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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Burstiness-Aware Resource Reservation for Server Consolidation in Computing Clouds
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Burstiness-Aware Resource Reservation for Server Consolidation in Computing Clouds

机译：用于计算云中服务器整合的突发性资源预留

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摘要

In computing clouds, burstiness of a virtual machine (VM) workload widely exists in real applications, where spikes usually occur aperiodically with low frequency and short duration. This could be effectively handled through dynamically scaling up/down in a virtualization-based computing cloud; however, to minimize energy consumption, VMs are often highly consolidated with the minimum number of physical machines (PMs) used. In this case, to meet the dynamic runtime resource demands of VMs in a PM, some VMs have to be migrated to some other PMs, which may cause potential performance degradation. In this paper, we investigate the burstiness-aware server consolidation problem from the perspective of resource reservation, i.e., reserving a certain amount of extra resources on each PM to avoid live migrations, and propose a novel server consolidation algorithm, . We first model the resource requirement pattern of each VM as a two-state Markov chain to capture burstiness, then we design a resource reservation strategy for each PM based on the stationary distribution of a Markov chain. Finally, we present , a complete server consolidation algorithm with a reasonable time complexity. We also show how to cope with heterogenous spikes and provide remarks on several extensions. Simulation and testbed results show that, improves the consolidation ratio by up to 45 percent with large spike size and around 30 perce- t with normal spike size compared with the strategy that provisions for peak workload, and achieves a better balance between performance and energy consumption in comparison with other commonly-used consolidation algorithms.
机译：在计算云中，虚拟机（VM）工作负载的突发性广泛存在于实际应用中，在这些应用中，峰值通常不定期地以低频率和短持续时间出现。这可以通过在基于虚拟化的计算云中动态放大/缩小来有效地解决。但是，为了最大程度地减少能耗，虚拟机通常与使用的最少物理机（PM）高度整合。在这种情况下，为了满足PM中VM的动态运行时资源需求，必须将某些VM迁移到其他PM，这可能会导致性能下降。在本文中，我们从资源预留的角度研究了可识别突发性的服务器整合问题，即在每个PM上保留一定数量的额外资源以避免实时迁移，并提出了一种新颖的服务器整合算法。我们首先将每个VM的资源需求模式建模为两个状态的马尔可夫链以捕获突发性，然后基于马尔可夫链的平稳分布为每个PM设计资源保留策略。最后，我们提出了一种具有合理时间复杂度的完整服务器整合算法。我们还展示了如何应对异构尖峰，并提供了几种扩展的说明。仿真和试验台结果表明，与提供峰值工作负载的策略相比，在峰值大的情况下，合并率提高了45％，在峰值大的情况下，合并率提高了约30％，并在性能和能耗之间实现了更好的平衡。与其他常用的合并算法相比。

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