A Fuzzy Logic Controller for Tasks Scheduling Using Unreliable Cloud Resources

TL;DR

This paper introduces a fuzzy logic-based dynamic scheduling algorithm for scientific workflows in cloud environments that optimally balances cost and makespan despite resource unreliability.

Contribution

It presents a novel Uncertainty-Driven Scheduling algorithm utilizing fuzzy logic to manage unreliable cloud resources effectively.

Findings

The proposed algorithm reduces workflow makespan and costs.

Fuzzy logic effectively handles resource uncertainty in scheduling.

Numerical results demonstrate improved performance over existing methods.

Abstract

The Cloud infrastructure offers to end users a broad set of heterogenous computational resources using the pay-as-you-go model. These virtualized resources can be provisioned using different pricing models like the unreliable model where resources are provided at a fraction of the cost but with no guarantee for an uninterrupted processing. However, the enormous gamut of opportunities comes with a great caveat as resource management and scheduling decisions are increasingly complicated. Moreover, the presented uncertainty in optimally selecting resources has also a negatively impact on the quality of solutions delivered by scheduling algorithms. In this paper, we present a dynamic scheduling algorithm (i.e., the Uncertainty-Driven Scheduling - UDS algorithm) for the management of scientific workflows in Cloud. Our model minimizes both the makespan and the monetary cost by dynamically selecting reliable or unreliable virtualized resources. For covering the uncertainty in decision making, we adopt a Fuzzy Logic Controller (FLC) to derive the pricing model of the resources that will host every task. We evaluate the performance of the proposed algorithm using real workflow applications being tested under the assumption of different probabilities regarding the revocation of unreliable resources. Numerical results depict the performance of the proposed approach and a comparative assessment reveals the position of the paper in the relevant literature.