Abstract
Cloud computing is a new commercial model that enables customers to acquire large amounts of virtual resources on demand. Resources including hardware and software can be delivered as services and measured by specific usage of storage, processing, bandwidth, etc. In Cloud computing, task scheduling is a process of mapping cloud tasks to Virtual Machines (VMs). When binding the tasks to VMs, the scheduling strategy has an important influence on the efficiency of datacenter and related energy consumption. Although many traditional scheduling algorithms have been applied in various platforms, they may not work efficiently due to the large number of user requests, the variety of computation resources and complexity of Cloud environment. In this paper, we tackle the task scheduling problem which aims to minimize makespan by Genetic Algorithm (GA). We propose an incremental GA which has adaptive probabilities of crossover and mutation. The mutation and crossover rates change according to generations and also vary between individuals. Large numbers of tasks are randomly generated to simulate various scales of task scheduling problem in Cloud environment. Based on the instance types of Amazon EC2, we implemented virtual machines with different computing capacity on CloudSim. We compared the performance of the adaptive incremental GA with that of Standard GA, Min-Min, Max-Min , Simulated Annealing and Artificial Bee Colony Algorithm in finding the optimal scheme. Experimental results show that the proposed algorithm can achieve feasible solutions which have acceptable makespan with less computation time.
Highlights
As the National Institute of Standards and Technology (NIST) defines, cloud computing [1]is a new paradigm that provides configurable resource pool by network access
The main contributions of this paper include: (i) proposing an incremental genetic algorithm to tackle large scale task scheduling problem in Cloud environment; (ii) designing an adaptive mutation and crossover rate for the proposed algorithm; and (iii) experimental results obtained using the proposed method compared with the results of applying Min-Min, Max-Min, Standard Genetic Algorithm (GA), Min-Min, Max-Min, Simulated Annealing and Artificial Bee Colony Algorithm, showing that our approach can reduce the computation time
We investigate some traditional and bio-inspired algorithms that can be applied in the distributed computing environments and propose an adaptive incremental Genetic Algorithm (AIGA) to address the Cloud task scheduling problem
Summary
Babu et al [17] presented a load balancing algorithm based on Artificial Bee Colony (ABC), in which tasks on overloaded virtual machines are selected and migrated to other VMs. Navimipour et al [18] applied ABC to the task scheduling problem in the cloud environment. The main contributions of this paper include: (i) proposing an incremental genetic algorithm to tackle large scale task scheduling problem in Cloud environment; (ii) designing an adaptive mutation and crossover rate for the proposed algorithm; and (iii) experimental results obtained using the proposed method compared with the results of applying Min-Min, Max-Min, Standard GA, Min-Min, Max-Min, Simulated Annealing and Artificial Bee Colony Algorithm, showing that our approach can reduce the computation time. We investigate some traditional and bio-inspired algorithms that can be applied in the distributed computing environments and propose an adaptive incremental Genetic Algorithm (AIGA) to address the Cloud task scheduling problem.
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