Description:COMPUTER-IMPLEMENTED SYSTEM FOR CLOUD RESOURCE MANAGEMENT WITHIN SERVICE-ORIENTED ARCHITECTURE
Field of the Invention
[0001] The current disclosure relates to cloud computing architectures, specifically an enhanced service-oriented and object-oriented design model for efficient and dynamic resource management within a multi-tenant cloud environment.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Cloud computing has emerged as a transformative force in the realm of computer science, heralding an era where a central facility is no longer a mere repository or processing hub but a versatile resource capable of servicing myriad organizational computing demands concurrently. This paradigm shift is deeply intertwined with the proliferation of the Internet of Things (IoT), fog, edge, mist, and the advent of 5G networks, signposting an inevitable migration towards cloud-centric computing strategies. The essence of cloud computing is captured in its quintessential model, which posits the framework for enabling ubiquitous, convenient on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction.
[0004] Characterized by on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service, cloud computing has solidified its foundational pillars within a service-oriented architecture. The versatility of cloud services is encapsulated in a spectrum of offerings that include Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), alongside emergent modalities like Database as a Service (DaaS), Function as a Service (FaaS), and the all-encompassing Anything as a Service (XaaS). This evolutionary service model has garnered widespread adoption across a diverse landscape of sectors, including but not limited to government agencies, non-profit organizations, educational institutions, manufacturing industries, and service-oriented enterprises, each leveraging the cloud's service-oriented architecture to fuel their digital aspirations.
[0005] The multi-tenant nature of cloud computing introduces a critical challenge in resource allocation. The act of balancing the distribution of resources among numerous subscribers simultaneously is a nuanced choreography that cloud providers navigate through a series of intricate activities. These include service deployment, service orchestration, comprehensive cloud service management, and the ever-critical tenets of security and privacy. Central to these activities is the formidable task of resource allocation, an endeavor fraught with complexity and inherent computational difficulties, often likened to the notorious NP-hard problem set. In addition to these, the cloud is burdened with the task of ensuring the reusability and preservation of effective resource allocation strategies, demanding a shared lexicon and robust interface design to facilitate coherent communication with the external world.
[0006] However, the ambit of challenges does not terminate at resource allocation. Quality of service (QoS), fault tolerance, and energy consumption are pivotal concerns that perpetually loom over the operational integrity of cloud services. Moreover, the dynamicity intrinsic to service requirements commands an adaptable framework capable of agile reconfigurations in response to shifts in geographic location, temporal constraints, user activities, and the intricate dance of interactions between agents and communication protocols. The capacity to remain resilient and adaptive in the face of such fluctuations is a non-negotiable requisite for the effective management of the cloud computing milieu.
[0007] In the wake of these multifaceted challenges, the proposed disclosure articulates an Improved Service-Oriented Architecture based on an Object-Oriented Design Pattern Model specifically tailored for Cloud Resource Management. This novel architecture stands as a testament to innovation, addressing the prevalent pain points with dexterity and precision. The model introduces a suite of mechanisms that empower subscribers with the ability to initiate and submit service requests with unprecedented ease. In parallel, it equips providers with the tools to offer and tailor resources in alignment with these requests, ensconced within a framework that embraces efficiency and dynamism.
[0008] Central to this architectural masterpiece is the dynamic strategy unit, a nexus that orchestrates the ebb and flow of service requests and resources. The strategy unit is a receptacle for inputs from a waiting queue and heuristic data pertaining to historical requests and resource configurations, serving as the cerebral core from which resource management strategies are derived and refined. The strategies extrapolated from this reservoir of data and experience are meticulously applied to sculpt the behaviour of cloud environments and their corresponding resource allocation mechanisms.
[0009] By interweaving the principles of object-oriented design with a robust service-oriented architecture, the disclosure delivers a paradigm of cloud resource management that is both sophisticated and intuitive. It assures a seamless, streamlined, and flexible workflow that can gracefully accommodate the inherent dynamism of cloud service demands. With a strategic unit that evolves in real-time, learning from the past to inform the future, the disclosure encapsulates the zenith of innovation in cloud computing—a beacon that guides the path towards a more resilient, efficient, and user-centric cloud ecosystem.

Summary
[00010] The current disclosure relates to cloud computing architectures, specifically an enhanced service-oriented and object-oriented design model for efficient and dynamic resource management within a multi-tenant cloud environment.
[00011] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00012] The following paragraphs provide additional support for the claims of the subject application.
