DESC:FORM – 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(SEE SECTION 10, RULE 13)

METHOD FOR MONITORING AND OPTIMIZATION SYSTEM USING DATA ANALYTICS FOR CLOUD COMPUTING VIRTUAL MACHINES

BHARAT ELECTRONICS LIMITED
WITH ADDRESS AT OUTER RING ROAD, NAGAVARA,
BANGALORE,
KARNATAKA, 560045

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED

TECHNICAL FIELD
[0001] The present invention relates generally to monitoring. The invention, more particularly, relates to a system and method for monitoring and optimization for cloud computing virtual machines.
BACKGROUND
[0002] Cloud Computing is “Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and re-leased with minimal management effort or service provider interaction”. Virtualization is the key technique for cloud computing. Virtualization is a technical innovation designed to increase the level of system abstraction and enable IT users to harness ever-increasing levels of computer performance.
[0003] Cloud computing offers the following service models:
[0004] Software as a Service (SaaS) is a kind of application that is available as a service to users; it delivers software as a service over the Internet, eliminating the need to install and run the application on local computers in order to simplify the maintenance and support. The benefits of SaaS are lower cost, user familiarity with WWW, and web availability and reliability.
[0005] Platform as a Service (PaaS) model enables the deployment of applications without the cost and complexity of buying and managing the underlying hardware and software layers. A customer can deploy an application directly on the cloud infrastructure (without managing and controlling that infra-structure) using the programming languages and tools sup-ported by a provider. A customer has the control over its applications and hosting environment’s configurations.
[0006] Infrastructure as a Service (IaaS) delivers a computer infrastructure that is a fundamental resource like processing power, storage capacity and network to customers; instead of building data centers, purchasing servers, software or network equipments, a customer buys the resources as a fully out-sourced service; a customer does not manage the underlying infrastructure but has full control over the operating systems and the applications running on it. IaaS models often provide automatic support for on demand scalability of computing and storage resources.
[0007] Cloud computing is a new way of supercomputing. Based on centre of data, it is a data-intensive supercomputing. In services optimizing the management, applications building and integration, applications continuous operation, multi-mode client and many other aspects, the cloud computing has its own unique technologies.
[0008] Cloud platform services optimize the management technology. Service optimizing the management is a key issue to improve the service quality of cloud platform and platform performance. The key technologies include the following aspects. Cloud service resource management, the research on demand management and partition isolation mechanism of physical machines, virtual machines and virtual clusters. Cloud task management, the research on classification, university scheduling, load balancing, power management and fault tolerance of cloud computing tasks. Cloud data management, the research on modelling, organization, storage, manipulation, retrieval, and data backup of massive structured, unstructured and multimedia data. The proposed method is a part of applied behaviour analysis and system evaluation, the work pertains to cloud computing monitoring tasks, evaluation metrics, benchmarks set, alert system and resource management.
[0009] There are many conventional solutions that exist, for example, one of a conventional solution is proposed in US9558246B1, titled “System and method for time-based clustering of data-access instances” discloses a method includes accessing a data-access history for a time period, the data-access history comprising a plurality of data-access instances. The method further includes initially associating each data-access instance with a time-based data-access cluster of a plurality of time-based data-access clusters based, at least in part, on a time of the data-access instance. In addition, the method includes iteratively refining a time distribution of the plurality of data-access instances across the plurality of time-based data-access clusters. Further, the method includes facilitating a time-density analysis of the plurality of data-access instances using the iteratively refined plurality of time-based data-access clusters.
[0010] Another conventional solution is proposed in US9444716B2titled “Secure cloud management agent” discloses a method for providing a secure management agent for high-availability continuity for cloud systems includes receiving operating parameters and threshold settings for a plurality of computing clouds. Secure relationships are established with the plurality of computing clouds based on the operating parameters. Data is mirrored across the plurality of computing clouds. Threshold data is then monitored for the plurality of computing clouds to maintain a continuity of resources for the plurality of computing clouds.
[0011] Another conventional solution is proposed in US9391855B2 titled “Systems and methods for a cloud management system which utilizes both technical and business metrics to achieve operational efficiencies. The systems and methods can be used to provide an elastic infrastructure model for an emergency notifications system which delivers near infinite scale with guaranteed near 100% uptime. In an embodiment, a mass recipient emulator can be utilized for testing of the notifications system with actual phone call or message exchange.
[0012] Another conventional solution is proposed in US20100131624A1 titled “Systems and methods for multiple cloud marketplace aggregation” discloses systems and methods for multiple cloud marketplace aggregation. An aggregation engine communicates with a set of multiple cloud marketplaces, each of which communicates with an associated set of clouds. A requesting entity, such as a user requesting the instantiation of a set of virtual machines, can transmit a resource request to the aggregation engine. The aggregation engine can fan out or distribute a replicated request to the set of multiple cloud marketplaces. Each cloud marketplace can receive the request and respond to indicate available resources that can be produced from their respect set of clouds. The aggregation engine can collect the responses of the various marketplaces,and can generate one or more selections based on selection logic such as best match, cost factors, or other criteria. In embodiments, a user can manually select the desired marketplace(s) to instantiate or update their virtual machine or other target objects.
[0013] Thus, there is a need for an invention that solves the above-defined problems and provides a system and method for monitoring and optimization using data analytics for cloud computing virtual machines.

