Distributed scheduling method for multiple workflows with parallelism prediction and DAG prioritizing for time constrained cloud applications

Abstract

Fog computing is an emerging popular paradigm that extends the availability of resources to the network's edge in order to improve the quality metrics of existing Cloud-based applications. However, scheduling workflow applications with time-constraints are complex regarding the count of resources, physical topology of clusters, and the structure of the task graph of the workflows. Adding Fog resources to the intricate problem space of Cloud-based scheduling needs even more time-consuming and complicated algorithms. In this paper, a multi-criteria Mamdani fuzzy algorithm is proposed to analyze the workflow graphs with the assistance of a Long-Short Term Memory neural network parallelism prediction module. The group-based priority assignment schema performed by the fuzzy inference system assigns a priority value to workflows to indicate the relative precedence of requests. Distributed schedulers then send the workflows to target sites according to their current workloads. The whole process is performed in a decentralized manner to prevent any bottlenecks. We have used an extensive software simulation study to compare the proposed algorithm in real workloads with two recent and notable algorithms. The simulation results confirm the proposed algorithm's superiority in fulfilling time-constraints, resource utilization, and overall application scheduling success rate.