[00013] In an embodiment, the disclosed computer-implemented system is tailored to manage resources efficiently in a cloud computing setting, which is vital for modern service-oriented architectures. The system is equipped with a request interface that can handle a multitude of resource requests from a diverse subscriber base. This interface is not only designed to process these requests but also to preserve solutions from previous successful allocations, thereby enhancing reusability and system intelligence over time.
[00014] In an embodiment, the system includes a resource provisioning module, which is tasked with the critical function of allocating and configuring cloud resources in response to subscriber requests. This module employs a shared vocabulary and standardized communication interfaces, ensuring clear and consistent interactions between subscribers and cloud resources. This standardization is essential for maintaining service coherence and facilitating easier resource management.
[00015] In an embodiment, a key component of the system is the dynamic strategy unit, which is intricately linked to both the request interface and the resource provisioning module. This unit manages a waiting queue, systematically organizing incoming requests and coordinating the subsequent allocation of resources. The unit’s capability to adapt to service requirements changes dynamically, accounting for factors such as location, time, and subscriber activity, making it a versatile tool in resource management.
[00016] In an embodiment, the dynamic strategy unit incorporates advanced statistical models, particularly autoregressive models, to predict future resource allocation needs. This predictive capability allows for anticipatory adjustments in resource management, leading to a more streamlined allocation process that can effectively pre-empt and adapt to changing demand patterns.
[00017] In an embodiment, the disclosed system also tackles the complex, NP-hard problem of resource allocation. The resource allocation algorithm is specifically crafted to navigate this computational challenge, striking a balance between optimizing resource use and managing allocation intricacies within the cloud environment.
[00018] In an embodiment, the system's dynamic strategy unit is enhanced by integrating quality of service (QoS) parameters directly into the resource allocation algorithm. By doing so, the system ensures that the allocation strategy not only meets the technical requirements but also aligns with the service quality expectations of the subscribers.
[00019] In an embodiment, the system is fortified with a fault tolerance module, which plays a critical role in maintaining service continuity. This module is designed to effectively handle potential allocation failures, ensuring that resources are available consistently and subscriber services are uninterrupted.
[00020] In an embodiment, within the resource provisioning module, there is an embedded mechanism for energy consumption optimization. This feature is crucial for reducing the environmental footprint of cloud resource management, aligning with the growing need for sustainable technology solutions.
[00021] In an embodiment, the disclosed method for managing cloud resources reflects the system's capabilities and emphasizes an object-oriented, pattern-based approach. This method provides an adaptable and predictive framework for real-time resource allocation that caters to the evolving requirements of a dynamic cloud environment, thereby optimizing operational efficiency and service quality.
Brief Description of the Drawings
[00022] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00023] FIG. 1 illustrates a computer-implemented system for cloud resource management within a service-oriented architecture, in accordance with the embodiments of the present disclosure;
[00024] FIG. 2 illustrates a method for managing cloud resources in a service-oriented architecture, in accordance with the embodiments of the present disclosure;
[00025] FIG. 3 shows the class diagram for a cloud resource management, in accordance with the embodiments of the present disclosure; and
[00026] FIG. 4 illustrates a state transition diagram that details the lifecycle of a Request within the cloud resource management, in accordance with the embodiments of the present disclosure; and
[00027] FIG. 5 presents a sequence diagram that delineates the procedural flow for managing resources in a cloud environment, in accordance with the embodiments of the present disclosure.
Detailed Description
[00028] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00029] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00030] The current disclosure relates to cloud computing architectures, specifically an enhanced service-oriented and object-oriented design model for efficient and dynamic resource management within a multi-tenant cloud environment.
[00031] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00032] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practicing the present disclosure are also possible.
[00033] FIG. 1 illustrates a computer-implemented system 100 (interchangeably referred as cloud resource management system 100 or system 100) for cloud resource management within a service-oriented architecture, in accordance with embodiments of the present disclosure. The system 100 comprises a request interface 102, a resource provisioning module 104 and a dynamic strategy unit 106.
[00034] In an embodiment, the proposed cloud resource management system features a dynamic strategy unit that actively contributes to the optimization of resource utilization within the cloud environment. This unit is not merely reactive to incoming data; it possesses the capability to independently trigger actions such as the provisioning or decommissioning of resources. By incorporating predictive analytics, the unit can anticipate demand surges and adjust resource distribution proactively, ensuring optimal resource utilization and system performance even during peak loads. This anticipatory approach facilitates the maintenance of a balanced state in resource usage, avoiding both underutilization and overloading of the cloud infrastructure.
