under the Labs category, Labs Quota may display the specific allocation of resources for each lab activity. This would help the Admin to understand the resource commitments for individual labs, allowing for real-time adjustments to prevent over or under-utilization. Labs Progress may offer a detailed status report on each lab, including completion rates, time taken, and resource usage. This information could be valuable for both administrative review and for feedback to instructors and students. In summary, FIG. 14 may provide the Admin with a comprehensive, centralized interface for managing and monitoring the cloud resource landscape. It is designed for maximal operational efficiency and oversight, although specific features and functionalities may vary based on system implementation and organizational requirements.
[00100] Referring to FIG. 15 presents a screen diagram that reveals the dashboard view specifically designed for the Instructor. This dashboard forms the core of the Instructor Interface, offering a comprehensive summary of lab activities, student performance, and available resources. It functions as the centralized platform for instructors, facilitating the effective coordination and management of educational content and lab environments. The Overall Image, labeled as 1500, includes various screen elements constituting this comprehensive dashboard. The Dashboard screen element may serve as the starting point for Instructors, aggregating all the essential functionalities and modules that they need to navigate. This could include lab setup, student performance tracking, and resource allocation. The Total Quota screen element may indicate the overall cloud resources that are allocated for the Instructor's usage. This high-level view can provide insight into the resources available for current and future lab sessions.

[00101] Total Instances may display real-time data on the number of active instances tied to the Instructor's account. This information can be essential for managing the virtualized lab environments and ensuring they are operating optimally. The Labs screen element may give the Instructor a broad perspective on the active, pending, and completed lab assignments. This can be crucial for scheduling and resource planning. Under the Labs section, Labs Quota may offer detailed insights into the allocation of resources per lab, enabling the Instructor to make informed decisions about resource distribution across different lab activities. Labs Progress may serve as a monitoring tool that shows the advancement of each lab session in terms of completion and resource consumption. This can be valuable for understanding the learning pace and needs of the students. Assigned Labs may provide a list of lab assignments that have been handed out to the students. This could facilitate tracking and managing student progress in real-time. Lab Mapping may offer a schematic representation or a list detailing the correlation between different labs and the resources allocated to them. This element can help in streamlining the organization of various lab activities. In summary, FIG. 15 aims to provide a robust and intuitive interface for Instructors. It may allow for streamlined management of educational content and lab environments, although specific functionalities may differ based on the system's overall architecture and organizational policies.

[00102] Referring to FIG. 16, the flow diagram may depict an exemplary method for automated cloud resource management and cost optimization. The diagram provides a holistic view with multiple interconnected steps that may facilitate both efficiency and cost-effectiveness in the utilization of cloud resources. Beginning with the overall figure, labeled as 1600, the diagram may encompass various steps aiming to fulfill its intended objectives. The step at 1602 may involve verifying the identity of users such as students, administrators, and instructors to possibly grant appropriate levels of access. This step may be crucial for maintaining security and targeted access to cloud resources. Following 1602, step 1604 may be concerned with customizing and setting up lab environments that are tailored to specific educational objectives or other requirements. This customization may meet the unique needs of each user or user group.

[00103] This may be followed by step 1606, which could dynamically assign cloud resources like EC2 instances, memory, and CPUs to each lab environment, based on predefined or dynamic criteria. Efficient resource allocation may be instrumental for optimizing performance and costs. Step 1608 may establish rules governing resource consumption, including, but not limited to, maximum usage time, instance types, and operational regions. This step may help in achieving better control and minimizing resource wastage. Subsequent to this, step 1610 may make the customized lab environments available for access to selected users, such as individual students or groups. This access may facilitate actual usage and hands-on experience. Step 1612 may continuously monitor the usage of cloud resources to ensure they are in alignment with predefined criteria, a process which may contribute to effective resource management.

[00104] Next, step 1614 could implement timers or triggers that may start, stop, or modify instances based on user activities or pre-established schedules. These controls may further aid in efficient resource and cost management. Step 1616 may focus on detecting and potentially shutting down rogue or unnecessary services and instances, reducing the likelihood of resource sprawl and unintended costs. The step indicated by 1618 may involve sending timely alerts or notifications to users and administrators regarding any anomalies, completions, or violations. Various channels like emails or system notifications may be utilized for this purpose. Step 1620 may be involved in collecting data on resource utilization, user activities, and incurred costs for analytical and reporting objectives. This data collection may serve as the basis for future adjustments and refinements.

