Joint request mapping and response routing for geo-distributed cloud services
Joint request mapping and response routing for geo-distributed cloud services
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DOI：
10.1109/infcom.2013.6566873
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发表时间：
2013-04
期刊：
影响因子：
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通讯作者：
Hong Xu;Baochun Li
中科院分区：
文献类型：
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作者：
Hong Xu;Baochun Li
许多云服务都运行在地理上分散的数据中心上，以获得更好的可靠性和性能。本文研究了分布式数据中心的联合请求映射和响应路由问题。我们制定的问题作为一个一般的工作负载管理优化。一个效用函数是用来捕捉各种性能目标，电力和带宽成本的位置多样性，现实建模。为了解决大规模优化问题，我们提出了一种基于交替方向乘子法（ADMM）的分布式算法。分解协调的方法之后，我们的算法允许在数据中心的并行实现，其中每个服务器解决一个小的子问题。协调这些解决方案以找到全局问题的最佳解决方案。我们的算法在几十次迭代内收敛到接近最优，并且对步长不敏感。我们经验评估我们的算法的基础上，现实世界的工作负载跟踪和延迟测量，并证明其有效性相比，传统的方法。
Many cloud services are running on geographically distributed datacenters for better reliability and performance. We consider the emerging problem of joint request mapping and response routing with distributed datacenters in this paper. We formulate the problem as a general workload management optimization. A utility function is used to capture various performance goals, and the location diversity of electricity and bandwidth costs are realistically modeled. To solve the large-scale optimization, we develop a distributed algorithm based on the alternating direction method of multipliers (ADMM). Following a decomposition-coordination approach, our algorithm allows for a parallel implementation in a datacenter where each server solves a small sub-problem. The solutions are coordinated to find an optimal solution to the global problem. Our algorithm converges to near optimum within tens of iterations, and is insensitive to step sizes. We empirically evaluate our algorithm based on real-world workload traces and latency measurements, and demonstrate its effectiveness compared to conventional methods.