[00035] In an embodiment, the request interface of the cloud resource management system includes an advanced machine learning component that enhances its functionality. This component learns from subscriber usage patterns over time, enabling the system to understand and adapt to the unique resource request behaviors of different subscribers. By recognizing specific preferences and commonly used terminologies, the request interface can offer personalized service, improving the accuracy of resource allocations and the overall user experience. This adaptability ensures that the interface remains relevant and efficient as subscriber behaviours evolve.
[00036] In an embodiment, the cloud resource management system is crafted with a focus on high availability and reliability. The architecture of the dynamic strategy unit, in particular, is distributed across several geographic locations, providing redundancy and resilience against localized outages. This design ensures that the cloud services remain uninterrupted, which is essential for maintaining continuous operations for the subscribers. The multi-regional deployment of strategic components guarantees that system failures are localized and do not affect the broader network, enhancing the system's robustness against various failure modes.

[00037] In an embodiment, the system incorporates an energy consumption model within the dynamic strategy unit to allocate resources more responsibly. This model takes into account the energy footprint of cloud resources, allowing the system to make decisions that not only meet performance requirements but also optimize for energy efficiency. Such a model is integral for cloud providers looking to reduce their carbon footprint and promote sustainable computing practices. By balancing resource allocation with energy efficiency, the system can contribute to environmental sustainability while still providing the necessary computational power.
[00038] In an embodiment, the system features a user interface that grants subscribers real-time visibility into their resource consumption and the status of their requests. This transparency enables subscribers to monitor their resource utilization actively, facilitating better resource management and planning. Through this interface, subscribers can make informed decisions regarding their current and future resource needs, ensuring they can optimize their application performance and budgeting with greater precision.
[00039] In an embodiment, the cloud resource management system is designed to be highly adaptable, with configurations that can comply with diverse regulatory requirements. Subscribers from different jurisdictions can use the system with the assurance that their resource management aligns with local data protection and privacy laws. This adaptability is crucial for subscribers who are subject to strict regulatory environments and need to ensure that their cloud infrastructure is compliant at all times.
[00040] In an embodiment, the cloud resource management system is built to support an extensive array of cloud services. It can seamlessly manage resources for not only the traditional IaaS, PaaS, and SaaS models but also for more contemporary services such as FaaS and XaaS. By providing a broad range of services under its management umbrella, the system presents itself as a one-stop solution for various business needs, from government and educational institutions to manufacturing and service providers, ensuring that any cloud computing requirement can be met efficiently and effectively.
[00041] In an embodiment, the system is enhanced with a self-healing mechanism within its dynamic strategy unit. This feature allows the cloud resource management system to autonomously detect and resolve issues, such as resource shortages or system malfunctions, with minimal human intervention. By utilizing self-diagnosis and automatic corrective measures, the system can maintain optimal performance and availability, quickly recovering from potential disruptions and continuously adapting to maintain service levels.
[00042] In an embodiment, the system features an integrated audit trail within the dynamic strategy unit, which meticulously records every decision and action undertaken by the system. This log is invaluable for maintaining a clear and accountable record of the system’s operations. In the event of anomalies or security breaches, these detailed records can be crucial for conducting a thorough investigation, identifying the root cause, and implementing preventive measures to enhance the system's integrity.
[00043] In an embodiment, the system's dynamic strategy unit includes a scalability estimator tool that continuously assesses the growth trajectories of subscriber applications. This estimator advises the resource provisioning module on potential adjustments needed to support the predicted growth, ensuring that resources are scaled appropriately in advance. This proactive approach helps prevent performance bottlenecks and enables subscribers to maintain a high level of service as their applications grow and evolve.
[00044] In an embodiment, the system integrates a component for monitoring service level agreements (SLAs). This ensures that the allocated resources meet the predefined service standards agreed upon with subscribers. By actively monitoring SLA compliance, the system guarantees high-quality service delivery and reliability, fostering subscriber satisfaction and trust. It also provides a framework for accountability, where the cloud provider can be held to the promised performance metrics, further reinforcing the system's commitment to excellence.
[00045] In an embodiment, the system is enhanced by equipping the request interface with capabilities that promote reusability and preservation of previously successful resource allocation patterns. This allows the system to retain and apply effective allocation strategies, shortening the time required for resource provisioning and enhancing overall efficiency. This feature also encourages the accumulation of a knowledge base of allocation solutions, which can be referred back to when similar requests are encountered in the future, streamlining the resource allocation process and improving subscriber satisfaction by providing tried-and-tested solutions.