[00105] Step 1622 could continuously analyze gathered metrics to make real-time adjustments to resource allocations, which may aid in cost optimization. Step 1624 may modify user access levels or available resources based on performance, requirements, or any changes in predefined criteria, ensuring adaptive and efficient resource usage. Step 1626 may store historical data, analytics, and user activities for possible future analysis or audits, providing a framework for continual improvement and compliance checks. Finally, step 1628 may periodically reassess the entire process, making necessary adjustments to lab definitions, resource allocations, and user access controls for ongoing optimization. Overall, FIG. 16 may offer a comprehensive and dynamic method that can be instrumental in achieving effective cloud resource management and cost optimization.

[00106] Referring to FIG. 17, the flow diagram depicts another exemplary method that may be employed for automated cloud resource management and cost optimization. The diagram starts with step 1700, which may serve as an overview of the entire process. Subsequent to this, step 1702 may involve establishing communication between each of the computing devices and the cloud server. This communication may be facilitated using a cloud resource optimization engine that could be implemented in each computing device and the cloud server. Following step 1702, step 1704 may be concerned with facilitating general interaction between the first computing device and the cloud server. This may occur via a user interaction module in the cloud resource optimization engine of the first computing device.

[00107] Step 1706 may then focus on managing the consumption of cloud resources, such as EC2 instances and storage. This management may be overseen by a resource consumption module in the cloud resource optimization engine of the first computing device. The method may continue to step 1708, which may execute policies, manage user configurations, and configure maximum limits and licenses. These tasks may be performed via a governance engine module, a user management module, and a config management module in the cloud resource optimization engine of the second computing device. Subsequently, step 1710 may involve monitoring resource usage and generating usage reports. These activities may be facilitated by a monitoring module and a report generation module in the cloud resource optimization engine of the third computing device.

[00108] Following this, step 1712 may manage educational content, lab scheduling, and performance assessment. These tasks may be handled via an instruction management module, a lab management module, and an assessment module in the cloud resource optimization engine of the fourth computing device. Step 1714 may then execute backend tasks for each module implemented in the first, second, third, and fourth computing devices. These tasks may be carried out via a backend services module in the cloud resource optimization engine of the cloud server. Lastly, step 1716 may involve storing, updating, and propagating global policies that govern resource usage across all connected devices. This may be performed via a policy management layer in the backend services module of the cloud server. Overall, the method described may offer a multifaceted approach to automated cloud resource management and cost optimization, incorporating various modules and layers for enhanced governance, management, and optimization.

[00109] According to the non-limiting exemplary embodiment of the invention, the described cloud-based application aims to assist educators utilizing AWS for their daily instructional activities. This application enables instructors to construct customized lab environments for each student, and manage the entire lab setting from start to finish in terms of usage time, resource allocation, and other security measures. It establishes a managed-cloud environment that may be easily navigated by students, while administrators may ensure that there are no unexpected billing issues or unintended use of EC2 instances.

[00110] With this application, instructors may no longer need to concern themselves with selecting the appropriate AWS instance types or regions, nor with the creation and assignment of access keys. Once instructors define a lab and set a time duration for its completion, the system may automate these tasks. Upon publishing a lab, a designated number of EC2 instances may become available to each assigned student through their individual portal. Students may then have the flexibility to initiate the lab, utilize it productively, and terminate it once done. If a student fails to shut down the lab, the system may automatically deactivate the lab after the elapsed assigned duration, contributing to efficient resource use and cost savings.

[00111] This application operates as a layer on top of AWS Labs, allowing students to interact only with this managed layer instead of the AWS console directly. This architecture eliminates the need for AWS administrators to create individual IAM accounts for students. Instead, their accounts may be provisioned within this application itself. If left unrestricted, students might potentially initiate countless EC2 instances across the multiple regions and availability zones offered by AWS. However, this managed layer ensures that students can only access their EC2 instances under the confines of policy-governed usage aligned with their assigned labs.

[00112] As a consequence, educational institutions may not have to worry about the kinds of resources students are using, the duration of usage, or their geographical placement within AWS. The application may thereby offer effective control over these aspects, potentially resulting in monthly cloud billing savings of over 77% for the organization.

WORKING EXAMPLE
[00113] Upon reviewing the impact of the cloud-based application on lowering cloud costs, the results indicate significant savings for educational institutions. For the purposes of this analysis, let's consider an institution with approximately 3,000 students. Traditionally, each student was allocated 2 instances for their labs during a semester. These instances, equivalent to 4 vCPUs with 8 GB RAMs, would run continuously for around eight hours per day. However, the actual utilization time was often greater, as students may have left the instances running for extended periods. Additionally, there was a high likelihood of students adding and running unnecessary services, leading to what can be described as EC2 sprawls and considerable cost overruns.

