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Performance analysis of scheduling algorithms for dynamic workflow applications
In recent years, Big Data has changed how we do computing. Even though we have large scale infrastructure such as Cloud computing and several platforms such as Hadoop available to process the workloads, with Big Data there is a high level of uncertainty that has been introduced in how an application processes the data. Data in general comes in different formats, at different speed and at different volume. Processing consists of not just one application but several applications combined to form a workflow to achieve a certain goal. With data variation and at different speed, applications, execution and resource needs will also vary at runtime. These are called dynamic workflows. One can say that we can just throw more and more resources during runtime. However this is not an effective way as it can lead to, in the best case, resource wastage or monetary loss and in the worst case, delivery of outcomes much later than when it is required. Thus, scheduling algorithms play an important role in efficient execution of dynamic workflow applications. In this paper, we evaluate several most commonly used workflow scheduling algorithms to understand which algorithm will be the best for the efficient execution of dynamic workflows.
Funding
University of Tasmania
History
Publication title
Proceedings of the 2015 IEEE International Congress on Big DataEditors
C Barbara, L KhanPagination
222-229ISBN
978-1-4673-7277-0Department/School
School of Information and Communication TechnologyPublisher
Institute of Electrical and Electronics Engineers, Inc.Place of publication
Los Alamitos, CaliforniaEvent title
2015 IEEE International Congress on Big DataEvent Venue
New York New YorkDate of Event (Start Date)
2015-06-27Date of Event (End Date)
2015-07-02Rights statement
Copyright 2015 IEEERepository Status
- Restricted