Scheduling Scientific Workflow Applications with Deadline and Budget Constraints Using Genetic Algorithms

TLDR

Grid technologies have evolved into a service‑oriented paradigm that supports diverse computing services, enabling scientific applications such as bioinformatics and astronomy to leverage distributed resources for workflow processing while meeting QoS requirements. The paper aims to develop a genetic algorithm for optimizing the scheduling of workflow applications under deadline and budget constraints. The proposed method applies a genetic algorithm to solve the scheduling optimization problem in utility Grid environments.

Abstract

Grid technologies have progressed towards a service‐oriented paradigm that enables a new way of service provisioning based on utility computing models, which are capable of supporting diverse computing services. It facilitates scientific applications to take advantage of computing resources distributed world wide to enhance the capability and performance. Many scientific applications in areas such as bioinformatics and astronomy require workflow processing in which tasks are executed based on their control or data dependencies. Scheduling such interdependent tasks on utility Grid environments need to consider users′ QoS requirements. In this paper, we present a genetic algorithm approach to address scheduling optimization problems in workflow applications, based on two QoS constraints, deadline and budget.