A New Conception of Load Balancing in Cloud Computing Using Tasks Classification Levels

Fahim Youssef, Ben Lahmar El Habib, Rahhali Hamza, Labriji El Houssine, Eddaoui Ahmed and Mostafa Hanoune
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Fahim Youssef: Hassan II University, Casablanca, Morocco
Ben Lahmar El Habib: Hassan II University, Casablanca, Morocco
Rahhali Hamza: Hassan II University, Casablanca, Morocco
Labriji El Houssine: Hassan II University, Casablanca, Morocco
Eddaoui Ahmed: Shaqra University, Shaqraa, Saudi Arabia
Mostafa Hanoune: Hassan II University, Casablanca, Morocco

International Journal of Cloud Applications and Computing (IJCAC), 2018, vol. 8, issue 4, 118-133

Abstract: Cloud users can have access to the service based on “pay as you go.” The daily increase of cloud users may decrease the performance, the availability and the profitability of the material and software resources used in cloud service. These challenges were solved by several load balancing algorithms between the virtual machines of the data centers. In order to determine a new load balancing improvement; this article's discussions will be divided into two research axes. The first, the pre-classification of tasks depending on whether their characteristics are accomplished or not (Notion of Levels). This new technique relies on the modeling of tasks classification based on an ascending order using techniques that calculate the worst-case execution time (WCET). The second, the authors choose distributed datacenters between quasi-similar virtual machines and the modeling of relationship between virtual machines using the pre-scheduling levels is included in the data center in terms of standard mathematical functions that controls this relationship. The key point of the improvement, is considering the current load of the virtual machine of a data center and the pre-estimation of the execution time of a task before any allocation. This contribution allows cloud service providers to improve the performance, availability and maximize the use of virtual machines workload in their data centers.

Date: 2018
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