SUMMARY OF THE INVENTION
[0014] This summary is provided to disclose a monitoring & optimization system and method for cloud computing virtual machines. This summary is neither intended to identify essential features of the present invention nor is it intended for use in determining or limiting the scope of the present invention.
[0015] For example, various embodiments herein may include one or more systems and methods for monitoring, resource management & optimization of cloud computing.
[0016] In an embodiment, the present invention discloses a monitoring and resource management system. The system consists of a controller configured to obtain a series of activity data of users from the virtual machines of a cloud computing environment. The system further consists of a data analytics server of cloud monitoring and alert server cluster system configured to learn a plurality of patterns corresponding to the series of the activity data over a period of time. The system further consists of a syslog data module, for storing the series of the activity data of the users and the learned pattern data. The system further consists of a cloud controller for providing optimized resource allocation to the user based on the stored data. The data analytics server of cloud monitoring and alert server cluster system further comprises master and slave nodes. The master node is configured to assign the series of the activity data of the users and the learned pattern data to the slave nodes to store and process the data. The cloud controller of the system is configured to perform the resource management on basis of an alert received from the master node to increase or decrease the resource capacity as per the instructions given by the master node.
[0017] In an exemplary implementation, the present invention discloses a method of monitoring and optimization for cloud computing virtual machines, the method comprising the steps of obtaining, by a controller, a series of activity data of users from the virtual machines of a cloud computing environment. In the second step, learning, by the data analytics server of cloud monitoring and alert server cluster system, a plurality of patterns corresponding to the series of the activity data over a period of time. The method further comprises the step of storing, by a learned data syslog data module, the series of the activity data of the users and the learned pattern data. In the further method step, providing, by a cloud controller, optimized resource allocation to the user based on the stored data.
BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS
[0018] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to reference like features and modules.
[0019] Fig. 1 illustrates a flowchart depicting a process of monitoring and alert system of cloud computing for optimization of resource allocation using data analytics learned system, according to an embodiment of the present disclosure.
[0020] FIG. 2 is a block diagram depicting a process of a data analytics server of cloud monitoring and alert server cluster system., according to an embodiment of the present disclosure.
[0021] FIG. 3 is an illustrative view showing an example of cloud management components, according to an embodiment of the present disclosure.
[0022] FIG. 4 is a block diagram of an example process of interaction between virtual machine instances with monitoring and alert server cluster, according to an embodiment of the present disclosure.
[0023] Figure 5 illustrates a schematic diagram depicting a flow diagram for monitoring and optimization for cloud computing virtual machines, according to an exemplary implementation of the present invention.
[0024] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative methods embodying the principles of the present disclosure. Similarly, it will be appreciated that any flow charts, flow diagrams, and the like represent various processes that may be substantially represented in a computer-readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
DETAILED DESCRIPTION
[0025] The various embodiments of the present invention describe a system and method for monitoring and optimization using data analytics for cloud computing virtual machines.
[0026] In the following description, for purpose of explanation, specific details are outlined to provide an understanding of the present disclosure. It will be apparent, however, to one skilled in the art that the present disclosure may be practiced without these details. One skilled in the art will recognize that embodiments of the present disclosure, some of which are described below, may be incorporated into a number of systems.
[0027] However, the systems and methods are not limited to the specific embodiments described herein. Further, structures and devices shown in the figures are illustrative of exemplary embodiments of the presently disclosure and are meant to avoid obscuring of the presently disclosure.
[0028] It should be noted that the description merely illustrates the principles of the present invention. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present invention. Furthermore, all examples recited herein are principally intended expressly to be only for explanatory purposes to help the reader in understanding the principles of the invention and the concepts contributed by the inventor to furthering the art and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass equivalents thereof.
