Intelligent cloud algorithms for load balancing problems: A survey

Cloud computing services are growing very fast especially with the high demand of mobile and online applications (Apps) and services. This exponential growth emphasis on the need of minimizing the makespan scheduling and utilizing the resources efficiently based on dynamic environment. Accordingly, many load balancing algorithms have been developed to overcome these issues using intelligent optimization methodologies, such as Genetic Algorithms (GA), Ant Colony optimization (ACO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). This paper surveys the above intelligent optimization techniques and focuses on the Ant Lion Optimizer (ALO) intelligent technique, also it proposes an implementation of ALO based cloud computing environment as efficient algorithm that expected to supplies better outcomes in load balancing.

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

[2]  Sanjeev Jain,et al.  An Analysis of Swarm Intelligence based Load Balancing Algorithms in a Cloud Computing Environment , 2015 .

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

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

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

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

[7]  Jianhua Gu,et al.  A New Resource Scheduling Strategy Based on Genetic Algorithm in Cloud Computing Environment , 2012, J. Comput..

[8]  Sugandha Sharma.  Research Paper on Optimized Utilization of Resources Using PSO and Improved Particle Swarm Optimization (IPSO) Algorithms in Cloud Computing , 2014 .

[9]  Hao Yuan,et al.  Optimal Virtual Machine Resources Scheduling Based on Improved Particle Swarm Optimization in Cloud Computing , 2014, J. Softw..

[10]  Farookh Khadeer Hussain,et al.  Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization , 2013, International Journal of Parallel Programming.

[11]  Gaochao Xu,et al.  A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem , 2013, TheScientificWorldJournal.

[12]  Mala Kalra,et al.  TASK SCHEDULING OPTIMIZATION OF INDEPENDENT TASKS IN CLOUD COMPUTING USING ENHANCED GENETIC ALGORITHM , 2014 .

[13]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

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