[00046] In an embodiment, the resource provisioning module of the system is engineered to incorporate a shared vocabulary and standardized interface for communications. This design facilitates a clear and effective exchange of information between the subscribers and the cloud resources, mitigating the possibility of misunderstandings and errors in resource allocation. The standardization of communication protocols ensures that all parties are on the same page, which is particularly beneficial in a multi-tenant cloud environment where clarity and consistency are essential for seamless operations.
[00047] In an embodiment, the dynamic strategy unit of the system comprises mechanisms specifically designed to adapt to dynamic changes in service requirements. These mechanisms consider varying factors such as geographic location, time zone differences, and subscriber activity patterns. This flexibility allows for a more personalized and responsive resource allocation, as the system can dynamically adjust resource provisioning in real-time to meet the ever-changing needs of the subscribers, thereby ensuring a high level of service and performance.
[00048] In an embodiment, the dynamic strategy unit within the system employs sophisticated autoregressive statistical models to anticipate future resource allocation demands. These predictive models analyze historical data to forecast upcoming requirements, allowing the system to manage resources proactively. By predicting peaks and troughs in demand, the system can prepare for them in advance, thus maintaining a balance between supply and demand and ensuring that resources are allocated where and when they are needed most.
[00049] In an embodiment, the system addresses the complex NP-hard problem of resource allocation by designing an advanced resource allocation algorithm. This algorithm takes into account the multifaceted nature of cloud environments, applying computational techniques that navigate through the complexities to provide efficient resource allocation solutions. By tackling this challenge, the system ensures that resources are utilized to their fullest potential while adhering to the constraints and requirements of the cloud computing environment.
[00050] In an embodiment, the dynamic strategy unit of the system integrates quality of service (QoS) parameters into its resource allocation decision-making process. This integration allows the system to not only allocate resources effectively but also to ensure that these resources meet the performance expectations set by subscribers. The QoS-aware resource allocation algorithm takes various metrics into account, such as latency, bandwidth, and reliability, to maintain a high standard of service delivery.
[00051] In an embodiment, the system includes a fault tolerance module specifically tailored to handle potential disruptions in resource allocation. This module is designed to detect and correct failures, ensuring that services remain available and reliable. The fault tolerance mechanism allows the system to quickly recover from anomalies, providing continuous service availability and minimizing downtime, thus protecting subscribers from the negative impacts of unexpected resource allocation issues.
[00052] In an embodiment, the resource provisioning module of the system is further refined to include algorithms focused on optimizing energy consumption. This feature enables the system to manage cloud resources in an environmentally conscious manner, aligning with sustainable practices. By minimizing the energy impact of cloud operations, the system not only reduces costs but also contributes to the reduction of the carbon footprint associated with cloud computing activities.
[00053] FIG. 2 illustrates a method 200 for managing cloud resources in a service-oriented architecture, in accordance with the embodiments of the present disclosure. At step 202, the process begins with the reception of resource requests from multiple subscribers via a dedicated request interface. These requests encompass a variety of cloud resource types and configurations necessary to support their respective services. At step 204, upon receiving the resource requests, a specialized resource provisioning module is activated. This module is responsible for allocating and configuring the required cloud resources in direct response to the received requests. It ensures that the resources are provisioned in a manner that aligns with the specific requirements of each subscriber. At step 206, a dynamic strategy unit takes charge of managing both the waiting queue and the ongoing resource allocation process. This unit employs a dynamic approach, meaning it adapts in real-time based on the changing demands and conditions within the cloud environment. At step 208, the method incorporates a crucial analytical step involving the examination of heuristic records. These records document past resource requests and allocations. By scrutinizing this historical data, the system gains valuable insights into patterns and trends, which play a pivotal role in making informed resource management decisions. At step 210, the dynamic strategy unit leverages an object-oriented design pattern-based resource allocation algorithm. This sophisticated algorithm is designed to dynamically reconfigure resources in real-time, aligning them precisely with the evolving requirements of subscribers and the prevailing conditions within the cloud environment.
