The major reason for using a simulator, instead of a real test-bed, is to enable repeatable evaluation of large-scale cloud systems. CloudSim, the most widely used simulator, enables users to implement resource provisioning, and management policies. However, CloudSim does not provide support for: (i) interactive online services; (ii) platform heterogeneities; (iii) virtual machine migration modelling; and (iv) other essential models to abstract a real datacenter. This paper describes modifications needed in the classical CloudSim to support realistic experimentations that closely match experimental outcomes in a real system. We extend, and partially re-factor CloudSim to “PerficientCloudSim” in order to provide support for large-scale computation over heterogeneous resources. In the classical CloudSim, we add several classes for workload performance variations due to: (a) CPU heterogeneities; (b) resource contention; and (c) service migration. Through plausible assumptions, our empirical evaluation, using real workload traces from Google and Microsoft Azure clusters, demonstrates that “PerficientCloudSim” can reasonably simulate large-scale heterogeneous datacenters in respect of resource allocation and migration policies, resource contention, and platform heterogeneities. We discuss statistical methods to measure the accuracy of the simulated outcomes.

使用模拟器而不是真实的测试平台的主要原因是能够对大型云系统进行可重复的评估。最广泛使用的模拟器CloudSim使用户能够实施资源供应和管理策略。但是，CloudSim不提供以下支持：（i）交互式在线服务；（ii）平台异质性；（iii）虚拟机迁移建模；（iv）其他用于抽象真实数据中心的基本模型。本文介绍了经典CloudSim所需的修改，以支持与真实系统中的实验结果非常匹配的现实实验。我们将CloudSim扩展并部分重构为“ PerficientCloudSim”，以便为异构资源上的大规模计算提供支持。在经典的CloudSim中，由于以下原因，我们为工作负载性能变化添加了几类：（a）CPU异构性；（b）资源争用；（c）服务迁移。通过合理的假设，我们的实证评估使用了来自Google和Microsoft Azure群集的真实工作负载跟踪，表明“ PerficientCloudSim”可以在资源分配和迁移策略，资源争用以及平台异构性方面合理地模拟大型异构数据中心。我们讨论统计方法来衡量模拟结果的准确性。证明“ PerficientCloudSim”可以在资源分配和迁移策略，资源争用以及平台异构性方面合理地模拟大型异构数据中心。我们讨论统计方法来衡量模拟结果的准确性。证明“ PerficientCloudSim”可以在资源分配和迁移策略，资源争用以及平台异构性方面合理地模拟大型异构数据中心。我们讨论统计方法来衡量模拟结果的准确性。