Abstract
The on-demand provisioning and resource availability in cloud computing make it ideal for executing scientific workflow applications. An application can start execution with a minimum number of resources and allocate further resources when required. However, workflow scheduling is an NP hard problem and therefore meta-heuristics based solutions have been widely explored for the same. This paper presents an augmented Shuffled Frog Leaping Algorithm (ASFLA) based technique for resource provisioning and workflow scheduling in the Infrastructure as a service (IaaS) cloud environment. The performance of the ASFLA has been compared with the state of art PSO and SFLA algorithms. The efficacy of ASFLA has been assessed over some well-known scientific workflows of varied sizes using a custom Java based simulator. The simulation results show a marked improvement in the performance criteria of achieving minimum execution cost and meeting the schedule deadlines.
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