Greening Geo-distributed Data Centers by Joint Optimization of Request Routing and Virtual Machine Scheduling

Greening Geo-distributed Data Centers by Joint Optimization of Request Routing and Virtual Machine Scheduling
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通过请求路由和虚拟机调度的联合优化来绿化地理分布式数据中心

DOI:
10.1109/ucc.2014.8
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发表时间:
2014-12
期刊:
2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing
影响因子:
--
通讯作者:
Fu Chen
Fu Chen
中科院分区:
其他
文献类型:
--
作者:
Xudong Xiang;Chuang Lin;Fu Chen

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本文通过三种控制操作(1)前端代理上的数据中心间请求路由,(2)分层核心交换机和边缘交换机上的数据中心内请求路由,以及(3)异构物理服务器上的虚拟机(VM)调度,提出了地理分布式数据中心(dc)绿化的统一优化框架。我们首先将请求路由和VM调度问题作为一个随机方案来制定,该方案旨在最小化云服务提供商(csp)的时间平均电力成本、碳税和带宽成本,同时保证云用户的长期请求队列稳定性。针对外部负荷、电价和碳排放量的随机性,提出了一种在线分散的GREEN算法。该算法可实现任意的成本-延迟权衡,从而使csp能够根据期望目标做出灵活的调度选择。迹迹驱动仿真验证了GREEN在非平稳环境中的有效性和适应性。
In this paper, we present a unifying optimization framework for greening geographically distributed Data Centers (DCs) by means of three control operations: (1) inter-DC request routing at front-end proxies, (2) intra-DC request routing at tiered core switches and edge switches, and (3) Virtual Machine (VM) scheduling on heterogeneous physical servers. We first formulate the request routing and VM scheduling problem as a stochastic program which aims to minimize the time average electricity cost, carbon taxes and bandwidth cost for Cloud Service Providers (CSPs) while guaranteeing the long-term request queue stability for cloud users. Then we propose an online and decentralized algorithm named GREEN to address the randomness of external workload, power price, and carbon emission rate. The proposed algorithm provably achieves arbitrary cost-delay tradeoffs, thereby enabling CSPs to make flexible scheduling choices toward the desired objective. Trace-driven simulations confirm the efficacy and adaptivity of GREEN in non-stationary environments.
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