Cloud computing is the latest
distributed computing paradigm and it offers tremendous opportunities to solve
large scale scientific problems. However, it presents various challenges that
need to be addressed in order to be efficiently utilized for workflow
applications. Although the workflow scheduling problem has been widely studied,
there are very few initiatives tailored for Cloud environments. Furthermore,
the existing works fail to either meet the user’s Quality of Service (QoS)
requirements or to incorporate some basic principles of Cloud computing such as
the elasticity and heterogeneity of the computing resources. This paper
proposes a resource provisioning and scheduling strategy for scientific
workflows on Infrastructure as a Service (IaaS) Clouds. We present an algorithm
based on the meta-heuristic optimization technique, Particle Swarm Optimization
(PSO), which aims to minimize the overall workflow execution cost while meeting
deadline constraints. Our heuristic is evaluated using Cloud Sim and various
well-known scientific workflows of different sizes. The results show that our
approach performs better than the current state-of-the-art algorithms.
No comments:
Post a Comment