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As computing infrastructure expands, resource management in a large, heterogeneous, and distributed environment becomes a challenging task. In a cloud environment, with uncertainty and dispersion of resources, one encounters problems of allocation of resources, which is caused by things such as heterogeneity, dynamism, and failures. Unfortunately, existing resource management techniques, frameworks, and mechanisms are insufficient to handle these environments, applications, and resource behaviors. To provide efficient performance of workloads and applications, the aforementioned characteristics should be addressed effectively. This research depicts a broad methodical literature analysis of autonomic resource management in the area of the cloud in general and QoS (Quality of Service)-aware autonomic resource management specifically. The current status of autonomic resource management in cloud computing is distributed into various categories. Methodical analysis of autonomic resource management in cloud computing and its techniques are described as developed by various industry and academic groups. Further, taxonomy of autonomic resource management in the cloud has been presented. This research work will help researchers find the important characteristics of autonomic resource management and will also help to select the most suitable technique for autonomic resource management in a specific application along with significant future research directions.

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The increase in energy consumption is the most critical problem worldwide. The growth and development of complex data-intensive applications have promulgated the creation of huge data centers that have heightened the energy demand. In this article, the need for energy efficiency is emphasized by discussing the dual role of cloud computing as a major contributor to increasing energy consumption and as a method to reduce energy wastage. This article comprehensively and comparatively studies existing energy efficiency techniques in cloud computing and provides the taxonomies for the classification and evaluation of the existing studies. The article concludes with a summary providing valuable suggestions for future enhancements.