[0029] In one of the exemplary implementations, the present invention discloses a method of monitoring and optimization of the notification alert system and resource management for cloud computing virtual machine instances with self-learning and self-management of the cloud resources using data analytics such as machine learning techniques. The method of self-learning and self-management of the cloud resources is an advancement feature with respect to the prior relevant art.
[0030] In an embodiment, the present invention discloses a software application developed using the methods of the present disclosure. It runs on the hardware. The hardware contains memory, processor(s), and display. The software application will have a user interface. The user interacts with the software application using its user interface. The Cloud computing system is distributed system where one or more physical server comprises of RAM, CPU, hard disk, network interfaces of the system are distributed across one or more networks. The application runs on the physical server and provides graphical user interface for configuration threshold setting for usage physical resource such as share of CPU, RAM, and storage data disk. The method also controls the hardware resource by complementing the instructions provided by the controller of the cloud computing resources.
[0031] In another embodiment, the present invention discloses a method for monitoring and optimization using data analytics for cloud virtual instances. The self-learning and self-management cloud environment for monitoring and resource management.
[0032] In another exemplary implementation, the present invention discloses a method of monitoring and optimization of the notification alert system and resource management for cloud computing the virtual machine instances using machine learning techniques. The Method also records the user behaviour after generating the alert based on recorded threshold settings and records of the precedent user pattern actions.
[0033] In another exemplary implementation, the present invention discloses the creation of historical data of user actions and Syslog data metrics of the virtual instance, and data is learned using linear regression machine learning techniques. The availability of the master node is to achieve the high availability and fault tolerance.
[0034] In another exemplary implementation, the present invention discloses the creation of auto-scaling of the alert system cluster based on the number of node virtual instances that are being monitored.
[0035] In another exemplary implementation, the present invention discloses identifying the pattern includes mapping the activity data according to a set of usage levels associated with the cloud service. Mapping the activity data can produce normalized activity data.
[0036] In another exemplary implementation, the present invention discloses identifying the pattern includes using a request of resources increase or decrease to cloud administrator.
[0037] In another exemplary implementation, the present invention discloses a method that includes a cloud monitoring system that learns patterns of user behaviour on alert notification based on threshold setting and uses the patterns to record the user activity behaviour and store the same in a cloud monitoring and alert system. Techniques discussed herein include obtaining activity data by syslog producer module from Cloud virtual instances. The system log data describes actions performed during use of a cloud service over a period of time. A pattern corresponding to a series of actions performed over a subset of time can be identified. The pattern can be added a model associated with the cloud service. The pattern and sys log data are stored in the monitoring and alert system. The data is processed and learned in the monitoring alert server cluster. Using the learned data the precedent action takes place in plurality of cloud computing resources.
[0038] In another exemplary implementation, the present invention discloses a method of monitoring and optimization for cloud computing virtual machines, the method comprising the steps of obtaining, by a controller, a series of activity data of users from the virtual machines of a cloud computing environment. In the second step, learning, by the data analytics server of cloud monitoring and alert server cluster system, a plurality of patterns corresponding to the series of the activity data over a period of time. The method further comprises the step of storing, by a syslog data module, the series of the activity data of the users and the learned pattern data. In the further method step, providing, by a cloud controller, optimized resource allocation to the user based on the stored data.
[0039] In another exemplary implementation, the present invention discloses that the activity data represents actions used during the use of the cloud computing environment over a period of time.
[0040] In another embodiment, the present invention discloses a monitoring and resource management system. The system consists of a controller configured to obtain a series of activity data of users from the virtual machines of a cloud computing environment. The system further consists of a data analytics server of cloud monitoring and alert server cluster system configured to learn a plurality of patterns corresponding to the series of the activity data over a period of time. The system further consists of a learned data syslog data module, for storing the series of the activity data of the users and the learned pattern data. The system further consists of a cloud controller for providing optimized resource allocation to the user based on the stored data. The data analytics server of the cloud monitoring and alert server cluster system further comprises master and slave nodes. The master node is configured to assign the series of the activity data of the users and the learned pattern data to the slave nodes to store and process the data. The cloud controller of the system is configured to perform the resource management on basis of an alert received from the master node to increase or decrease the resource capacity as per the instructions given by the master node.
