Workload consolidation is an important method for the efficient operation of cloud data centers, impacting important quality attributes such as resource utilization and power consumption. Many different approaches have been proposed for workload consolidation, but few comparative studies were executed to date. Therefore, it is unclear which of the proposed approaches work best in which situation. In this article, we present a comprehensive simulation‐based comparison of five workload consolidation techniques. We introduce a general framework for workload consolidation techniques to the DISSECT‐CF simulator to foster the development and comparison of efficient data center consolidation algorithms. We use this framework to evaluate the effectiveness of a first fit best fit decreasing heuristic, a custom heuristic, and three population‐based metaheuristics (genetic algorithm, artificial bee colony, and particle swarm optimization). The evaluation is based on a wide variety of real‐world workload traces. The five algorithms are compared in terms of total energy consumption, the duration of the simulation, and the number of migrations. Based on the results, there is no generally best consolidation technique. The results deliver insight into the pros and cons of the algorithms as well as the impact of different parameters. In particular, the results show that population‐based metaheuristics do not offer a significant gain in terms of solution quality to compensate for the increased simulation time.

中文翻译：

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云数据中心工作负载整合算法的比较

工作负载合并是有效运行云数据中心的重要方法，会影响重要的质量属性，例如资源利用率和功耗。已经提出了许多不同的方法来合并工作负载，但是迄今为止很少进行比较研究。因此，尚不清楚哪种提议的方法在哪种情况下效果最佳。在本文中，我们将对五种工作负载合并技术进行全面的基于仿真的比较。我们向DISSECT-CF模拟器引入了工作负载整合技术的通用框架，以促进高效数据中心整合算法的开发和比较。我们使用此框架来评估最适合递减启发式，自定义启发式，以及三种基于人口的元启发法（遗传算法，人工蜂群和粒子群优化）。评估基于各种实际工作负载跟踪。比较了这五种算法的总能耗，仿真持续时间和迁移次数。根据结果​​，通常没有最佳的合并技术。结果可以深入了解算法的优缺点以及不同参数的影响。尤其是，结果表明，基于群体的元启发式方法在解决方案质量方面并不能提供明显的收益来补偿增加的仿真时间。比较了这五种算法的总能耗，仿真持续时间和迁移次数。根据结果​​，通常没有最佳的合并技术。结果可以深入了解算法的优缺点以及不同参数的影响。尤其是，结果表明，基于群体的元启发式方法在解决方案质量方面并不能提供明显的收益来补偿增加的仿真时间。比较了这五种算法的总能耗，仿真持续时间和迁移次数。根据结果​​，通常没有最佳的合并技术。结果可以深入了解算法的优缺点以及不同参数的影响。尤其是，结果表明，基于群体的元启发式方法在解决方案质量方面并不能提供明显的收益来补偿增加的仿真时间。