Heterogeneity-Aware Workload Placement and Migration in Distributed Sustainable Datacenters
Heterogeneity-Aware Workload Placement and Migration in Distributed Sustainable Datacenters
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DOI：
10.1109/ipdps.2014.41
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发表时间：
2014-05
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影响因子：
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通讯作者：
Dazhao Cheng;Changjun Jiang;Xiaobo Zhou
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文献类型：
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作者：
Dazhao Cheng;Changjun Jiang;Xiaobo Zhou
虽然主要的云服务运营商已经采取了各种举措来利用绿色能源来运营其可持续数据中心，但由于其生成取决于动态自然条件，因此有效利用绿色能源具有挑战性。幸运的是，数据输入的地理分布为通过分布云工作负载来优化系统性能提供了机会。在本文中，我们提出了一个整体的异构性感知的云工作负载的放置和迁移方法，sCloud，旨在最大限度地提高系统的好放在分布式自我可持续的数据输入。sCloud自适应地将事务工作负载放置到分布式数据中心，将可用资源分配给每个数据中心中的异构工作负载，并跨数据中心迁移批处理作业，同时考虑绿色电源可用性和QoS要求。我们制定的事务性工作负载的位置作为一个约束优化问题，可以解决的非线性规划。然后，我们提出了一个批作业迁移算法，以进一步提高系统的好把当绿色电源变化很大，在不同的位置。我们已经在一个大学的云测试平台上实现了sCloud，该平台具有真实的天气条件和工作负载跟踪。实验结果表明，sCloud可以实现接近最佳的系统性能，同时对动态电源可用性具有弹性。它在提高系统良率和减少QoS违规方面的性能分别比异构性无关方法高出26%和29%。
While major cloud service operators have taken various initiatives to operate their sustainable data enters with green energy, it is challenging to effectively utilize the green energy since its generation depends on dynamic natural conditions. Fortunately, the geographical distribution of data enters provides an opportunity for optimizing the system performance by distributing cloud workloads. In this paper, we propose a holistic heterogeneity-aware cloud workload placement and migration approach, sCloud, that aims to maximize the system good put in distributed self-sustainable data enters. sCloud adaptively places the transactional workload to distributed data enters, allocates the available resource to heterogeneous workloads in each data enter, and migrates batch jobs across data enters, while taking into account the green power availability and QoS requirements. We formulate the transactional workload placement as a constrained optimization problem that can be solved by nonlinear programming. Then, we propose a batch job migration algorithm to further improve the system good put when the green power supply varies widely at different locations. We have implemented sCloud in a university cloud test bed with real-world weather conditions and workload traces. Experimental results demonstrate sCloud can achieve near-to-optimal system performance while being resilient to dynamic power availability. It outperforms a heterogeneity-oblivious approach by 26% in improving system good put and 29% in reducing QoS violations.