Patent Application
20160098287
The present application claims priority from Indian Application Number 4528/CHE/2013, filed on 7 Oct. 2013, the disclosure of which is hereby incorporated by reference herein.
The embodiments herein relate to data centre infrastructure management and, more particularly, to analyze and deploy interrelated objects in a virtual data centre at virtual deployment level.
In current scenario, ‘virtualization’ has become an essential data center technology, allowing the IT administrators to consolidate server infrastructure and reduce costs while enhancing service levels. Further, virtualization provides more efficiency and enhanced capabilities which are not possible when constrained within a physical world. Furthermore, ‘data centre virtualization’ provides other key benefits such as less heat buildup, faster redeploy, easier backups, better testing, no vendor lock in, easier migration to cloud and so on. Hence, many companies now take advantage of virtualization solutions to consolidate several specialized physical servers and workstations into fewer servers running virtual machines. The ‘virtual deployment’ in a virtual data centre can consists of different configuration data, settings and elements like multiple virtual machines, cloud management products, virtual appliances and multiple virtual applications which may contain multiple virtual machines that form a multi-tier application, network and security configurations and so on. Thus, understanding the performance of a virtual infrastructure at ‘virtual deployment level’ is a very important task which is quite challenging. Hence the system administrators or technical specialists who are responsible for maintaining, managing, protecting and configuring computer systems and their resources are often struggle to understand and monitor the virtual infrastructure, and also struggle to quickly diagnose and resolve problems.
Further, when there is any defect in infrastructure of the present virtual data centre, the redeployment of whole applications at deployment level to another suitable data centre is necessary which should be done quickly without affecting the performance of the application. Following are some reason for redeployment:
Existing systems used for virtualization requires frequent user intervention at various stages of the process. This consumes more time and may affect efficiency of the system.
In view of the foregoing, an embodiment herein provides a method for redeploying interrelated objects in a virtual data centre at a virtual deployment level. Initially, elements of a source virtual deployment in a source virtual data centre are identified. Further, an analysis report is created by analyzing the identified elements. Based on the analysis report and identified elements, a policy data and a history data are updated. Further, a redeployment requirement is identified and a redeployment request is constructed. Further, a suitable target cloud is identified and the source virtual deployment to the identified target cloud.
Embodiments further disclose a system for redeploying interrelated objects in a virtual data centre at a virtual deployment level. The system configured for identifying elements of a source virtual deployment in a source virtual data centre using a virtual deployment analyzer. Further, an analysis report is created by analyzing the identified elements using the virtual deployment analyzer. Further the system updates a policy data and a history data based on the analysis report and identified elements and identifies a redeployment requirement using the virtual deployment analyzer. After identifying the redeployment request, the system constructs a redeployment request and identifies a target cloud using the virtual deployment analyzer. Further, the system redeploys the source virtual deployment to the identified target cloud using the virtual deployment analyzer.
These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings.
The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
In the below description terms “target cloud” and “target virtual data centre” are used interchangeably.
The embodiments herein disclose a system and method for intelligent redeployment of source virtual deployment by monitoring and analyzing interrelated objects in a virtual data centre at virtual deployment level. Referring now to the drawings, and more particularly to
List below shows some of the configurations/properties which will be analyzed for the virtual deployment.
For example, the isolated virtual environment created by a customer in a multi-tenant cloud platform can be considered as a ‘virtual deployment’. Typically, such virtual deployment environment may contain:
Further, the virtual deployment analyzer 102 present in the Intelligent analytics based virtual deployment system identifies and analyzes source virtual deployment 101 hosted on the virtual data center based on various parameters as mentioned in TABLE-1, virtual deployment performance data, past history data, future requirement and policy based data. In an embodiment, future requirement data and policy data are pre-configured to the database present in the memory module 202. Based on the analysis result, the virtual deployment analyzer 102 suggests the redeployment of source virtual deployment 101 to best suitable target virtual deployment 104 through deployment server 103 present in the system.
The hypervisor monitoring module 301 of cloud monitoring module 201 monitors the hypervisor level performances and its configurations such as CPU parameter, Memory parameters, Network bandwidth parameters and so on, present in the source virtual deployment 101. Similarly, the virtual machine monitors module 302 monitors the virtual machine level performance and its properties such as but not limited to VM SCSI data, VM network dataVM memory performance, and VM vCPU stats. The application monitoring module 303 present in the cloud monitoring module 201 monitors the virtual application level monitoring and their configurations. Further, the management monitoring module 304 monitors the cloud management level configuration and its properties. Finally, the monitoring engine 305 consolidates the monitored data and manages it with respect to source virtual deployment 101.
