An Ant-Colony-Based Meta-Heuristic Approach for Load Balancing in Cloud Computing

This book chapter proposes use of Ant Colony Optimization (ACO), a novel computational intelligence technique for balancing loads of virtual machine in cloud computing. Computational intelligence(CI), includes study of designing bio-inspired artificial agents for finding out probable optimal solution. So the central goal of CI can be said as, basic understanding of the principal, which helps to mimic intelligent behavior from the nature for artifact systems. Basic strands of ACO is to design an intelligent multi-agent systems imputed by the collective behavior of ants. From the perspective of operation research, it’s a meta-heuristic. Cloud computing is a one of the emerging technology. It’s enables applications to run on virtualized resources over the distributed environment. Despite these still some problems need to be take care, which includes load balancing. The proposed algorithm tries to balance loads and optimize the response time by distributing dynamic workload in to the entire system evenly.

[1]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[2]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

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

[4]  Shu-Chin Wang,et al.  A Three-Phases Scheduling in a Hierarchical Cloud Computing Network , 2011, 2011 Third International Conference on Communications and Mobile Computing.

[5]  Christine W. Chan,et al.  An Artificial Intelligence-Based Vehicular System Simulator , 2017, Int. J. Softw. Sci. Comput. Intell..

[6]  Ravinder Singh,et al.  An innovative approach of Ant Colony optimization for load balancing in peer to peer grid environment , 2014, 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT).

[7]  Antonios Symvonis,et al.  Dimension-Exchange Algorithms for Load Balancing on Trees , 2002, SIROCCO.

[8]  Paramartha Dutta,et al.  Genetic algorithm and gravitational emulation based hybrid load balancing strategy in cloud computing , 2015, Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT).

[9]  Borja Sotomayor,et al.  Virtual Infrastructure Management in Private and Hybrid Clouds , 2009, IEEE Internet Computing.

[10]  Frédéric Vivien,et al.  Load-balancing scatter operations for grid computing , 2004, Parallel Comput..

[11]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[12]  G. Sahoo,et al.  Mathematical Model of Cloud Computing Framework Using Fuzzy Bee Colony Optimization Technique , 2009, 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies.

[13]  Saudi Arabia,et al.  A Guide to Dynamic Load Balancing in Distributed Computer Systems , 2010 .

[14]  Yahya Slimani,et al.  Task Load Balancing Strategy for Grid Computing , 2007 .

[15]  Mohsen Moradi,et al.  A new time optimizing probabilistic load balancing algorithm in grid computing , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[16]  Kousik Dasgupta,et al.  A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing , 2013 .

[17]  Rajkumar Buyya,et al.  CloudAnalyst: A CloudSim-Based Visual Modeller for Analysing Cloud Computing Environments and Applications , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[18]  Gaochao Xu,et al.  A Load Balancing Model Based on Cloud Partitioning for the Public Cloud , 2013 .

[19]  Yifan Hu,et al.  An optimal migration algorithm for dynamic load balancing , 1998 .

[20]  Martin Randles,et al.  Scalable Self-Governance Using Service Communities as Ambients , 2009, 2009 Congress on Services - I.

[21]  J. Deneubourg,et al.  Trails and U-turns in the Selection of a Path by the Ant Lasius niger , 1992 .

[22]  Laurence T. Yang,et al.  A routing load balancing policy for grid computing environments , 2006, 20th International Conference on Advanced Information Networking and Applications - Volume 1 (AINA'06).

[23]  B. Yagoubi,et al.  A load balancing model for grid environment , 2007, 2007 22nd international symposium on computer and information sciences.

[24]  Xinhuai Tang,et al.  A Load-Balance Based Resource-Scheduling Algorithm under Cloud Computing Environment , 2010, ICWL Workshops.

[25]  Mario Zagar,et al.  Analysis of issues with load balancing algorithms in hosted (cloud) environments , 2011, 2011 Proceedings of the 34th International Convention MIPRO.

[26]  Jun Liu,et al.  A Task Scheduling Based on Simulated Annealing Algorithm in Cloud Computing , 2016 .

[27]  eva Kühn,et al.  Chapter 8 Self-Organized Load Balancing through Swarm Intelligence , 2011, Next Generation Data Technologies for Collective Computational Intelligence.

[28]  Kousik Dasgupta,et al.  An Ant Colony Based Load Balancing Strategy in Cloud Computing , 2014 .

[29]  Xiaoli Wang,et al.  A PSO-Based Algorithm for Load Balancing in Virtual Machines of Cloud Computing Environment , 2012, ICSI.

[30]  Luis Rodero-Merino,et al.  A break in the clouds: towards a cloud definition , 2008, CCRV.

[31]  Brian Hayes,et al.  What Is Cloud Computing? , 2019, Cloud Technologies.

[32]  Kousik Dasgupta,et al.  Load Balancing in Cloud Computing using Stochastic Hill Climbing-A Soft Computing Approach , 2012 .

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