• 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