Cloud Task Scheduling for Load Balancing based on Intelligent Strategy

Abstract

Abstract-Cloud computing is a type of parallel and distributed system consisting of a co llect ion of interconnected and virtual computers. With the increasing demand and benefits of cloud computing infrastructure, different co mputing can be performed on cloud environment. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorith ms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper a cloud task scheduling policy based on ant colony optimizat ion algorith m for load balancing compared with different scheduling algorith ms has been proposed. Ant Co lony Optimization (ACO) is random optimization search approach that will be used for allocating the incoming jobs to the virtual mach ines. The main contribution of our work is to balance the system load while t rying to min imizing the make span of a given tasks set. The load balancing factor, related to the job fin ishing rate, is proposed to make the job fin ishing rate at different resource being similar and the ability of the load balancing will be improved. The proposed scheduling strategy was simu lated using Cloudsim toolkit package. Experimental results showed that, the proposed algorith m outperformed scheduling algorith ms that are based on the basic ACO or Modified Ant Colony Optimization (MACO).

Task scheduling and resource allocation in cloud computing using a heuristic approach

“…Alejandra et al [13] proposed the use of meta-heuristic optimization and particle swarm optimization to reduce execution costs through scheduling. Keshk et al [14] proposed the use of modified ant colony optimization in load balancing. This method improves the makespan of a job.…”

Design o f a Fault Tolerant Strategy f or Resource Scheduling i n Cloud Environment

“…A load balancing intelligent strategy for task scheduling [7] is proposed that makes use of Ant Colony Optimization which allocates tasks to VMs. This algorithm works by considering the load balancing factor, related to the task finishing rate.…”

“…Scheduling the cloudlet in cloud computing is NP-hard problem [20]. This NP hard problem is defined as a platform constituted by potentially large numbers of users from different environments and with different needs and demands on thousands or millions of Virtual Machines waiting for their turn to be served [7,9]. It is the wish of every cloud service provider to ensure that every available resource is fully utilized to avoid resources being idle [10].…”

Job Scheduling and Resource sharing on cloud platform based on Improved Bees Algorithm.

“…If a job can't be completed during its turn, it will store back to the queue waiting for the next turn until it is completed. Each job is given a quantum, hence eliminating the need to wait for the previous job to complete execution [5]. The First Come First Serve (FCFS) algorithm is a simple job scheduling algorithm, executes jobs as they are sent by the users.…”