[00114] Before the implementation of the cloud-based application, the institution reported monthly AWS costs of at least 300 USD per student. Management costs were additional, requiring one cloud admin per 500 students. Essentially, at least six cloud admins were employed, working regular 8-hour shifts, five days a week, at a rate of 50 USD per hour. The institution estimated monthly management expenses to be around 53,000 USD, equivalent to approximately 18 USD per student.
[00115] After the implementation of the cloud management application, the EC2 sprawls were eliminated. Instances were assigned predefined durations, post which they stopped automatically. Furthermore, since students interacted with the application layer instead of AWS directly, the chances of them inadvertently starting and running unauthorized services and instances were virtually nullified. According to estimates, the monthly AWS cloud infrastructure costs dropped to around 70 USD per student in the post-implementation period.

[00116] Moreover, the automation capabilities of the application allowed for more efficient administration. One cloud admin could now effectively manage the entire network, thereby reducing the cloud management costs to approximately 8,800 USD per month, or merely 3 USD per student. These financial variations are summarized in the table below, and they demonstrate the substantial cost-saving potential that may be realized by educational institutions through the implementation of this cloud-based application for managing AWS resources.
Pre-Installation of the Application Post- Installation of the Application
Cost component Cost
(in USD) Cost Justification Cost
(in USD) Cost Justification
AWS infrastructure cost per student per month 300 EC2 sprawls due to unstopped instances and inessential AWS services 70 Timed instances and no scope of starting inessential services
Cloud Management Cost per student per month 18 Manual management of the entire cloud lab infrastructure 3 Automated management of the cloud labs infrastructure
Total Cost per student per month (in USD) 318 73
Total Cost for all students per month (in USD) 954000 219000

[00117] Consequently, conservative estimates indicate that the cloud-based application reduced the overall cost of operating cloud labs by more than 77%. This aligns with the previous analysis, substantiating the significant cost-saving potential that may be realized by educational institutions through the implementation of this cloud management solution.

[00118] According to the non-limiting exemplary embodiment of the present invention, the cloud management solution has demonstrated significant cost-saving benefits, particularly for educational institutions utilizing cloud-based labs. This solution serves as an interface between the end-users, such as students, and the cloud infrastructure. It automates various processes such as access control, instance creation, and runtime management. As a result, it can notably reduce monthly cloud expenditures by averting unnecessary resource utilization, commonly known as EC2 sprawls. Additionally, operational management costs may also be reduced.

[00119] Given that the core application of this invention centers around the effective control of cloud resource utilization or 'cloud sprawls,' it has wide-ranging implications for multiple industry sectors. Considering the pervasive adoption of cloud technologies across various industries, there is significant potential for the broader application of this invention. With minimal adjustments to its original architecture, the technology may be adapted for diverse industrial applications, thereby extending its utility beyond educational settings.

[00120] Reference throughout this specification to “one embodiment”, “an embodiment”, or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases “in one embodiment”, “in an embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[00121] Although the present disclosure has been described in terms of certain preferred embodiments and illustrations thereof, other embodiments and modifications to preferred embodiments may be possible that are within the principles and spirit of the invention. The above descriptions and figures are therefore to be regarded as illustrative and not restrictive.

[00122] Thus the scope of the present disclosure is defined by the appended claims and includes both combinations and sub-combinations of the various features described hereinabove as well as variations and modifications thereof, which would occur to persons skilled in the art upon reading the foregoing description.

, Claims:We Claim:

1. A system for automated cloud resource management and cost optimization comprising:
a first computing device comprising a student interface, a second computing device comprising a super admin interface, a third computing device comprising an admin interface and a fourth computing device comprising an instructor interface, whereby each computing device connected to a cloud server via a network;

a cloud resource optimization engine implemented in each computing device and in the cloud server, configured to establish communication between the first computing device via the student interface, the second computing device via the super admin interface, the third computing device via the admin interface and the fourth computing device via the instructor interface;

the cloud resource optimization engine implemented in the first computing device further comprising a user interaction module and resource consumption module, whereby the user interaction module configured to facilitates general interaction with the cloud server, and the resource consumption module configured to manage the consumption of cloud resources, such as EC2 instances and storage;

the cloud resource optimization engine implemented in the second computing device further comprising a governance engine module, a user management module and a config management module, whereby the governance engine module configured for policy execution, smart tagging and event logging, the user management module configured to manage bulk user configuration, email notifications, and account privileges, and the config management module configured to manage maximum limits and license configurations;

the cloud resource optimization engine implemented in the third computing device further comprising a monitoring module, a report generation module, and a resource configuration module, whereby the monitoring module configured to includes dashboard configurations and management, the report generation module configured to generate and manage usage reports, and the resource configuration module configured to manage AMI, EC2, and region configurations;

the cloud resource optimization engine implemented in the fourth computing device further comprising an instruction management module, a lab management module, and an assessment module, whereby the instruction management module configured to manage creation and distribution of educational content, the lab management module configured to manage lab scheduling, resource allocation, and timer-based activities, and the assessment module configured to manage performance proof of work;

the cloud resource optimization engine implemented in the cloud server further comprising a backend services module configured to execute backend tasks for each module implemented in the first, second, third and fourth computing devices, thereby facilitating automated cloud resource management and cost optimization.