[0041] In another exemplary implementation, the present invention discloses monitoring and optimization of the notification alert system and resource management for virtual machine instances of cloud computing using machine learning techniques. The illustrative embodiments provide optimized resource allocation based on the learned user behaviour in response to the alert generated based on the threshold settings. The monitoring and alert server cluster system comprises master and slave nodes. The cluster system performs data analysis of virtual instance data metrics and interacts with the controller of the cloud environment system by using machine learning. The learned data is stored in slave nodes and takes the activity independently without any user interactions in precedent situations. The cluster system comprising the availability of at least two master nodes is to achieve high availability and fault tolerance. Further, it also provisioned for the creation of auto-scaling of the alert system cluster based on the number of virtual instance nodes that are being monitored. The monitoring and optimization of the notification alert system and resource management for cloud computing virtual machine instances is self-learning and self-management of the cloud resources using machine learning techniques.
[0042] FIG. 1 illustrates a flowchart depicting a process of monitoring and alert system of cloud computing for optimization of resource allocation using a data analytics learned system. In FIG. 1, a process of monitoring and alert system of cloud computing is discussed. The process of monitoring and alert system of cloud computing for optimization of resource allocation is achieved by using the learned data syslog data module (201).
[0043] FIG. 2 is a block diagram depicting a process of a data analytics server of cloud monitoring and alert server cluster system. In FIG, 2 the data analytics server of cloud monitoring and alert server cluster system (202) comprises of a master (203) and slave nodes (205-207n). Further, the cluster system (202) performs data analysis of virtual instance data metrics and interacts with the cloud controller of the cloud environment system by using machine learning.
[0044] The monitoring system provided by the methods, analysis, and computer-readable medium discussed above further includes generating the model using a machine learning program. The model is generated using a plurality of patterns determined from the activity data. The model used in the system for machine learning is the linear regression with providing the supervised data from syslog to the slave nodes (205-207n) of the data analytics server of cloud monitoring and alert server cluster system (202). The analyzed output is recorded in the cluster of the data analytics server of cloud monitoring and alert server cluster system (202) redundantly. The master (203) system sends the instruction to the cloud computing controller for resource management. The cloud controller performs the resource management on basis of the alert received from the master node (203) to increase or decrease the resource capacity as per instructions given by the master node (203) of the data analytics server of cloud monitoring and alert server cluster system (202). The initial instruction is provided by the user for the alerted notification. The system records and learns the user behavior with generated alerts. A similar subsequent alert and action are executed without any user intervention. This illustration proves that the system is self-learning and self-management of the cloud resources based on machine learning techniques.
[0045] FIG. 3 is an illustrative view showing an example of cloud management components. In FIG. 3, cloud management components with the addition of monitoring and alert server cluster system as a part of cloud computing component modules are disclosed.
[0046] Typical cloud computing consists of all the resources required to provide cloud computing services. Cloud computing comprises of servers (309), storage (308), network (304), management software (305), deployment software (306), and platform virtualization.
[0047] Hypervisor (302) is a firmware or low-level program that acts as a Virtual Machine Manager. It allows sharing the physical instance of cloud resources between several tenants. Management Software (305) helps to maintain and configure the infrastructure. Deployment software (306) helps to deploy and integrate the application on the cloud. The network (304) is the key component of cloud infrastructure. It allows connecting cloud services over the Internet. It is also possible to deliver network as a utility over the Internet, i.e., the consumer can customize the network route and protocol. The server (309) helps to compute the resource sharing and offer other services such as resource allocation and deallocation, monitoring resources, security, etc. Cloud uses distributed file system for storage (308) purposes. If one of the storage resources fails, then it can be extracted from another one which makes cloud computing more reliable. Security (307) is enabled in the mega data centre in the cloud should be securely architected. Also the control node, an entry point in mega data centre also needs to be secure.
