Efficient autonomic and elastic resource management techniques in cloud environment: taxonomy and analysis

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.