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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Online Resource Scheduling Under Concave Pricing for Cloud Computing
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Online Resource Scheduling Under Concave Pricing for Cloud Computing

机译：凹定价下的云计算在线资源调度

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摘要

With the booming cloud computing industry, computational resources are readily and elastically available to the customers. In order to attract customers with various demands, most Infrastructure-as-a-service (IaaS) cloud service providers offer several pricing strategies such as pay as you go, pay less per unit when you use more (so called volume discount), and pay even less when you reserve. The diverse pricing schemes among different IaaS service providers or even in the same provider form a complex economic landscape that nurtures the market of cloud brokers. By strategically scheduling multiple customers’ resource requests, a cloud broker can fully take advantage of the discounts offered by cloud service providers. In this paper, we focus on how a broker can help a group of customers to fully utilize the volume discount pricing strategy offered by cloud service providers through cost-efficient online resource scheduling. We present a randomized online stack-centric scheduling algorithm (ROSA) and theoretically prove the lower bound of its competitive ratio. Three special cases of the offline concave cost scheduling problem and the corresponding optimal algorithms are introduced. Our simulation shows that ROSA achieves a competitive ratio close to the theoretical lower bound under the special cases. Trace-driven simulation using Google cluster data demonstrates that ROSA is superior to the conventional online scheduling algorithms in terms of cost saving.
机译：随着云计算行业的蓬勃发展，客户可以轻松，灵活地使用计算资源。为了吸引具有各种需求的客户，大多数基础设施即服务（IaaS）云服务提供商提供了几种定价策略，例如随用随付，使用更多时每单位支付更少（所谓的批量折扣）以及预订时支付更少。在不同的IaaS服务提供商之间，甚至在同一提供商中，不同的定价方案形成了复杂的经济格局，孕育了云经纪人的市场。通过有计划地安排多个客户的资源请求，云代理可以充分利用云服务提供商提供的折扣。在本文中，我们重点介绍经纪人如何通过具有成本效益的在线资源调度来帮助一群客户充分利用云服务提供商提供的批量折扣定价策略。我们提出了一种随机的以堆栈为中心的在线调度算法（ROSA），并从理论上证明了其竞争比率的下限。介绍了离线凹面成本调度问题的三种特殊情况以及相应的最优算法。我们的仿真表明，在特殊情况下，ROSA的竞争率接近理论下限。使用Google集群数据进行跟踪驱动的模拟表明，ROSA在节省成本方面优于传统的在线调度算法。

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