Background & Objective:
Cloud computing emerges out as a new way of computing
which enables the users to fulfill their computation need using the underlying computing resources
like software, memory, computing nodes or machines without owning them purely on the basis of
pay-per-use that too round the clock and from anywhere. People defined this as the extension of the
existing technologies like parallel computing, distributed computing or grid computing. Lots of research
have been conducted in the field of cloud computing but the task scheduling is considered to
be the most fundamental problem which is still in infancy and requires a lot of attention and a proper
mechanism for the optimal utilization of the underlying computing resources. Task scheduling in
cloud computing environment lies into the category of NP-hard problem and many heuristics and
Meta heuristics strategies have been applied to solve the problem.
Methods:
In this work, Fuzzy Enabled Genetic Algorithm (FEGA) is proposed to solve the problem
of task scheduling in cloud computing environment as classical roulette wheel selection method has
certain limitations to solve complex optimization problem.
Results & Discussion:
In this work, an efficient fuzzy enabled genetic algorithm based task scheduling
mechanism has been designed, implemented and investigated. The efficiency of the proposed
FEGA algorithm is tested using various randomly generated data sets in different situations and
compared with the other meta-heuristics.
Conclusion:
The authors suggest that the proposed Fuzzy Enabled Genetic Algorithm (FEGA) to
solve the task scheduling problem helps in minimizing the total execution time or makespan and on
comparing with other Meta-heuristic like genetic algorithm and greedy based strategy found that
FEGA outperforms the both in different set of experiments.