We consider an auto-scaling technique in a cloud system where virtual machines hosted on a physical node are turned on and off depending on the queue's occupation (or thresholds), in order to minimise a global cost integrating both energy consumption and performance. We propose several efficient optimisation methods to find threshold values minimising this global cost: local search heuristics coupled with aggregation of Markov chain and with queues approximation techniques to reduce the execution time and improve the accuracy. The second approach tackles the problem with a Markov Decision Process (MDP) for which we proceed to a theoretical study and provide theoretical comparison with the first approach. We also develop structured MDP algorithms integrating hysteresis properties. We show that MDP algorithms (value iteration, policy iteration) and especially structured MDP algorithms outperform the devised heuristics, in terms of time execution and accuracy. Finally, we propose a cost model for a real scenario of a cloud system to apply our optimisation algorithms and show their relevance.
翻译:我们考虑在云层系统中采用自动扩缩技术,在云层系统中,以物理节点为主的虚拟机器视队列占用情况(或阈值)而开关和关闭,以便最大限度地降低全球成本,同时结合能源消耗和性能。我们提出了几种有效的优化方法,以找到最小值,从而尽可能降低这一全球成本:本地搜索超常,加上Markov链和队列近距离技术,以减少执行时间和提高准确性。第二种方法用马尔科夫决策程序(MDP)来解决问题,为此我们着手进行理论研究,并提供与第一种方法的理论比较。我们还开发了结构化的MDP算法,将歇斯底里特性结合起来。我们展示了MDP算法(价值迭代、政策迭代)和特别结构化的MDP算法在时间执行和准确性方面优于所设计的超常值。最后,我们提出了一个云系统真实情景的成本模型,以应用我们的优化算法并展示其相关性。