In cloud computing, it is crucial to maintain service continuity, while power outage is one of the most common and serious threats. To improve the resilience of cloud against power outage, a service provider usually deploys emergency energy supply (e.g., UPSs and generators) in a data center. When a power outage at a data center happens, the cloud service provider needs to make the operation decision on which subset of VMs to keep running and which servers to host such VMs to minimize its loss (or maximize its profit) using the emergency energy supply while the selected VMs are running in the affected data center until they are finished, migrated to other data centers, or normal power supply of the affected data center has been restored. No prior research has theoretically studied such a cloud service continuity problem under power outage. In this paper, we tackle this challenge and investigate the cloud service continuity problem. Specifically, we consider that a profit is associated with maintaining the continuity of a service, denoted as service continuity profit. Based on that we first formulate an optimization problem that aims to maximize the total profit subject to energy constrains. After showing the hardness of the problem, we focus on the design of approximation algorithms for solving the problem, where we consider two practical cases. In the first one with sufficient number of servers for re-provisioning, we develop a constant approximation algorithm of which the worst-case performance approaches the optimal solution within a constant factor ($\approx$≈ 4.5-6.4). In the second one, we consider the general case with limited number of servers, and we develop an approximation algorithm with an approximation ratio of around 5.7-8. By combining these two algorithms together, we can achieve both good worst-case performance and average performance. Simulation results demonstrate the efficiency in terms of maximizing the service continuity profit of the proposed algorithms.

中文翻译：

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在停电的情况下在云端提供服务连续性

在云计算中，保持服务连续性至关重要，而停电是最常见和最严重的威胁之一。为了提高云对停电的恢复能力，服务提供商通常在数据中心部署应急能源供应（例如，UPS 和发电机）。当数据中心发生断电时，云服务提供商需要做出运营决策，决定哪些虚拟机子集继续运行以及哪些服务器托管这些虚拟机，以使用紧急能源供应将其损失降至最低（或利润最大化）选定的虚拟机在受影响的数据中心运行，直到它们完成、迁移到其他数据中心或受影响的数据中心的正常供电已恢复。之前没有研究从理论上研究过这种停电情况下的云服务连续性问题。在本文中，我们解决了这一挑战并研究了云服务连续性问题。具体来说，我们认为利润与维持服务的连续性有关，称为服务连续性利润。在此基础上，我们首先制定了一个优化问题，旨在最大化受能源约束的总利润。在展示了问题的难度之后，我们将重点放在解决问题的近似算法的设计上，我们考虑了两个实际案例。在第一个具有足够数量的用于重新配置的服务器中，我们开发了一个常数近似算法，该算法的最坏情况性能在常数因子（$\approx$≈4.5-6.4）内接近最佳解决方案。在第二个中，我们考虑服务器数量有限的一般情况，我们开发了一个近似算法，近似比约为 5.7-8。通过将这两种算法结合在一起，我们可以实现良好的最坏情况性能和平均性能。仿真结果证明了所提出算法在最大化服务连续性利润方面的效率。