[0048] The optimization of a cloud computing alert system and resource allocation system (303) by using the method of monitoring and alert system with help of machine learning techniques, is a one of the component module in the cloud environment which interacts controller (200) module of the cloud computing.
[0049] FIG. 4 is a block diagram of an example process of interaction between virtual machine instances with monitoring and alert server cluster. In FIG.4, the process of interaction between virtual machines instances with monitoring and alert server cluster with storage access to get details of virtual machine instance data metric through the virtual router provided by software defined network of the cloud computing environment is disclosed.
[0050] The method provides a system for cloud management and alert system that learns patterns of user behavior and uses the learned patterns for optimization of resource management. For illustration, the cloud management and alert system use machine learning techniques to learn patterns of user behavior, where the patterns represent actions regularly and/or habitually taken by users in using a cloud computing service reply for alerts generated. For illustrations, the patterns can capture actions that occur over a span of hours, days, weeks, months, or another time period. In the proposed method, the cloud management and alert system can use the learned patterns to identify user behavior after the alerting user on reaching predefined and recorded threshold settings. In various implementations, provided are methods, including computer-implemented methods, computing systems implementing a cloud monitoring and alert system for managing the resource utilization for computing virtual instances optimally, and computer-readable medium including instructions for a monitoring and alert management system that learns patterns of user behavior and uses the learned patterns to identify user activity in a monitoring system cluster (402). In various implementations, the techniques discussed herein include obtaining activity data from virtual machines (403) of the cloud computing environment by running the syslog metric data producer module and transmitting the syslog data to the monitoring system cluster (402). The activity data describes actions performed during the use of a cloud service over a period of time. The actions are performed by one or more users associated with a tenant, where the service provider system provides the tenant with a tenant account. The tenant account enables one or more users to access the cloud service. The techniques further include identifying, using the model, a set of actions in the additional activity data that do not correspond to the model. The techniques further include outputting the set of actions and an indicator that identifies the set of actions by users. The set of actions is stored in the learned data syslog data module (201) of the data analytics server of the cloud monitoring and alert server cluster system (300). The data analytics server of the cloud monitoring and alert server cluster system (300) consists of master (203) and slave nodes (205-207n). The master node (203) in the cloud monitoring and alert system assigns the data metrics to the slave nodes. The slave nodes process and store the data, the slave nodes (205-207n) also process the learning method of metrics and user behavior. The learned data is stored in slave nodes and takes the activity independently without user interactions in precedent situations. The master node (203) in the server (405) of the cloud monitoring and alert server cluster system (300) comprises two in number to achieve high availability and fault tolerance. The communication between controller (200), monitoring alert server system (300), the user from the public (400), and node instances (403) carried through the virtual router (401) provided by software-defined network of the cloud computing environment.
[0051] Figure 5 illustrates a schematic diagram depicting a flow diagram for monitoring and optimization for cloud computing virtual machines, according to an exemplary implementation of the present invention.
[0052] Referring now to Fig. 5 which illustrates a flowchart of monitoring and optimization for cloud computing virtual machines, according to an exemplary implementation of the present invention. The flow chart of Fig. 5 is explained below with reference to Fig. 2 as described above.
At step 501, obtaining, by a controller (200), a series of activity data of users from the virtual machines of a cloud computing environment.
At step 502, learning, by the data analytics server of cloud monitoring and alert server cluster system (300), a plurality of patterns corresponding to the series of the activity data over a period of time.
At step 503, storing, by a learned data syslog data module (201), the series of the activity data of the users and the learned pattern data.
At step 504, providing, by a cloud controller, optimized resource allocation to the user based on the stored data.
[0053] The foregoing description of the invention has been set merely to illustrate the invention and is not intended to be limiting. Since modifications of the disclosed embodiments incorporating the substance of the invention may occur to a person skilled in the art, the invention should be construed to include everything within the scope of the invention.
,CLAIMS:We claim:

