Total views : 238
Memetic Algorithm for Multi-objective Workflow Scheduling In Cloud
Objectives: Cloud computing is a service delivery over the internet where users pay based on the usage and the Quality of service (Qos). The cloud environment supports high performance computing based on protocols, which allow sharing of computation and storage. Scheduling in a cloud is the process of scheduling the virtual machines (VM) to meet the customer’s request. Methods/Statistical Analysis: The proposed evolutionary algorithm called Memetic Algorithm (MA) takes makespan and total cost as two objectives and gives an optimal workflow schedule of jobs. Findings: The algorithm is testing with different IaaS parameters from Amazon. Results show that MA gives significantly better solution than other algorithms like Genetic Algorithm (GA) and Iasi Cloud Partial Critical Path (IC-PCP). The schedule generated by MA gives more stability on most of the workflow instances. Application/Improvements: The proposed model applied to schedule the VMs in a cloud in an effective way.
Cloud Computing, Genetic Algorithm, IAAS Cloud Partial Critical Path, Memetic Algorithm, Optimal Workflow Schedule.
- Yu J, Kirley M , Buyya R. Multi-Objective Planning for Workflow Execution on Grids, 8th IEEE/ ACM International Conference on Grid Computing, IEEE Computer Society, USA, 2007, p.10−17.
- Chen WN, Zhang J. An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem with Various QoS Requirements, IEEE Transaction System Man Cybern. 2009; 39(1):29−43.
- Zhang F, Cao J, Wu HK. Ordinal Optimized Scheduling of Scientific Workflows in Elastic Compute Clouds, 3rd IEEE International Conference on Cloud Computing Technology and Science, China, IEEE, 2011, p. 1−9.
- Diallo L, Hashim AHA, Olanrewaju RF, Islam S, Zarir. Two Objectives Big Data Task Scheduling using Swarm Intelligence in Cloud Computing, Indian Journal of Science and Tecnology. 2016; 9(28):1−10.
- Sakellaiou R, Zaho H, Tsiakkouri E, Dikaiakos M. Scheduling Workflows with Budget Constraints, Integrated Research in GRID Computing, Springer, 2007, p. 189−202.
- Zuo X. Self Adaptive Learning PSO – Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloud, IEEE Transactions on Automation Science and Engineering. 2014 Apr; 11(2):564−73.
- Garg R, Singh AK. Multi-objective Workflow Grid Scheduling Based on Discrete Particle Swarn Optimization, Swarn, Evolutionary and Memetic Computing. 2011 Springer; 7076:183−90.
- Durillo J, Pordan R. Multi-objective Workflow Scheduling in Amazon EC2, Cluster Computing. 2014; 17(2):169–89.
- Zhu Z, Zang G.,Evolutionary Multi-Objective Workflow Scheduling in Cloud, Transactions on Parallel and Distributed Systems. 2015; 27(5):1344−57.
- Abrishami S, Naghibzadeh M, Epema D. Deadline Constrained Workflow Scheduling Algorithms for IaaS Clouds, Future Generation Computer Systems. 2012; 19(3):680−89.
- Bharathi S, Chervanak A, Deelman E, Mehta G. Characterization of Scientific Workflows, 3rd Workshop on Workflows in Support of Large Scale IEEE, 2008, p.1−11.
- Juve G, Chervenak A, Deelman E, Bharathi S, Mehta G, Vahi K. Characterizing and Profiling Scientific Workflows, Future Generation Computer System. 2013; 29(3):682−92.
- Rodriguez MA, Buyya R. Deadline Based Resource Provisioning and Scheduling Algorithm for Scientific Workflows on Cloud, IEEE Transactions on Cloud Computing. 2014 Apr-Jun; 2(2):222−35.
- Udomkasemsub O, Xiaorong L, Achalkul T. A Multiple Objective Workflow Scheduling Framework for Cloud Data Analytics, 24th IEEE International Joint Conference in Computing Sciences and Software Engineering, IEEE, 2012, p.1−8.
- Ge Y, Wei G. GA Based Task Scheduler for the Cloud Computing Systems, International Conference on Web Information Systems and Mining, 2010, 2, p.181−86.
- Eizadpanah E, Koroupi F. Timing of Resources in Cloud Computing by using Multi-Purpose Particles Congestion Algorithm, Indian Journal of Science and Technology. 2015; 8(8):474−83.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.