[00054] FIG. 3 shows the class diagram for a cloud resource management, in accordance with the embodiments of the present disclosure. The cloud resource management incorporating classes such as Client, Request, Provider, Component, Resource, Account, Assignment, Queue, and Strategy, each imbued with specific attributes and historical data that are self-explanatory. Clients and Requests are tightly coupled, as are Providers, Components, and Resources, reflecting the interconnected nature of the system. The Strategy class acts as a fulcrum, connecting Requests and Resources to delineate a management strategy informed by historical allocations and set properties. Within this ecosystem, Clients initiate Requests, Providers generate Resources, and these elements are systematically queued. The Strategy class harnesses this information to forge validation rules and allocate resources efficiently, influencing the overarching policy for cloud service provision. Through the Assignment function, the Strategy orchestrates a match between queued Resources and Requests, triggering a chain reaction that updates the historical allocation data and kick-starts the billing cycle. As the ledgers for both Clients and Providers are updated, a new operational cycle commences, armed with enriched heuristics from previous interactions, thereby enabling the system to adapt and refine its resource management approach continually, fostering an evolving cloud environment that prioritizes efficiency and sustainability.
[00055] FIG. 4 illustrates a state transition diagram that details the lifecycle of a Request within the cloud resource management, in accordance with the embodiments of the present disclosure. As illustrated, the Request is systematically charted, commencing at the 'Created' state and culminating in either an 'Expiring' or 'Exiting' state, indicating the conclusion of its lifecycle. The Request undergoes a series of sequential transitions through a range of states: 'Loaded', where it is introduced into the system; 'Validated', ensuring it meets certain criteria; 'Allocated', where it is paired with resources; 'Rearranged', allowing adjustments based on system dynamics; 'Updated', reflecting any changes; 'Strategized', where long-term plans are formulated; 'Reloaded', which involves re-entering the system for further processing; and finally 'Unloaded', where it is removed from active consideration. Each state is reached through a deliberate progression that adheres to specific conditions outlined in Figure 4 of the diagram. This ensures that the Request does not arbitrarily jump between states, but moves in a controlled and predictable manner, allowing for efficient management and a clear understanding of the process at each stage. This structured flow ensures a disciplined and orderly management of Requests within the cloud resource management environment, maintaining integrity and traceability throughout the Request's tenure within the system.
[00056] FIG. 5 presents a sequence diagram that delineates the procedural flow for managing resources in a cloud environment, in accordance with the embodiments of the present disclosure. The sequence diagram elaborates on the specific operations involved, commencing with the creation of a request by a client and concurrent resource setup by a provider. FIG. 5 illustrates the dynamic interactions between the states of a request as outlined in FIG. 4. The process initiates when a request from client is entered into the queue of system, mirroring the action taken by the provider who readies and enlists resources into the same queue. A critical stage follows where both request and resource are subject to validation checks, ensuring that their attributes align with established allocation criteria and configuration guidelines. Upon successful validation, the assignment phase commences, transitioning into billing where the transactional details are accurately recorded and the relevant attributes of both the request and resource are accordingly updated. Subsequently, the request and its matched resource are either released for operation or reassessed for potential reloading based on their current state—whether they are to be maintained in an active state within the system or concluded and removed from the queue. This systematic procedure enables a synchronized and controlled management of cloud resources, ensuring an orderly transition from initiation through to completion, while adapting to the dynamic conditions presented by the request’s progression through its lifecycle states.
[00057] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00058] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00059] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00060] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00061] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims
I/We Claim:
1. A system for cloud resource management within a service-oriented architecture, the system comprising:
a request interface configured to receive and process resource requests from multiple subscribers;
a resource provisioning module designed to allocate and configure resources in response to the processed requests; and
a dynamic strategy unit communicatively coupled with the request interface and the resource provisioning module; the dynamic strategy unit configured to:
manage a waiting queue for the incoming requests and the allocation of resources;
analyze heuristic records of past requests and resource allocations to inform resource management decisions; and
implement a resource allocation algorithm based on the analyzed data, guiding the behavior of cloud environment resource allocations to optimize efficiency, reduce complexity, and improve fault tolerance; wherein the dynamic strategy unit utilizes object-oriented design patterns to facilitate the dynamic reconfiguration of resources in real-time based on subscriber requirements and cloud environment conditions.
2. The system of claim 1, wherein the request interface is further configured to support reusability and preservation of prior successful resource allocation solutions.
3. The system of claim 1, wherein the resource provisioning module is further configured to utilize a shared vocabulary and standardized interface for communication between subscribers and the cloud resources.
4. The system of claim 1, wherein the dynamic strategy unit further includes mechanisms to adapt to dynamic changes in service requirements concerning location, time, and subscriber activity.
5. The system of claim 1, wherein the dynamic strategy unit employs autoregressive statistical models to predict and manage resource allocation needs.
6. The system of claim 1, wherein the resource allocation algorithm is designed to address the NP-hard problem of resource allocation within the cloud computing environment.
