Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing

AbstractScheduling in cloud computing is the assignment of tasks to resources with maximum performance, which is a multi-purpose problem. The scheduling is of NP-Hard issues that is the reason why meta-heuristic algorithms are used in scheduling problems. The meta-heuristic scheduling algorithms are divided into two categories of biological and non-biological. Swarm-based meta-heuristics are of biological algorithms that are based on imitation, or based on sign. The whale optimization algorithm is a meta-heuristic biological swarm-based intelligence algorithm based on imitation. This algorithm suffers from the early convergence problem which means the population convergences early to an unfavorable optimum point. Usually, the early convergence occurs because of the weakness in exploration capability (global search). In this study, an optimized version of the Whale optimization algorithm is introduced that presents a new idea in grouping whales called GWOA. It is firstly proposed to overcome the early convergence problem and then make a balance between the local and the global search in finding the optimal solution. The proposed method divides the sorted population into δ groups and a member of each group is randomly selected which is used in encircling prey section of the whale optimization algorithm. Then, the average best fitness was enhanced to improve both exploitation and exploration as well as premature convergence. In the next step, GWOA is used in a cloud computing scheduler at high workload to reduce the average execution time, response time, and increase the throughput in the cloud computing environment. The proposed whale optimization algorithm is compared with the standard whale optimization algorithm (WOA), improved whale optimization algorithm (CWOA), particle swarm optimization (PSO), and bat algorithms applying CEC2017 functions to compare the average parameter of the best fit, and then they are implemented as a cloud computing scheduler. The results of the experiments show that the proposed method has a better performance in comparison with competent meta-heuristic algorithms and scheduling algorithms.

[1]  Alexandru Iosup,et al.  Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing , 2011, IEEE Transactions on Parallel and Distributed Systems.

[2]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[3]  Task Scheduling Model of Cloud Computing based on Firefly Algorithm , 2015 .

[4]  Hongwei Chen,et al.  Cloud Task Scheduling Simulation via Improved Ant Colony Optimization Algorithm , 2013 .

[5]  Ali Kaveh,et al.  Enhanced whale optimization algorithm for sizing optimization of skeletal structures , 2017 .

[6]  Farookh Khadeer Hussain,et al.  Task Scheduling Optimization in Cloud Computing Applying Multi-Objective Particle Swarm Optimization , 2013, ICSOC.

[7]  Arun Kumar Sangaiah,et al.  An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment , 2018, Cluster Computing.

[8]  Alexandru Iosup,et al.  Grid Computing Workloads , 2011, IEEE Internet Computing.

[9]  Ling Wang,et al.  A Pareto based fruit fly optimization algorithm for task scheduling and resource allocation in cloud computing environment , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[10]  Faramarz Safi-Esfahani,et al.  An Adaptive and Fuzzy Resource Management Approach in Cloud Computing , 2019, IEEE Transactions on Cloud Computing.

[11]  Yanping Bai,et al.  A whale optimization algorithm with inertia weight , 2016 .

[12]  V. SURESH KUMAR,et al.  HYBRID OPTIMIZED LIST SCHEDULING AND TRUST BASED RESOURCE SELECTION IN CLOUD COMPUTING , 2014 .

[13]  Jyh-Horng Chou,et al.  Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm , 2013, Comput. Oper. Res..

[14]  S. Chettih,et al.  A hybrid whale algorithm and pattern search technique for optimal power flow problem , 2016, 2016 8th International Conference on Modelling, Identification and Control (ICMIC).

[15]  Nima Jafari Navimipour,et al.  Task scheduling in the Cloud Environments based on an Artificial Bee Colony Algorithm , 2015 .

[16]  Leili Salimian,et al.  Survey of Energy Efficient Data Centers in Cloud Computing , 2013 .

[17]  Medhat A. Tawfeek,et al.  Cloud task scheduling based on ant colony optimization , 2013, 2013 8th International Conference on Computer Engineering & Systems (ICCES).

