PDF(Pubmed)
Abstract：
Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations.
摘要：
云数据中心由于其巨大的能源消耗而对环境可持续性提出了挑战。此外,能源管理解决方案导致的性能下降，例如虚拟机(VM)整合、影响云服务提供商和用户之间的服务级别协议(SLA)。因此，为了在有效的能源消耗和避免违反SLA之间取得平衡，我们提出了一种新的虚拟机整合算法。传统算法在选择虚拟机不当时会导致不必要的迁移。因此，我们提出的E2SVM算法通过从过载的服务器中选择具有高负载波动和最小资源使用的VM来解决这个问题。然后将这些选定的VM放置在正常加载的服务器上，考虑到它们的稳定性指数。此外，我们的方法通过应用所有或不应用VM迁移来防止服务器利用率不足。仿真结果表明，与最小迁移时间VM选择策略相比，最大能耗降低了12.9％。此外,当使用中等绝对偏差作为过载检测策略时，SLA违规减少了47%。因此,这种方法为现实世界的数据中心带来了希望，因为它最大限度地减少了能源浪费，并保持了较低的SLA违规。
