42 QoS-Aware Autonomic Resource Management in Cloud Computing: A Systematic Review SUKHPAL SINGH and INDERVEER CHANA, Thapar University, Patiala 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 re- sources, 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 mech- anisms 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 tech- niques 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. Categories and Subject Descriptors: A.1 [General Literature]: Introductory and Survey; C.0 [General]: Systems Architectures; C.2.4 [Computer-Communication Networks]: Distributed Systems; D.4.1 [Pro- cess Management]: Scheduling; H.3.4 [Systems and Software]: Distributed Systems; J.7 [Distributed Parallel and Cluster Computing]; K.6.2 [Management of Computing and Information Systems]: Installation Management General Terms: Documentation, Cloud Computing, Methodical Analysis, Theory, Management Additional Key Words and Phrases: Resource provisioning, cloud computing, autonomic management, service-level agreement, quality of service, grid computing, resource scheduling, autonomic cloud comput- ing, autonomic computing, self-management, self-optimizing, self-protecting, self-healing, self-configuring, resource management Sukhpal Singh gratefully acknowledges the Department of Science and Technology (DST), Government of India, for awarding him the INSPIRE (Innovation in Science Pursuit for Inspired Research) Fellowship (Registration/IVR Number: 201400000761 [DST/INSPIRE/03/2014/000359]) to carry out this research work. Mr. Singh received the Gold Medal in Master of Engineering in Software Engineering. Mr. Singh is on the Roll-of-honor being the DST Inspire Fellow as an SRF Professional under the INSPIRE Fellowship. We would like to thank all the anonymous reviewers for their valuable comments and suggestions for improving the article. We would like to thank Dr. Maninder Singh [EC-Council’s Certified Ethical Hacker (C-EH)] for useful suggestions. Authors’ addresses: S. Singh, Computer Science and Engineering Department, Thapar University, Patiala, Punjab, India-147004; email: ssgill@thapar.edu; I. Chana, Computer Science and Engineering Department, Thapar University, Patiala, Punjab, India-147004; email: inderveer@thapar.edu. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permissions@acm.org. c  2015 ACM 0360-0300/2015/12-ART42 $15.00 DOI: http://dx.doi.org/10.1145/2843889 ACM Computing Surveys, Vol. 48, No. 3, Article 42, Publication date: December 2015.