ijaser
IJASER publishes high-quality, original research papers, brief reports, and critical reviews in all theoretical, technological, and interdisciplinary studies that make up the fields of advanced science and engineering and its applications.
Abstract—In the current work, we analyzed the problem of multi-criteria Cloud workflow scheduling. Based on a careful selection of the relevant work in this area and on our own experience, we identified the main aspects of the problem that have to be taken into consideration when scheduling workflows on the Cloud.Distributed computing is an expansion of parallel registering, appropriated figuring and network processing. Cloud gives on request benefits in light of client prerequisites. The vast majority of the current Research for planning focus on cost or time or both. In this examination, the Multiple VM schedules the administrations in view of more than three QOS prerequisite, for example, time cost, dependability and accessibility. It assesses execution for different experiments with the various number of work processes and distinctive arrangement of QoS parameters for every work process. The Scheduling of Different VM of cloud results is the enhanced execution from the current technique, for example, diminishing time impact, lessening cost impact and also increment unwavering quality and accessibility in a solitary target way.
Keywords- Multi-criteria workflow scheduling ,Partical swarm optimization,Heuristic Earliest
Finish Time algorithm
[1]J. J. Durillo, v. Nae, and r. Prodan, “multi-objective energy-efficient workflow scheduling using list-based heuristics,” future generation computer systems, vol. [36], pp. [221]–[236], [2014].
[2]Y. C. Lee and A. Y. Zomaya, “stretch out and compact: workflow scheduling with resource abundance,” in proceedings of the [2013] international symposium on cluster cloud and the grid (ccgrid). Ieee, [2013], conference proceedings, pp. [367]–[381].
[3]S. Abrishami, m. Naghibzadeh, and D. Epema, “cost-driven scheduling of grid workflows using partial critical paths,” ieee transactions on parallel and distributed systems, vol. [23], no. [8], pp. [1400]– [1414],[ 2012].
[4]S. Abrishami, m. Naghibzadeh, and D. H. Epema, “deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds,” future generation computer systems, vol. [29], no. [1], pp. [158]–[169],[ 2013].
[5]Y. Xu, k. Li, J. Hu, and K. Li, “a genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues,” information sciences, vol. [270], pp. [255]–[287], [2014].
[6].amazonweb service, http://aws.amazon.com/autoscaling
[7]J. Yu and R. Buyya, “workflow scheduling algorithms for grid computing,” in metaheuristics for scheduling in distributed computing environments, f. Xhafa and a. Abraham, eds., springer, berlin, germany, [2008].
[8].X. Liu, Y. Yang, Y. Jiang, and J. Chen, “preventing temporal violations in scientific workflows:
where and how,” ieee transactions on software engineering, vol. [37], no. [6], pp. [805]–[825], [2011].
[9]J. Yu and R. Buyya, “scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms,” scientific programming, vol. [14], no. [3]-[4], pp. [217]–[230], [2006].
[10].zhaomengzhu, gongxuanzhang, “evolutionary multi-objective workflow scheduling in cloud”, ieee transactions on parallel and distributed systems, vol. [27], no. [5], may [2016]
[11]Z. Zhu, G. Zhang, M. Li, and X. Liu, “evolutionary multi-objective workflow scheduling in cloud,” doi:10.1109/tpds.[2015].2446459, [2015].
[12]R. N. Calheiros and R. Buyya, “meeting deadlines of scientific work- flows in public clouds with tasks replication,” ieee transactions on parallel and distributed systems, vol. [25], no. [7], pp.[ 1787]– [1796],[ 2014].
[13]X. Tang, K. Li, G. Liao, K. Fang, and F. Wu, “a stochastic scheduling algorithm for precedence constrained tasks on grid,” future generation computer systems, vol. 27, no. [8], pp. [1083]–[1091], [2011].
[14]W. Zheng and R. Sakellariou, “stochastic dag scheduling using a monte carlo approach,”
journal of parallel and distributed computing, vol. [73], no. [12], pp. [1673]–[1689], [2013].
[15]M. Rodriguez sossa and R. Buyya, “deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds,” ieee transactions on cloud computing, vol. [2], no. [2], pp. [222]–[235], [2014].
-->