Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm

The on-demand provisioning and resource availability in cloud computing make it ideal for executing scientific workflow applications. An application can start execution with a minimum number of resources and allocate further resources when required. However, workflow scheduling is an NP hard problem and therefore meta-heuristics based solutions have been widely explored for the same. This paper presents an augmented Shuffled Frog Leaping Algorithm (ASFLA) based technique for resource provisioning and workflow scheduling in the Infrastructure as a service (IaaS) cloud environment. The performance of the ASFLA has been compared with the state of art PSO and SFLA algorithms. The efficacy of ASFLA has been assessed over some well-known scientific workflows of varied sizes using a custom Java based simulator. The simulation results show a marked improvement in the performance criteria of achieving minimum execution cost and meeting the schedule deadlines. Meta-heuristic algorithms explored for workflow scheduling in clouds.An improvement proposed to the meta-heuristic algorithms.An augmented variation of Shuffled Frog Leaping Algorithm (ASFLA) formulated.Obtained solutions are execution cost optimal and also meet deadline constraint.ASFLA outperforms Particle Swarm Optimization and SFLA.

[1]  Hema Banati,et al.  Improved shuffled frog leaping algorithm for continuous optimisation adapted SEVO toolbox , 2013, Int. J. Adv. Intell. Paradigms.

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

[3]  Sasmita Kumari Padhy,et al.  Dynamic task scheduling using a directed neural network , 2015, J. Parallel Distributed Comput..

[4]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[5]  R. Buyya,et al.  A budget constrained scheduling of workflow applications on utility Grids using genetic algorithms , 2006, 2006 Workshop on Workflows in Support of Large-Scale Science.

[6]  Jun Zhang,et al.  Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches , 2015, ACM Comput. Surv..

[7]  Hema Banati,et al.  Trust aware social context filtering using Shuffled frog leaping algorithm , 2012, 2012 12th International Conference on Hybrid Intelligent Systems (HIS).

[8]  Xiao Liu,et al.  A Revised Discrete Particle Swarm Optimization for Cloud Workflow Scheduling , 2010, 2010 International Conference on Computational Intelligence and Security.

[9]  Ali Maroosi,et al.  Application of shuffled frog-leaping algorithm on clustering , 2009 .

[10]  Ewa Deelman,et al.  Scientific Workflows in the Cloud , 2011 .

[11]  Muzaffar Eusuff,et al.  Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization , 2006 .

[12]  Yun Yang,et al.  Robust Scheduling of Scientific Workflows with Deadline and Budget Constraints in Clouds , 2014, 2014 IEEE 28th International Conference on Advanced Information Networking and Applications.

[13]  Rajkumar Buyya,et al.  A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[14]  Jun Zhang,et al.  An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[15]  Alexandru Iosup,et al.  A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing , 2009, CloudComp.

[16]  Jeffrey D. Ullman,et al.  NP-Complete Scheduling Problems , 1975, J. Comput. Syst. Sci..

[17]  Rajkumar Buyya,et al.  Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds , 2014, IEEE Transactions on Cloud Computing.

[18]  Marty Humphrey,et al.  Auto-scaling to minimize cost and meet application deadlines in cloud workflows , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[19]  Athanasios V. Vasilakos,et al.  Cloud Computing , 2014, ACM Comput. Surv..

[20]  Jarek Nabrzyski,et al.  Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

[21]  Rajkumar Buyya,et al.  A Dynamic Critical Path Algorithm for Scheduling Scientific Workflow Applications on Global Grids , 2007, Third IEEE International Conference on e-Science and Grid Computing (e-Science 2007).

[22]  Guang-Yu Zhu,et al.  An improved Shuffled Frog-leaping Algorithm to optimize component pick-and-place sequencing optimization problem , 2014, Expert Syst. Appl..

[23]  Shiyong Lu,et al.  Scheduling Scientific Workflows Elastically for Cloud Computing , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[24]  Qian Tao,et al.  A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow , 2011, Comput. Oper. Res..

[25]  Ann L. Chervenak,et al.  Characterizing and profiling scientific workflows , 2013, Future Gener. Comput. Syst..