7. The system of claim 1, wherein the dynamic strategy unit further incorporates quality of service (QoS) parameters into the resource allocation algorithm.
8. The system of claim 1, wherein the system includes a fault tolerance module configured to handle potential resource allocation failures and ensure continuous service availability.
9. The system of claim 1, wherein the resource provisioning module further includes energy consumption optimization algorithms to minimize the environmental impact of the cloud resource management.
10. A method for managing cloud resources in a service-oriented architecture, the method comprising:
receiving, via a request interface, resource requests from multiple subscribers;
allocating and configuring cloud resources in response to the requests using a resource provisioning module;
managing a waiting queue and resource allocation using a dynamic strategy unit;
analyzing heuristic records of past requests and resource allocations to inform resource management decisions; and
implementing, by the dynamic strategy unit, an object-oriented design pattern-based resource allocation algorithm that dynamically reconfigures resources in real-time according to subscriber requirements and cloud environment conditions; wherein the resource allocation algorithm optimizes efficiency, reduces allocation complexity, and improves overall cloud service quality.

COMPUTER-IMPLEMENTED SYSTEM FOR CLOUD RESOURCE MANAGEMENT WITHIN SERVICE-ORIENTED ARCHITECTURE
Abstract
The present disclosure relates to a computer-implemented system for efficient cloud resource management within a service-oriented architecture. This system integrates a request interface for processing resource requests from various subscribers, a resource provisioning module for allocating and configuring resources, and a dynamic strategy unit. The dynamic strategy unit is pivotal in managing a queue, analyzing historical resource allocation data, and executing a resource allocation algorithm that is informed by object-oriented design patterns. This algorithm dynamically adjusts resources in real-time, optimizing efficiency, reducing complexity, and enhancing fault tolerance in cloud environments. Additional features include support for reusability, utilization of shared communication protocols, adaptation to dynamic service requirements, predictive resource allocation modeling, and energy consumption optimization.
Fig. 1 , Claims:Claims
I/We Claim:
1. A system for cloud resource management within a service-oriented architecture, the system comprising:
a request interface configured to receive and process resource requests from multiple subscribers;
a resource provisioning module designed to allocate and configure resources in response to the processed requests; and
a dynamic strategy unit communicatively coupled with the request interface and the resource provisioning module; the dynamic strategy unit configured to:
manage a waiting queue for the incoming requests and the allocation of resources;
analyze heuristic records of past requests and resource allocations to inform resource management decisions; and
implement a resource allocation algorithm based on the analyzed data, guiding the behavior of cloud environment resource allocations to optimize efficiency, reduce complexity, and improve fault tolerance; wherein the dynamic strategy unit utilizes object-oriented design patterns to facilitate the dynamic reconfiguration of resources in real-time based on subscriber requirements and cloud environment conditions.
2. The system of claim 1, wherein the request interface is further configured to support reusability and preservation of prior successful resource allocation solutions.
3. The system of claim 1, wherein the resource provisioning module is further configured to utilize a shared vocabulary and standardized interface for communication between subscribers and the cloud resources.
4. The system of claim 1, wherein the dynamic strategy unit further includes mechanisms to adapt to dynamic changes in service requirements concerning location, time, and subscriber activity.
5. The system of claim 1, wherein the dynamic strategy unit employs autoregressive statistical models to predict and manage resource allocation needs.
6. The system of claim 1, wherein the resource allocation algorithm is designed to address the NP-hard problem of resource allocation within the cloud computing environment.
7. The system of claim 1, wherein the dynamic strategy unit further incorporates quality of service (QoS) parameters into the resource allocation algorithm.
8. The system of claim 1, wherein the system includes a fault tolerance module configured to handle potential resource allocation failures and ensure continuous service availability.
9. The system of claim 1, wherein the resource provisioning module further includes energy consumption optimization algorithms to minimize the environmental impact of the cloud resource management.
10. A method for managing cloud resources in a service-oriented architecture, the method comprising:
receiving, via a request interface, resource requests from multiple subscribers;
allocating and configuring cloud resources in response to the requests using a resource provisioning module;
managing a waiting queue and resource allocation using a dynamic strategy unit;
analyzing heuristic records of past requests and resource allocations to inform resource management decisions; and
implementing, by the dynamic strategy unit, an object-oriented design pattern-based resource allocation algorithm that dynamically reconfigures resources in real-time according to subscriber requirements and cloud environment conditions; wherein the resource allocation algorithm optimizes efficiency, reduces allocation complexity, and improves overall cloud service quality.