There is a growing trend for service providers to migrate their services from local clusters to the cloud data center. When there is no single service can satisfy the functionality requirement of the end user, existing services are combined together to fulfill the requirements. The data communication between component service hosting servers imposes a heavy burden on the data center network. In this article, we seek to reduce the data center network resource consumption by designing a novel service composition aware virtual machine migration approach. First, we formulate the problem as a multi-object integer non-linear(INLP) programming problem. The problem, which can be reduced into a well-known multi-object quadratic assignment problem, is proved to be NP-hard. Second, we simplify the multiple-objects INLP formulation into an equivalent, but much simplified single object ILP formulation. Then, we prove that the simplified formulation can also lead to the optimal solutions. Finally, optimization problem solvers, such as LPSolver, are employed to solve the problem. Experimental results in a large scale cloud data center demonstrate that our method significantly reduce the network resource consumption than other approaches.

中文翻译：

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在云中实现服务组合感知虚拟机迁移方法

服务提供商将其服务从本地集群迁移到云数据中心的趋势越来越大。当没有单一服务可以满足最终用户的功能需求时，将现有服务组合在一起以满足需求。组件服务托管服务器之间的数据通信给数据中心网络带来了沉重的负担。在本文中，我们试图通过设计一种新颖的服务组合感知虚拟机迁移方法来减少数据中心网络资源消耗。首先，我们将问题表述为多对象整数非线性 (INLP) 规划问题。该问题可以简化为众所周知的多对象二次分配问题，被证明是 NP-hard 问题。其次，我们将多对象 INLP 公式简化为等效的，但大大简化了单对象 ILP 公式。然后，我们证明简化的公式也可以导致最优解。最后，使用优化问题求解器（例如 LPSolver）来解决问题。在大规模云数据中心的实验结果表明，我们的方法比其他方法显着减少了网络资源消耗。