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Joint Monitorless Load-Balancing and Autoscaling for Zero-Wait-Time in Data Centers

机译:在数据中心中的零等待时间联合无系统负载平衡和自动播放

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Cloud architectures achieve scaling through two main functions: (i) load-balancers, which dispatch queries among replicated virtualized application instances, and (ii) autoscalers, which automatically adjust the number of replicated instances to accommodate variations in load patterns. These functions are often provided through centralized load monitoring, incurring operational complexity. This article introduces a unified and centralized-monitoring-free architecture achieving both autoscaling and load-balancing, reducing operational overhead while increasing response time performance. Application instances are virtually ordered in a chain, and new queries are forwarded along this chain until an instance, based on its local load, accepts the query. Autoscaling is triggered by the last application instance, which inspects its average load and infers if its chain is under- or over-provisioned. An analytical model of the system is derived, and proves that the proposed technique can achieve asymptotic zero-wait time with high (and controlable) probability. This result is confirmed by extensive simulations, which highlight close-to-ideal performance in terms of both response time and resource costs.
机译:云体系结构通过两个主要功能实现缩放:(i)负载平衡器,在复制虚拟化应用程序实例中调度查询,(ii)自动调整,它自动调整复制实例的数量以适应负载模式的变化。这些功能通常通过集中负载监测,导致操作复杂性提供。本文介绍了统一和集中的监控无线架构,实现了自动播放和负载平衡,在增加响应时间性能时减少操作开销。应用程序实例实际上在链中排序,并且新查询沿着该链转发,直到实例基于其本地负载,接受查询。最后一个应用程序实例触发了自动播放,如果其链或过度配置,则检查其平均负载和Infers。衍生系统的分析模型,并证明了所提出的技术可以实现高(和控制)概率的渐近零等待时间。该结果是通过广泛的模拟确认,这在响应时间和资源成本方面突出了近乎理想的性能。

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