DocumentCode
3740321
Title
Intelligent cloud algorithms for load balancing problems: A survey
Author
Aya A. Salah Farrag;Safia Abbas Mahmoud;El Sayed M. El-Horbaty
Author_Institution
Faculty of Computer and Information Sciences - Ain Shams University, Cairo, Egypt
fYear
2015
Firstpage
210
Lastpage
216
Abstract
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.
Keywords
"Dynamic scheduling","Quality of service","Mathematical model"
Publisher
ieee
Conference_Titel
Intelligent Computing and Information Systems (ICICIS), 2015 IEEE Seventh International Conference on
Print_ISBN
978-1-5090-1949-6
Type
conf
DOI
10.1109/IntelCIS.2015.7397223
Filename
7397223
Link To Document