An Improved Multi-Objective Workflow Scheduling Using F-NSPSO with Fuzzy Rules

A lot of scientific problems in various domains from modelling sky as mosaics to understanding Genome sequencing in biological applications are modelled as workflows with a large number of interconnected tasks. Even though many works are cited in the literature on workflow scheduling, most of the existing works are focused on reducing the makespan alone. Moreover, energy efficiency is considered only in a few works included in the literature. Constraints about the dynamic workload allocation are not introduced in the existing systems. Moreover, the optimization techniques used in the existing systems have improved the QoS with little scalability in the cloud environment since they consider only the infrastructure as the service model. In this work, a new algorithm has been proposed based on the proposal of a new Multi-Objective Optimization model called F-NSPSO using NSPSO Meta-heuristics. This method allows the user to choose a suitable configuration dynamically. When compared to NSPSO an energy reduction of at least 10% has been observed for F-NSPSO for Montage, Cybershake, and Epigenomics workflow applications. Compared to the NSPSO algorithm F-NSPSO algorithm shows at least 13%, 12%, and 21% improvement in average makespan for Montage, Cybershake, and Epigenomics workflow applications respectively.