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Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems

机译:云系统的双重时间尺度分布式容量分配和负载重定向算法

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摘要

Resource management remains one of the main issues of cloud computing providers because system resources have to be continuously allocated to handle workload fluctuations while guaranteeing Service Level Agreements (SLA) to the end users. In this paper, we propose novel capacity allocation algorithms able to coordinate multiple distributed resource controllers operating in geographically distributed cloud sites. Capacity allocation solutions are integrated with a load redirection mechanism which, when necessary, distributes incoming requests among different sites. The overall goal is to minimize the costs of allocated resources in terms of virtual machines, while guaranteeing SLA constraints expressed as a threshold on the average response time. We propose a distributed solution which integrates workload prediction and distributed non-linear optimization techniques. Experiments show how the proposed solutions improve other heuristics proposed in literature without penalizing SLAs, and our results are close to the global optimum which can be obtained by an oracle with a perfect knowledge about the future offered load.
机译:资源管理仍然是云计算提供商的主要问题之一,因为必须连续分配系统资源来处理工作负载波动,同时还要保证最终用户的服务水平协议(SLA)。在本文中,我们提出了新颖的容量分配算法,该算法能够协调在地理分布的云站点中运行的多个分布式资源控制器。容量分配解决方案与负载重定向机制集成在一起,该机制在必要时可以在不同站点之间分配传入的请求。总体目标是最大程度地减少虚拟机分配资源的成本,同时保证SLA约束表示为平均响应时间的阈值。我们提出了一种集成了工作量预测和分布式非线性优化技术的分布式解决方案。实验表明,所提出的解决方案如何在不损害SLA的情况下改进文献中提出的其他启发式方法,并且我们的结果接近于可以由对未来提供的负载有全面了解的预言家获得的全局最优值。

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