Bio-inspired Load Balancing Algorithm in Cloud Computing

Cloud computing is a widespread computing concepts which access a huge amount of data that can be used by more clients. Therefore, load balancing between resources is an important field for scheduling tasks to achieve better performance. In this paper, a Hybrid artificial Bee and Ant Colony optimization (H_BAC) load balancing algorithm is proposed. It depends on joining the important behavior of Ant Colony Optimization (ACO) such as discovering good solutions rapidly and Artificial Bee Colony (ABC) Algorithm such as collective interaction of bees and sharing information by waggle dancing. The experimental results show that H_BAC improves execution time, response time, makespan, resource utilization and standard deviation. This improvement reaches about 40% in the execution time and response time and 30% in the makespan over the other algorithms.

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

[2]  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).

[3]  B. Kruekaew,et al.  Virtual Machine Scheduling Management on Cloud Computing Using Artificial Bee Colony , 2014 .

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

[5]  Antony Selvadoss Thanamani,et al.  Scheduling in High Performance Computing Environment using Firefly Algorithm and Intelligent Water Drop Algorithms , 2014 .

[6]  Judith Kelner,et al.  High availability in clouds: systematic review and research challenges , 2016, Journal of Cloud Computing.

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

[8]  Rui Lu Fast methods for designing circulant network topology with high connectivity and survivability , 2016, Journal of Cloud Computing.

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

[10]  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).

[11]  Abhijit Patil,et al.  Dynamic Load Balancing in Cloud Computing using Swarm Intelligence Algorithms , 2015 .

[12]  Cristian Mateos,et al.  Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006) , 2015, Adv. Eng. Softw..

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