Abstract
Nowadays, cloud computing makes it possible for users to use the computing resources like application, software, and hardware, etc., on pay as use model via the internet. One of the core and challenging issue in cloud computing is the task scheduling. Task scheduling problem is an NP-hard problem and is responsible for mapping the tasks to resources in a way to spread the load evenly. The appropriate mapping between resources and tasks reduces makespan and maximizes resource utilization. In this paper, we present and implement an independent task scheduling algorithm that assigns the users' tasks to multiple computing resources. The proposed algorithm is a hybrid algorithm for task scheduling in cloud computing based on a genetic algorithm (GA) and particle swarm optimization (PSO). The algorithm is implemented and simulated using CloudSim simulator. The simulation results show that our proposed algorithm outperforms the GA and PSO algorithms by decreasing the makespan and increasing the resource utilization.
Highlights
Cloud computing is a novel technology that enhances the usage of the virtualized resources on the internet for the end user
PROPOSED METHOD we have introduced the genetic algorithm (GA) and Particle swarm optimization (PSO) algorithms and presented HTSCC algorithm in detail
The Proposed hybrid HTSCC algorithm The HTSCC algorithm is an optimization algorithm that consolidates the features of GA and PSO algorithms to overcome the drawbacks of these two algorithms
Summary
Cloud computing is a novel technology that enhances the usage of the virtualized resources on the internet for the end user. Cloud computing consists of a collection of a huge number of computing resources such as virtual machines, network bandwidth, processing, and storage [2]. Provisioning of these resources on demand is one of the major objectives of the cloud computing task scheduling. Genetic algorithm and particle swarm optimization are the most popular meta-heuristic techniques to solve the TSP. The proposed HTSCC algorithm improves the local search by using the GA mutation operator and expected to work with the different size of tasks. These features of the proposed algorithm reduce the makespan and increase the resources utilization.
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