[18]  Faramarz Safi Esfahani,et al.  A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing , 2018, The Journal of Supercomputing.

[19]  Faramarz Safi Esfahani,et al.  Energy-efficient placement of virtual machines in cloud data centres based on fuzzy decision making , 2018, Int. J. Grid Util. Comput..

[20]  Faramarz Safi Esfahani,et al.  RePro-Active: a reactive–proactive scheduling method based on simulation in cloud computing , 2018, The Journal of Supercomputing.

[21]  Nima Jafari Navimipour,et al.  Task Scheduling in Cloud Computing Based on The Cuckoo Search Algorithm , 2015, Iraqi Journal of Computer, Communication, Control and System Engineering.

[22]  Faramarz Safi Esfahani,et al.  Knowledge-based adaptable scheduler for SaaS providers in cloud computing , 2015, Human-centric Computing and Information Sciences.

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

[24]  Sankalap Arora,et al.  Chaotic whale optimization algorithm , 2018, J. Comput. Des. Eng..

[25]  Indrajit N. Trivedi,et al.  A Novel Hybrid PSO–WOA Algorithm for Global Numerical Functions Optimization , 2018 .

[26]  Alan Oxley,et al.  Dynamic Multilevel Hybrid Scheduling Algorithms for Grid Computing , 2011, ICCS.

[27]  Fatma A. Omara,et al.  Task Scheduling Using PSO Algorithm in Cloud Computing Environments , 2015 .

[28]  Jeng-Shyang Pan,et al.  Interaction Artificial Bee Colony Based Load Balance Method in Cloud Computing , 2014, ICGEC.

[29]  Fatma A. Omara,et al.  Task Scheduling using Hybrid Algorithm in Cloud Computing Environments , 2015 .

[30]  Karnam Sreenu,et al.  W-Scheduler: whale optimization for task scheduling in cloud computing , 2017, Cluster Computing.

[31]  Hongying Huo,et al.  Improved PSO-based Task Scheduling Algorithm in Cloud Computing , 2012 .

[32]  Saloni Jain,et al.  Efficient Optimal Algorithm of Task Scheduling in Cloud Computing Environment , 2014, ArXiv.

[33]  Yongquan Zhou,et al.  Lévy Flight Trajectory-Based Whale Optimization Algorithm for Global Optimization , 2017, IEEE Access.

[34]  Majdi M. Mafarja,et al.  Hybrid Whale Optimization Algorithm with simulated annealing for feature selection , 2017, Neurocomputing.

[35]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization , 2014 .

[36]  Faramarz Safi-Esfahani,et al.  CRFF.GP: cloud runtime formulation framework based on genetic programming , 2019, The Journal of Supercomputing.

[37]  Sriyankar Acharyya,et al.  Optimal task scheduling in cloud computing environment: Meta heuristic approaches , 2015, 2015 2nd International Conference on Electrical Information and Communication Technologies (EICT).

[38]  Mansoor Alam,et al.  Cloudlet Scheduling with Particle Swarm Optimization , 2015, 2015 Fifth International Conference on Communication Systems and Network Technologies.

[39]  M. Aramudhan,et al.  Trust Based Resource Selection in Cloud Computing Using Hybrid Algorithm , 2015 .

[40]  Indrajit N. Trivedi,et al.  Novel Adaptive Whale Optimization Algorithm for Global Optimization , 2016 .

[41]  MOHAMMAD H. NADIMI-SHAHRAKI,et al.  EFFICIENT LOAD BALANCING USING ANT COLONY OPTIMIZATION , 2015 .

[42]  Nima Jafari Navimipour,et al.  Multi-Objective Task Scheduling in Cloud Computing Using an Imperialist Competitive Algorithm , 2016 .

[43]  Dan Wang,et al.  Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization , 2011, 2011 Sixth Annual Chinagrid Conference.

[44]  Faramarz Safi Esfahani,et al.  Scientific Workflow Scheduling Based on Deadline Constraints in Cloud Environment , 2015 .