We study the problem of dynamically migrating a service in the cloud to satisfy an online sequence of mobile batch-request demands in a cost-effective way. The service may have single or multiple replicas, each running on a virtual machine. As the origin of mobile accesses frequently changes over time, this problem is particularly important for time-bounded services to achieve enhanced Quality of Service and cost effectiveness. Moving the service closer to the client locations not only reduces the service access latency but also minimizes the network costs for service providers. However, these benefits are not free. The migration comes at a cost of bulk-data transfer and service disruption, and hence, increasing the overall service costs. To gain the benefits of service migration while minimizing the caused monetary costs, we propose an efficient search-based algorithm Dmig to migrate a single server, and then extend it as a scalable algorithm, called mDmig , to the multi-server situation, a more general case in the cloud. Both algorithms are fully distributed, symmetric, and characterized by the effective use of historical access information to conduct virtual migration so that the limitations of local search in the cost reduction can be overcome. To evaluate the algorithms, we compared them with some existing algorithms and an off-line algorithm. Our simulation results showed that the proposed algorithms exhibit better performance in service migration by adapting to the changes of mobile access patterns in a cost-effective way.

中文翻译：

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关于移动访问的云服务迁移

我们研究了在云中动态迁移服务以满足移动批量请求的在线序列以经济高效的方式的问题。该服务可能有一个或多个副本，每个副本都在虚拟机上运行。由于移动接入的来源随着时间的推移经常发生变化，这个问题对于有时限的服务以实现更高的服务质量和成本效益尤为重要。将服务移近客户端位置不仅可以减少服务访问延迟，还可以最大限度地降低服务提供商的网络成本。但是，这些好处不是免费的。迁移是以批量数据传输和服务中断为代价的，因此会增加整体服务成本。为了获得服务迁移的好处，同时最大限度地减少造成的货币成本，米格迁移单个服务器，然后将其扩展为可扩展的算法，称为米格，对于多服务器的情况，云中比较一般的情况。两种算法都是完全分布式的、对称的，其特点是有效利用历史访问信息进行虚拟迁移，从而克服了局部搜索在降低成本方面的局限性。为了评估这些算法，我们将它们与一些现有算法和离线算法进行了比较。我们的仿真结果表明，所提出的算法通过以具有成本效益的方式适应移动接入模式的变化，在服务迁移方面表现出更好的性能。