2. The system as claimed in claim 1, wherein the communication established between the cloud server and the computing devices includes not only basic data transfer but also encompasses functions for resource allocation, monitoring, reporting, and real-time adjustments to resource utilization for optimized performance and cost-efficiency.

3. The system as claimed in claim 1, wherein the resource consumption module is additionally configured to allocate GPU resources dynamically.

4. The system as claimed in claim 1, wherein the user management module is further configured to send automated alerts to super admins for account activity that violates predefined policies.

5. The system as claimed in claim 1, wherein the monitoring module in the third computing device further comprises real-time analytics capabilities to monitor system performance.

6. The system as claimed in claim 1, wherein the assessment module is further configured to allow instructors to manually override generated performance metrics.

7. The system as claimed in claim 1, wherein each computing device further comprises a local storage module for caching frequently accessed data.

8. The system as claimed in claim 1, wherein the backend services module in the cloud server further comprises a policy management layer configured to store, update, and propagate global policies that govern resource usage across all connected devices.

9. The system as claimed in claim 1, wherein the backend services module in the cloud server further comprises an analytics and monitoring layer configured to collect, analyze, and provide real-time data on resource utilization and costs, as well as to generate alerts based on predetermined triggers or thresholds.

10. The system as claimed in claim 1, wherein the backend services module in the cloud server further comprises a Resource Allocation Layer configured to control and optimize the allocation and deployment of cloud resources like EC2 instances across different labs, users, and computing devices based on global policies.

11. The system as claimed in claim 1, wherein the backend services module in the cloud server further comprises a Billing and Cost Management Layer configured to handle centralized billing, cost estimation, and financial reporting, providing detailed insights into overall cloud expenditure.

12. The system as claimed in claim 1, wherein the backend services module in the cloud server further comprises an Audit and Logging Layer configured to maintain a centralized, secure, and immutable log of all activities for compliance and auditing purposes.

13. The system as claimed in claim 1, wherein the backend services module in the cloud server further comprises a Security Compliance Layer configured to manage security policies, conduct automated scans for compliance, and flag non-compliant activities for immediate remediation.

14. The system as claimed in claim 1, wherein the backend services module in the cloud server further comprises a Data Backup and Recovery Layer configured to manage automated data backup and recovery processes, ensuring data integrity and availability.

15. The system as claimed in claim 1, wherein the backend services module in the cloud server further comprises a License Management Layer configured to oversee the procurement, allocation, and compliance of software licenses used across various labs.

16. The system as claimed in claim 1, wherein the backend services module in the cloud server further comprises an Event-Trigger Management Layer configured to set up and manage event-based triggers that can automatically initiate actions or alerts based on specific conditions.

17. The system as claimed in claim 1, wherein the backend services module in the cloud server further comprises a Disaster Recovery Layer configured to orchestrate and manage disaster recovery protocols, ensuring quick restoration of services in the event of a system failure or other catastrophic events.

18. A method for automated cloud resource management and cost optimization in a system comprising a first computing device with a student interface, a second computing device with a super admin interface, a third computing device with an admin interface, and a fourth computing device with an instructor interface, each connected to a cloud server via a network, the method comprising:
a) Establishing communication between each of the computing devices and the cloud server using a cloud resource optimization engine implemented in each computing device and in the cloud server;

b) Facilitating general interaction between the first computing device and the cloud server via a user interaction module in the cloud resource optimization engine of the first computing device;

c) Managing the consumption of cloud resources like EC2 instances and storage via a resource consumption module in the cloud resource optimization engine of the first computing device;

d) Executing policies, managing user configuration, and configuring maximum limits and licenses via a governance engine module, a user management module, and a config management module in the cloud resource optimization engine of the second computing device;

e) Monitoring resource usage and generating usage reports via a monitoring module and a report generation module in the cloud resource optimization engine of the third computing device;

f) Managing educational content, lab scheduling, and performance assessment via an instruction management module, a lab management module, and an assessment module in the cloud resource optimization engine of the fourth computing device;

g) Executing backend tasks for each module implemented in the first, second, third, and fourth computing devices via a backend services module in the cloud resource optimization engine of the cloud server;

h) Storing, updating, and propagating global policies that govern resource usage across all connected devices via a policy management layer in the backend services module in the cloud server.