The energy efficiency of a data center depends on the cooling cost of clusters in the data center. Enhancing thermal efficiency of clusters is a practical approach to reducing energy consumption cost, optimizing scalability, and improving reliability. In this paper, we propose ThermoBench to evaluate the thermal efficiency of computing and storage clusters deployed in data centers. We shed light on the criteria, metrics and challenges of developing a thermal efficiency benchmark. We pay particular attention to clusters running scalable client-server enterprise applications in data centers. Because these applications are quite popular in modern data centers, thermal efficiency benchmarks for clusters providing services to these applications become critical. We characterize workload conditions in such a cluster computing environment in forms of client sessions of multiple transactional requests. To resemble real-world applications, ThermoBench makes use of the TPC-W benchmark to change transactional requests mix and load over time. We apply ThermoBench to evaluate the thermal efficiency of a real-world cluster. Experimental results show that ThermalBench provides a simple yet powerful benchmark solution for assessing thermal behaviours of computing clusters in data centers as well as offers thermal-aware scheduling strategies during the course of requests dispatching.

数据中心的能源效率取决于数据中心集群的散热成本。提高群集的热效率是降低能耗成本，优化可伸缩性和提高可靠性的实用方法。在本文中，我们提出了ThermoBench来评估部署在数据中心中的计算和存储集群的热效率。我们阐明了开发热效率基准的标准，指标和挑战。我们特别注意在数据中心中运行可伸缩客户端服务器企业应用程序的集群。由于这些应用程序在现代数据中心中非常流行，因此为这些应用程序提供服务的集群的热效率基准变得至关重要。我们以多个事务请求的客户端会话的形式来表征这种集群计算环境中的工作负载条件。为了与现实世界中的应用程序相似，ThermoBench利用TPC-W基准来随着时间改变事务请求的混合和加载。我们应用ThermoBench评估实际集群的热效率。实验结果表明，ThermalBench提供了一个简单而强大的基准解决方案，用于评估数据中心中计算集群的热行为，并在请求分配过程中提供了热感知调度策略。我们应用ThermoBench评估实际集群的热效率。实验结果表明，ThermalBench提供了一个简单而强大的基准解决方案，用于评估数据中心中计算集群的热行为，并在请求分发过程中提供了热感知调度策略。我们应用ThermoBench评估实际集群的热效率。实验结果表明，ThermalBench提供了一个简单而强大的基准解决方案，用于评估数据中心中计算集群的热行为，并在请求分发过程中提供了热感知调度策略。