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Self regulatory graph based model for managing VM migration in cloud data centers

机译:基于自我调节图的云数据中心管理VM迁移的模型

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Cloud Computing involves the concepts of parallel processing and distributed computing in order to provide the shared resources by means of Virtual Machines(VMs) hosted by physical servers. Efficient management of VMs directly influences resource utilization and QoS delivered by the system. As the cloud setting is dynamic in nature, the number of VMs distributed among the physical servers tends to become uneven over a period of time. Under this circumstance, VMs must be migrated from overloaded server to underloaded server to balance the load. In this paper, we present a random graph model of the network of servers in a data center. By initiating random walks and using the heuristics Maximum Correlation Coefficient and Migration Opportunity, we select the migrating set of VMs as well as the target server respectively. Simulation results show that the model always finds a target server in minimum time. Also the graph maintains uniform average degree which shows that the network of physical servers remains load balanced even when the load and the migration opportunity vary with time.
机译:云计算涉及并行处理和分布式计算的概念,以便通过物理服务器托管的虚拟机(VM)提供共享资源。 VM的高效管理直接影响系统提供的资源利用率和QoS。由于云设置本质上是动态的,因此在物理服务器中分布的VM的数量往往在一段时间内变得不均匀。在这种情况下,必须将VM从超载的服务器迁移到欠载服务器以平衡负载。在本文中,我们在数据中心中介绍了服务器网络网络的随机图模型。通过启动随机散步并使用启发式最大相关系数和迁移机会,我们分别选择迁移的VM和目标服务器。仿真结果表明,该模型始终在最短时间内找到目标服务器。此外,图表保持了统一的平均程度,表明,即使在负载和迁移机会随时间而变化时,物理服务器网络也保持均衡。

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