A krill herd behaviour inspired load balancing of tasks in cloud computing

• Main Authors: Hasan, Raed Abdulkareem, Mohammed, Muamer N.
• Format: Article
• Language: English
• Published: ICI Bucharest 2017
• Subjects: T Technology (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/22386/1/SIC_2017-4-Art.5.pdf; http://umpir.ump.edu.my/id/eprint/22386/; https://sic.ici.ro/a-krill-herd-behaviour-inspired-load-balancing-of-tasks-in-cloud-computing/; https://doi.org/10.24846/v26i4y201705
• Institution: Universiti Malaysia Pahang

A developing trend in the IT environment is mobile cloud computing (MCC) with colossal infrastructural and resource requirements. In the cloud computing environment, load balancing – a way of distributing workloads across numerous computing resources, is a vital aspect. A proficient load balancing guarantees an effective resource usage through the supply of network resources based on the user demands. It can also organize the network clients using the fitting planning criteria. This paper sets forth an advanced load balancing and energy/cost aware technique for a demand-based network resource allocation in cloud computing. The load balancing process in the proposed strategy utilizes a Krill load balancer (Krill LB) which is expected to achieve a well-balanced load over virtual machines. The aim of using the Krill LB as the load balancer is to increase the throughput of the network as much as possible. The speed, task cost, and weight of the tasks were first determined, after which, the Krill herd optimization algorithm was for the load balancing based on the measured parameters. Furthermore, a modified dynamic energy-aware cloudlet-based mobile cloud computing model (MDECM) was introduced for energy cost awareness in load balancing based on the service rate and energy of the mobile users. The proposed work was aimed at optimizing resource allocation in MCC in an energy-efficient manner. The performance of the suggested Krill-LB was benchmarked against that of Honey Bee Behavior Load Balancing (HBB-LB), Kill Herd, and Round Robin algorithms.