Further, the policy database 403 have may have parameters and threshold values for the target virtual deployment 104 needs to meet. The parameters may also include but not limited to Input Output performance, threshold or feature needs and so on. The monitoring data database 404 maintains the data which is collected by cloud monitoring module 201. In an embodiment, the future requirements and policy data can be pre-configured with the database as per the user's requirements. The cloud feature mapping table 405 further comprises information on a list of clouds available in the network and corresponding features. The information related to various clouds and corresponding features may be used at a later stage so as to identify a suitable target cloud so as to redeploy a source virtual deployment.
Another use case scenario is in a multi tenanted cloud deployment environment, each tenant is expected to have an isolated virtual deployment. For example consider the case of delivering a complete test management system as a service. In this case each virtual deployment may have:
The web applications of the same tenant should have accessibility to each other's but should be isolated from the other tenants. The web application virtual machines may need to scale up or down based on the user traffic and should be accessed through a load balancer. The DB volumes may need to scale based on the incoming and outgoing data size.
In this case the various levels of monitoring will help to track the performance of the virtual deployment on hypervisor level, VM level, application level and management level and take redeployment decision if required. For example continues spike in incoming traffic can trigger a scaling up of the applications based on the configured scalability policy but cannot handle in the current cloud due the infrastructure limitation. In this scenario based on the past history, configured SLA, it can conclude on any of the re-deployment decision as: (1) scale up the web application alone to a cloud which has infrastructure available and establish the connectivity (2) re-deploy whole virtual deployment of the tenant to an appropriate target cloud and scale up (3) re-deploy another less priority virtual deployment to a target cloud and make room for the scale up.
Later, the data collector module 501 of analyzer engine 203 collects the monitored data for further processing. Further, the data analyzer module 502 of analyzer engine 203 performs the analysis (704) of retrieved monitored data. This analysis can be done based on the pre-configured parameters at various levels like virtual deployment performance data, past history data, future requirements data and policy data present at history database 401, future requirements database 402 and policy database 403 respectively of the memory module 202. Values of the parameters stored in the memory module 202 may be dynamically changed.
After analyzing the monitored data with the pre-configured parameters, data analyzer module 502 creates (706) an analysis report. Based on the analysis report and monitored data, the policy data and history data is updated through policy updater module 503 and history data updater module 504 which enhances the future analytics. Further, the decision module 505 takes decision on source virtual deployment 101 redeployment by considering analysis report, future requirements data, configured policy and history data which are present in the memory module 202. In case of redeploying new applications, as the past history data is not available, the decision is taken by considering policy data, future requirements and monitored data. If redeployment is necessary, the decision module 505 invokes the deployment initiator module 204 for initiating (708) the deployment process. Further, the deployment initiator module 204 constructs a ‘redeployment request’ for virtual deployment by using virtual deployment identifier module 601 and target virtual redeployment requirement identifier module 602. In an embodiment, the redeployment request comprises of source virtual deployment details, expected performance requirement and future expansion requirements.
Further, the constructed redeployment request is passed to target cloud identifier module 205. The target cloud identifier module 205 based on the received redeployment request, identifies the best suitable target virtual deployment cloud 104 by using the cloud feature mapping table 405 present in the memory module 202. In an embodiment, a cloud feature mapping table 405 is prepared (710) and pre-configured in memory module 202 which maintains a list of target cloud vendor properties that are compared to the future requirements and policies of the applications which are to be redeployed. Further, a suitable target virtual deployment cloud is identified (712) from list of available cloud vendors by comparing the required parameters with the existing cloud vendor parameters. Now, the redeployment request is passed to deployment server 103 which is present in intelligent analytics based virtual deployment system. The deployment server 103 further locates the identified source virtual deployment 104 and converts the identified source virtual deployment 104 into a cloud independent standard entity. Further, a target virtual deployment which is specific to the target cloud is prepared by the deployment server 103. Finally, the converted target cloud specific virtual deployment is deployed (714) to identified target cloud 104 and verifies its performance and features.
In an embodiment, the clouds in which the source and target data centers reside can be same or they can be of different clouds. For example, if there is problem with the ‘networking gates’ present in the source virtual data centre, then there is no need of changing the source cloud as the problem is resolved by changing the virtual network present in the source virtual data centre. Thus, depending on the type of problem encountered in the source virtual deployment 101, redeployment can be done.
The various actions in method 700 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in
The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the network elements. The network elements shown in
The embodiment disclosed herein specifies a system for data centre infrastructure management. The mechanism allows analyzing and deploying interrelated objects in a virtual data centre to a target virtual data center at virtual deployment level by providing a system thereof.
The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the claims as described herein.
