الفهرس 
Chapter 1 IntroductionOnly 14 pages are availabe for public view
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Abstract
In this chapter, five approaches based on swarm intelligence techniques for
achieving cloud computing tasks scheduling has been proposed. MACOLB is
used to find the near-optimal resource allocation for batch tasks in the dynamic
cloud system and minimize the makespan of tasks. The proposed algorithm
uses the same self-adapting criteria for the MACO control parameters but has
an added load balancing factor. ACO, PSO and ABC for cloud task scheduling
algorithms model the behavior ant colony, the behavior of Particle swarm and
the behavior of honey bees respectively. Firstly the best values of parameters for each algorithm, experimentally determined. Then the algorithms in
applications with the number of tasks varying from 100 to 1000 evaluated.
Simulation results demonstrate that MACOLB algorithm outperforms the
compared algorithms. MACO, ABC, PSO and ACO algorithms achieves better
resource utilization and significantly outperforms FPLTF, random and FCFS
algorithms on the basis of makespan and degree of imbalance.