ABC Optimized Weighted Task Load Balancing Algorithm in Cloud Computing

Abstract Cloud computing is a new paradigm where high quality and low-cost information services are served by cloud services providers on pay-per-use service. It offers scalability, availability and different services as important benefits. Cloud provides the services to the organizations like storage, applications, and servers. The unique concept of cloud computing creates new opportunities for Business and IT enterprises to accomplish their objectives. In cloud computing, usually, there is a number of jobs that need to be executed with the available resources to achieve best performance, least probable total time for task completion, a lesser processing cost, shortened average waiting time, lessened response time, and effective utilization of resources etc. Normally errands are scheduled by client prerequisites. Latest scheduling systems should be designed to conquer the issues proposed by the system properties in the hub of client and assets. The new scheduling methodologies might utilize a segment of the usual scheduling recommendations to union them in conjunction with some procedure conscious techniques to present options for higher and extra remarkable venture scheduling. Task scheduling algorithms admit being actually the biggest hypothetical issues in the cloud computing domain. Countless deep investigations and efforts had been applied in this regard. This paper proposes a weighted load balancing based task scheduling algorithm that considers a wide variety of attributes in the cloud environment and uses weight based sorting for prioritizing the tasks and bee colony optimization for balancing the load. The proposed algorithm considers four parameters i.e. Total processing cost, total processing time, makespan time and the average waiting time. The paper desires to enhance the performance and compare the performance with antecedent implemented task scheduling algorithm. Keywords: Cloud Computing, Job Scheduling, Resource allocation, Efficiency, Performance, Cost, Quality of Service (QoS), Virtual Machine (VM)