Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

Cloud computing is a framework which provides on-demand services to the user for scalability, security, and reliability based on pay as used service anytime & anywhere. For load balancing, task scheduling is the most critical issues in the cloud environment. There are so many meta-heuristic algorithms used to solve the load balancing problem. A good task scheduling algorithm should be used for optimum load balancing in cloud environment. Such scheduling algorithm must have some vital characteristic like minimum makespan, maximum throughput, and maximum resource utilization, etc. In this paper, a dynamic load balancing and task scheduling algorithm based on ant colony optimization (DLBACO) has been proposed. This algorithm assigns the task the VM which has highest probability of availability in minimum time. The proposed algorithm balances the whole system by minimizing the makespan of the task and maximizing the throughput. CloudSim simulator is used to simulate the proposed scheduling algorithm and results show that the proposed (DLBACO) algorithm is better than the existing algorithms such as FCFS, LBACO (Load balancing ACO), and primary ACO


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