1. A method of monitoring and optimization for cloud computing virtual machines , the method comprising:
obtaining, by a controller (200), a series of activity data of users from the virtual machines of a cloud computing environment;
learning, by the data analytics server of cloud monitoring and alert server cluster system (300), a plurality of patterns corresponding to the series of the activity data over a period of time;
storing, by a learned data syslog data module (201), the series of the activity data of the users and the learned pattern data; and
providing, by a cloud controller, optimized resource allocation to the user based on the stored data.

2. The method as claimed in claim 1, wherein the activity data represents actions used during use of the cloud computing environment over a period of time.

3. The method as claimed in claim 1, wherein the data analytics server of cloud monitoring and alert server cluster system (300) further comprising a master (203) and slave nodes (205-207n).

4. The method as claimed in 2, wherein the master node (203) assigns the series of the activity data of the users and the learned pattern data to the slave nodes (205-207n) to store and process.

5. The method as claimed in claim 1, wherein performing, by the cloud controller, of the resource management on basis of an alert received from the master node (203) to increase or decrease the resource capacity as per the instructions given by the master node (203).

6. A monitoring and optimization system for cloud computing virtual machines, the system comprising:
a controller (200) configured to obtain a series of activity data of users from the virtual machines of a cloud computing environment;
a data analytics server of cloud monitoring and alert server cluster system (202) configured to learn a plurality of patterns corresponding to the series of the activity data over a period of time;
a learned data syslog data module (201), for storing the series of the activity data of the users and the learned pattern data; and
a cloud controller for providing optimized resource allocation to the user based on the stored data.

7. The system as claimed in claim 6, wherein the data analytics server of cloud monitoring and alert server cluster system (202) further comprising a master (203) and slave nodes (205-207n).

8. The system as claimed in claim 7, wherein the master node (203) is configured to assign the series of the activity data of the users and the learned pattern data to the slave nodes (205-207n) to store and process the data.

9. The system as claimed in claim 6, wherein the cloud controller is configured to perform the resource management on basis of an alert received from the master node (203) to increase or decrease the resource capacity as per the instructions given by the master node (203).
Dated this 30th day of March, 2021

For BHARAT ELECTRONICS LIMITED
By their Agent)

D. MANOJ KUMAR (IN/PA-2110)

KRISHNA & SAURASTRI ASSOCIATES LLP