In recent years, there has been a great increase in usage of cloud data centers which leads the energy consumption growth by about 10% a year continuously. Further, due to the increase in temperature of cloud data center, the hardware failure rate increases and maintenance cost is also increased. Hotspots and an irregular temperature distribution are the major research issues. Therefore, accurate and reliable thermal management of the data center is a challenging task. In order to select appropriate and efficient thermal management approach, this survey presents a state of art review on the development in this field, discusses the classification of thermal management approaches and thermal mapping models. This review also reveals the thermal management and heat management strategies at data center level to make the data centre more energy efficient. Further, it discusses various thermal aware task scheduling strategies. In thermal management, computing equipments are scheduled with the objective to minimize the hotspot and cooling cost. Finally, evaluation metrics for measuring thermal efficiency and open research issues in this field are provided. Both researchers and academicians find this review useful since it presents the significant research in the field of thermal management.

近年来，云数据中心的使用已大大增加，这导致能源消耗每年连续增长约10％。此外，由于云数据中心的温度升高，硬件故障率增加并且维护成本也增加。热点和温度分布不规则是主要的研究问题。因此，数据中心的准确和可靠的热管理是一项艰巨的任务。为了选择合适和有效的热管理方法，本次调查介绍了该领域的最新发展状况，讨论了热管理方法和热图模型的分类。这篇评论还揭示了数据中心级别的热管理和热管理策略，以使数据中心更加节能。此外，它讨论了各种热感知任务调度策略。在热管理中，计划安排计算设备的目的是最大程度地减少热点和冷却成本。最后，提供了用于测量热效率的评估指标，并提供了该领域的开放研究问题。研究人员和院士都认为这篇评论有用，因为它提出了热管理领域的重要研究。提供了用于测量热效率的评估指标，并提供了该领域的开放研究问题。研究人员和院士都认为这篇评论有用，因为它提出了热管理领域的重要研究。提供了用于测量热效率的评估指标，并提供了该领域的开放研究问题。研究人员和院士都认为这篇评论有用，因为它提出了热管理领域的重要研究。