Review on Multiobjective Task Scheduling in Cloud Computing using Nature Inspired Algorithms

In cloud computing huge pool of resources are available and shared through internet. The scheduling is a core technique which determines the performance of a cloud computing system. The goal of scheduling is to allocate task to appropriate machine to achieve one or more QOS. To find the suitable resource among pool of resources to achieve the goal is an NP Complete problem. A new class of algorithm called nature inspired algorithm came into existence to find optimal solution.  In this paper we provide a survey as well as a comparative analysis of various existing nature inspired scheduling algorithms which are based on genetic algorithm and ant colony optimization algorithm.

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

[2]  Sakshi Kaushal,et al.  Budget constrained priority based genetic algorithm for workflow scheduling in cloud , 2013, ARTCom 2013.

[3]  Liu Kun-lun,et al.  Improved GEP Algorithm for Task Scheduling in Cloud Computing , 2014, 2014 Second International Conference on Advanced Cloud and Big Data.

[4]  Ning Zhang,et al.  SAACO: A Self Adaptive Ant Colony Optimization in Cloud Computing , 2015, 2015 IEEE Fifth International Conference on Big Data and Cloud Computing.

[5]  Guiyi Wei,et al.  GA-Based Task Scheduler for the Cloud Computing Systems , 2010, 2010 International Conference on Web Information Systems and Mining.

[6]  Zhi-hui Zhan,et al.  Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach , 2014, GECCO.

[7]  Tingting Wang,et al.  Load Balancing Task Scheduling Based on Genetic Algorithm in Cloud Computing , 2014, 2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing.

[8]  Nitin,et al.  Load Balancing of Nodes in Cloud Using Ant Colony Optimization , 2012, 2012 UKSim 14th International Conference on Computer Modelling and Simulation.

[9]  Manu Vardhan,et al.  Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint , 2016, IEEE Access.

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

[11]  Christine Morin,et al.  Energy-Aware Ant Colony Based Workload Placement in Clouds , 2011, 2011 IEEE/ACM 12th International Conference on Grid Computing.

[12]  Cheng-Ming Zou,et al.  A Task Scheduling Algorithm Based on Genetic Algorithm and Ant Colony Optimization in Cloud Computing , 2014, 2014 13th International Symposium on Distributed Computing and Applications to Business, Engineering and Science.

[13]  Fatos Xhafa,et al.  Computational models and heuristic methods for Grid scheduling problems , 2010, Future Gener. Comput. Syst..

[14]  Jiong Yu,et al.  Energy-Aware Genetic Algorithms for Task Scheduling in Cloud Computing , 2012, ChinaGrid.

[15]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[16]  Mala Kalra,et al.  Scheduling of Independent Tasks in Cloud Computing Using Modified Genetic Algorithm , 2014, 2014 International Conference on Computational Intelligence and Communication Networks.

[17]  Himansu Das,et al.  Energy aware scheduling using genetic algorithm in cloud data centers , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).

[18]  Wang Bei,et al.  Load balancing task scheduling based on Multi-Population Genetic Algorithm in cloud computing , 2016, 2016 35th Chinese Control Conference (CCC).

[19]  Reinaldo J. Moraga,et al.  Metaheuristics: A Solution Methodology for Optimization Problems , 2005 .

[20]  Mansi Bhonsle,et al.  A Study on Scheduling Methods in Cloud Computing , 2012 .

[21]  Barrie Sosinsky.  Defining Cloud Computing , 2011 .

[22]  Xin Lu,et al.  A load-adapative cloud resource scheduling model based on ant colony algorithm , 2011, 2011 IEEE International Conference on Cloud Computing and Intelligence Systems.

[23]  Ciprian Dobre,et al.  Genetic algorithm for DAG scheduling in Grid environments , 2009, 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing.

[24]  Yonghua Xiong,et al.  A Task Scheduling Method for Energy-Efficient Cloud Video Surveillance System Using a Time-Clustering-Based Genetic Algorithm , 2016, 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS).

[25]  Valentin Cristea,et al.  Reputation Guided Genetic Scheduling Algorithm for Independent Tasks in Inter-clouds Environments , 2013, 2013 27th International Conference on Advanced Information Networking and Applications Workshops.

[26]  Ning Zhang,et al.  PACO: A Period ACO Based Scheduling Algorithm in Cloud Computing , 2013, 2013 International Conference on Cloud Computing and Big Data.