A Genetic Algorithm for Scheduling Workflow Applications in Unreliable Cloud Environment
Cloud Computing refers to application and services offered over Internet using pay-as-you-go model. The services are offered from data centers all over the world, which jointly are referred to as the “Cloud”. The data centers use scheduling techniques to
- PDF / 812,005 Bytes
- 12 Pages / 439.363 x 666.131 pts Page_size
- 51 Downloads / 201 Views
Abstract. Cloud Computing refers to application and services offered over Internet using pay-as-you-go model. The services are offered from data centers all over the world, which jointly are referred to as the “Cloud”. The data centers use scheduling techniques to effectively allocate virtual machines to cloud applications. The cloud applications in area such as business enterprises, bioinformatics and astronomy need workflow processing in which tasks are executed based on data dependencies. The cloud users impose QoS constraints while executing their workflow applications on cloud. The QoS parameters are defined in SLA (Service Level Agreement) document which is signed between cloud user and cloud provider. In this paper, a genetic algorithm has been proposed that schedules workflow applications in unreliable cloud environment and meet user defined QoS constraints. A budget constrained time minimization genetic algorithm has been proposed which reduces the failure rate and makespan of workflow applications. It allocates those resources to workflow application which are reliable and cost of execution is under user budget. The performance of genetic algorithm has been compared with max-min and minmin scheduling algorithms in unreliable cloud environment.
1
Introduction
Cloud Computing offers computational and storage resources on demand by using pay-as-you-go model. The computational and storage resources are provided with the help of virtualization technologies. The cloud applications in areas such as business enterprises, bio-informatics and astronomy need workflow processing in which tasks are executed based on data dependencies. Cloud users generally impose QoS constraints while executing their applications on the cloud. These QoS parameters are defined in SLA (Service Level Agreement), which is signed between cloud user and cloud provider. The workflow applications may contain sensitive data that cannot tolerate failure of resource on which the applications are scheduled along with QoS constraints. With this motivation, we propose genetic algorithm which schedules workflow applications in unreliable cloud environment and meets user defined QoS constraints. The proposed genetic algorithm finds the schedule that costs the user under his budget and also reduces the failure rate and makespan of workflow G. Martínez Pérez et al. (Eds.): SNDS 2014, CCIS 420, pp. 139–150, 2014. © Springer-Verlag Berlin Heidelberg 2014
140
L. Singh and S. Singh
application. The performance of the algorithm has been compared with list heuristic scheduling algorithms viz. max-min and min-min, in unreliable cloud environment. The rest of the paper is structured as follow: The related work is presented in section 2. The problem overview is presented in section 3. The proposed approach is presented in section 4. Experimental results and comparison are presented in section 5 and Section 6 concludes the work carried out.
2
Related Work
Many heuristic and meta-heuristic approaches have been proposed by different researchers to schedu
Data Loading...
