Resource management (RM) is a challenging task in a cloud computing environment where a large number of virtualized, heterogeneous, and distributed resources are hosted in the datacentres. The uncertainty, heterogeneity, and the dynamic nature of such resources affect the efficiency of provisioning, allocation, scheduling, and monitoring tasks of RM. The most existing RM techniques and strategies have insufficiency in handling such cloud resources dynamic behaviour. To resolve these limitations, there is a need for the design and development of intelligent and efficient autonomic RM techniques to ensure the Quality-of-Service (QoS) of cloud-based applications, satisfy the cloud user requirements, and avoid a Service-Level Agreement (SLA) violations. This paper presents a comprehensive review along with a taxonomy of the most recent existing autonomic and elastic RM techniques in a cloud environment. The taxonomy classifies the existing autonomic and elastic RM techniques into different categories based on their design, objective, function, and applications. Moreover, a comparison and qualitative analysis is provided to illustrate their strengths and weaknesses. Finally, the open issues and challenges are highlighted to help researchers in finding significant future research options.

在云计算环境中，资源管理（RM）是一项艰巨的任务，在云计算环境中，数据中心中托管有大量虚拟化，异构和分布式资源。这些资源的不确定性，异构性和动态性质会影响RM的置备，分配，调度和监视任务的效率。现有的RM技术和策略在处理此类云资源动态行为方面不足。为了解决这些限制，需要设计和开发智能高效的自主RM技术，以确保基于云的应用程序的服务质量（QoS），满足云用户的需求并避免服务级别违反协议（SLA）。本文对云环境中最新的现有自治和弹性RM技术进行了全面的回顾，并对其进行了分类。分类根据其设计，目标，功能和应用将现有的自治和弹性RM技术分类为不同的类别。此外，提供了比较和定性分析以说明它们的优缺点。最后，突出了未解决的问题和挑战，以帮助研究人员找到重要的未来研究选择。提供了比较和定性分析，以说明它们的优缺点。最后，突出了未解决的问题和挑战，以帮助研究人员找到重要的未来研究选择。提供了比较和定性分析，以说明它们的优缺点。最后，强调了未解决的问题和挑战，以帮助研究人员找到重要的未来研究选择。