Modified HEFT Algorithm for Task Scheduling in Cloud Environment

Abstract Cloud computing is now dominated in the area of high performance distributing computing and it provides resource polling and on demand services through internet. Therefore task scheduling becomes an important research area in the field of cloud environment because user’s services demand change dynamically. Heterogeneous Earliest Finish Time (HEFT) unable to distribute the task efficiently. We modify HEFT algorithm that distribute the workload among the processor in effective way and reduce the makespan time of applications. Computational results (Fig. 4-5) shows that modify HEFT algorithm perform better than existing HEFT, Heterogeneous Earliest Finish Time (CPOP) algorithm.

[1]  Ke Xu,et al.  Dynamic Resource Provisioning and Scheduling with Deadline Constraint in Elastic Cloud , 2013, 2013 International Conference on Service Sciences (ICSS).

[2]  Shafii Muhammad Abdulhamid,et al.  Tasks Scheduling Technique Using League Championship Algorithm for Makespan Minimization in IaaS Cloud , 2015, ArXiv.

[3]  Shafii Muhammad Abdulhamid,et al.  Resource scheduling for infrastructure as a service (IaaS) in cloud computing: Challenges and opportunities , 2016, J. Netw. Comput. Appl..

[4]  Philip Samuel,et al.  Enhanced Bee Colony Algorithm for Efficient Load Balancing and Scheduling in Cloud , 2015, IBICA.

[5]  Salim Hariri,et al.  Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[6]  K. Chandrasekaran,et al.  Bat algorithm for scheduling workflow applications in cloud , 2015, 2015 International Conference on Electronic Design, Computer Networks & Automated Verification (EDCAV).

[7]  Shafii Muhammad Abdulhamid,et al.  Symbiotic Organism Search optimization based task scheduling in cloud computing environment , 2016, Future Gener. Comput. Syst..

[8]  Yihua Lan,et al.  The load balancing algorithm in cloud computing environment , 2012, Proceedings of 2012 2nd International Conference on Computer Science and Network Technology.

[9]  Sarbjeet Singh,et al.  A review of metaheuristic scheduling techniques in cloud computing , 2015 .

[10]  Guan Wang,et al.  Task Scheduling Algorithm Based on Improved Min-Min Algorithm in Cloud Computing Environment , 2013 .

[11]  Fatma A. Omara,et al.  Dynamic task scheduling algorithm with load balancing for heterogeneous computing system , 2012 .

[12]  Yi Peng,et al.  The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment , 2011, The Journal of Supercomputing.

[13]  Jie Qian,et al.  Task Scheduling and Resource Allocation of Cloud Computing Based on QoS , 2014 .

[14]  Nicola Cordeschi,et al.  FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method , 2014, Cluster Computing.

[15]  G Krishnalal,et al.  Credit Based Scheduling Algorithm in Cloud Computing Environment , 2015 .

[16]  Kalka Dubey,et al.  Job Scheduling Algorithm in Cloud Environment Considering the Priority and Cost of Job , 2016, SocProS.

[17]  Hai Yang,et al.  Improved Ant Colony Algorithm Based on PSO and its Application on Cloud Computing Resource Scheduling , 2014, CIT 2014.