Effective Modeling Approach for IaaS Data Center Performance Analysis under Heterogeneous Workload

Effective Modeling Approach for IaaS Data Center Performance Analysis under Heterogeneous Workload
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异构工作负载下 IaaS 数据中心性能分析的有效建模方法

DOI:
10.1109/tcc.2016.2560158
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发表时间:
2018-10
影响因子:
6.5
通讯作者:
Jiqiang Liu
Jiqiang Liu
中科院分区:
计算机科学2区
文献类型:
--
作者:
Xiaolin Chang;Ruofan Xia;Jogesh K. Muppala;Kishor S. Trivedi;Jiqiang Liu

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异构性不仅在物理机之间盛行,在真实的IaaS云数据中心(CDC)的工作负载之间也是如此。这种异构性使得大型且复杂的IaaS CDC的性能建模更具挑战性。本文考虑这样一种情况,即每个客户作业请求的虚拟CPU数量可能不同。我们提出了一种适用于IaaS CDC在这种异构性负载下性能分析的分层随机建模方法。通过离散事件仿真验证了所提出的解析模型在不同系统参数设置下的数值结果。
Heterogeneity prevails not only among physical machines but also among workloads in real IaaS Cloud data centers (CDCs). The heterogeneity makes performance modeling of large and complex IaaS CDCs even more challenging. This paper considers the scenario where the number of virtual CPUs requested by each customer job may be different. We propose a hierarchical stochastic modeling approach applicable to IaaS CDC performance analysis under such a heterogeneous workload. Numerical results obtained from the proposed analytic model are verified through discrete-event simulations under various system parameter settings.
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