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

机译:基于自我调节图的模型,用于管理云数据中心中的VM迁移

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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)来提供共享资源。 VM的有效管理直接影响系统所利用的资源利用率和QoS。由于云设置本质上是动态的,因此在一段时间内分布在物理服务器之间的VM数量趋于变得不均衡。在这种情况下,必须将VM从过载服务器迁移到欠载服务器,以平衡负载。在本文中,我们提出了数据中心服务器网络的随机图模型。通过启动随机游走并使用启发式最大相关系数和迁移机会,我们分别选择虚拟机的迁移集和目标服务器。仿真结果表明,该模型总是在最短的时间内找到目标服务器。该图还保持均匀的平均程度,这表明即使负载和迁移机会随时间变化,物理服务器网络仍保持负载平衡。

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