Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing

Cloud computing is increasing rapidly as a successful paradigm presenting on-demand infrastructure, platform, and software services to clients. Load balancing is one of the important issues in cloud computing to distribute the dynamic workload equally among all the nodes to avoid the status that some nodes are overloaded while others are underloaded. Many algorithms have been suggested to perform this task. Recently, worldview is turning into a new paradigm for optimization search by applying the osmosis theory from chemistry science to form osmotic computing. Osmotic computing is aimed to achieve balance in highly distributed environments. The main goal of this paper is to propose a hybrid metaheuristics technique which combines the osmotic behavior with bio-inspired load balancing algorithms. The osmotic behavior enables the automatic deployment of virtual machines (VMs) that are migrated through cloud infrastructures. Since the hybrid artificial bee colony and ant colony optimization proved its efficiency in the dynamic environment in cloud computing, the paper then exploits the advantages of these bio-inspired algorithms to form an osmotic hybrid artificial bee and ant colony (OH_BAC) optimization load balancing algorithm. It overcomes the drawbacks of the existing bio-inspired algorithms in achieving load balancing between physical machines. The simulation results show that OH_BAC decreases energy consumption, the number of VMs migrations and the number of shutdown hosts compared to existing algorithms. In addition, it enhances the quality of services (QoSs) which is measured by service level agreement violation (SLAV) and performance degradation due to migrations (PDMs).

[1]  Rawya Rizk,et al.  Honey Bee Based Load Balancing in Cloud Computing , 2017, KSII Trans. Internet Inf. Syst..

[2]  Changhe Li,et al.  A survey of swarm intelligence for dynamic optimization: Algorithms and applications , 2017, Swarm Evol. Comput..

[3]  S. Jayalekshmi,et al.  Cost effective load balancing based on honey bee behaviour in cloud environment , 2014, 2014 First International Conference on Computational Systems and Communications (ICCSC).

[4]  L. D. Dhinesh Babu,et al.  Honey bee behavior inspired load balancing of tasks in cloud computing environments , 2013, Appl. Soft Comput..

[5]  Ahmad Patooghy,et al.  Bee-MMT: A load balancing method for power consumption management in cloud computing , 2013, 2013 Sixth International Conference on Contemporary Computing (IC3).

[6]  Munam Ali Shah,et al.  Load balancing algorithms in cloud computing: A survey of modern techniques , 2015, 2015 National Software Engineering Conference (NSEC).

[7]  Adel Nadjaran Toosi,et al.  Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications , 2012, Artificial Intelligence Review.

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

[9]  Rawya Rizk,et al.  Bio-inspired Load Balancing Algorithm in Cloud Computing , 2017, AISI.

[10]  Shabnam Sharma,et al.  An Optimal Load Balancing Technique for Cloud Computing Environment using Bat Algorithm , 2016 .

[11]  Chang-Dong Wang,et al.  An ACO-based Scheduling Strategy on Load Balancing in Cloud Computing Environment , 2015, 2015 Ninth International Conference on Frontier of Computer Science and Technology.

[12]  Christian Blum,et al.  Hybrid metaheuristics in combinatorial optimization: A survey , 2011, Appl. Soft Comput..

[13]  Aneena Ann Alexander,et al.  An Efficient Resource Management for Prioritized Users in Cloud Environment Using Cuckoo Search Algorithm , 2016 .

[14]  Rajiv Ranjan,et al.  Osmotic Computing: A New Paradigm for Edge/Cloud Integration , 2016, IEEE Cloud Computing.

[15]  P. Venkata Krishna,et al.  Bio-inspired algorithms for cloud computing: a review , 2015 .

[16]  Atef M. Ghuniem,et al.  LBSR: Load Balance Over Slow Resources , 2018, 2018 1st International Conference on Computer Applications & Information Security (ICCAIS).

[17]  Inderveer Chana,et al.  Energy-aware Virtual Machine Migration for Cloud Computing - A Firefly Optimization Approach , 2016, Journal of Grid Computing.

[18]  Ahmed Shawish,et al.  Cloud Computing: Paradigms and Technologies , 2014 .

[19]  S. Siva Sathya,et al.  A Survey of Bio inspired Optimization Algorithms , 2012 .

[20]  Xing Xu,et al.  Cloud Task and Virtual Machine Allocation Strategy in Cloud Computing Environment , 2012 .

[21]  Rashedur M. Rahman,et al.  Implementation and performance analysis of various VM placement strategies in CloudSim , 2015, Journal of Cloud Computing.

[22]  Rachhpal Singh.  Cuckoo Genetic Optimization Algorithm for Efficient Job Scheduling with Load Balance in Grid Computing , 2016 .

[23]  Amritpal Kaur,et al.  Bio Inspired Algorithms: An Efficient Approach for Resource Scheduling in Cloud Computing , 2015 .

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

[25]  Rawya Rizk,et al.  Smart elastic scheduling algorithm for virtual machine migration in cloud computing , 2019, The Journal of Supercomputing.

[26]  Uday Kumar Chakraborty,et al.  Genetic and evolutionary computing , 2008, Inf. Sci..

[27]  Maria Fazio,et al.  Towards Osmotic Computing: Looking at Basic Principles and Technologies , 2017, CISIS.

[28]  S. Sowmya Kamath,et al.  An hybrid bio-inspired task scheduling algorithm in cloud environment , 2014, Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT).