Abstract: This study focuses on the issues of resource allocation in Cloud computing. The user requirements, strategies, execution time and resource management for resource allocation is reviewed in different Cloud applications. The study also indicates the problems faced during resource allocation and how to improve the resource allocation efficiency in the Cloud. Load balancing guarantees the optimum use of available resources and thereby empowers reliability and performance of the overall system. In this study, an optimization technique Artificial Immune System (AIS) is utilized to provide a fast, accurate and nearest resource to the appropriate user request. The communication model used in this study follows the Directed Acyclic Graph (DAG) model which can improve the communication and make the cloud take the right decision for resource allocation. This study utilizes an artificial immune system based on time, cost, energy and the relevant resource allocation in a Cloud environment. The performance of the proposed resource allocation model, AIS-DAG is analyzed using NS3-GreenCloud. The simulation results show that the proposed AIS-DAG model has enormous potential as it offers major enhancements in the traits of response time, high potential for the improvement in energy efficiency of the data center and can effectively meet the service level agreement requested by the users.
M. Kandan and R. Manimegalai, 2016. AIS-DAG: Artificial Immune System for Directed Acyclic Graphs Model Based Fair Resource Allocation for Heterogeneous Cloud Computing. Asian Journal of Information Technology, 15: 3673-3686.