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Adaptive Scheduling for Real-Time Distributed Systems

Adaptive Scheduling for Real-Time Distributed Systems

Apurva Shah
Copyright: © 2014 |Pages: 13
ISBN13: 9781466660786|ISBN10: 1466660783|EISBN13: 9781466660793
DOI: 10.4018/978-1-4666-6078-6.ch011
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MLA

Shah, Apurva. "Adaptive Scheduling for Real-Time Distributed Systems." Biologically-Inspired Techniques for Knowledge Discovery and Data Mining, edited by Shafiq Alam, et al., IGI Global, 2014, pp. 236-248. https://doi.org/10.4018/978-1-4666-6078-6.ch011

APA

Shah, A. (2014). Adaptive Scheduling for Real-Time Distributed Systems. In S. Alam, G. Dobbie, Y. Koh, & S. ur Rehman (Eds.), Biologically-Inspired Techniques for Knowledge Discovery and Data Mining (pp. 236-248). IGI Global. https://doi.org/10.4018/978-1-4666-6078-6.ch011

Chicago

Shah, Apurva. "Adaptive Scheduling for Real-Time Distributed Systems." In Biologically-Inspired Techniques for Knowledge Discovery and Data Mining, edited by Shafiq Alam, et al., 236-248. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-6078-6.ch011

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Abstract

Biologically inspired data mining techniques have been intensively used in different data mining applications. Ant Colony Optimization (ACO) has been applied for scheduling real-time distributed systems in the recent time. Real-time processing requires both parallel activities and fast response. It is required to complete the work and deliver services on a timely basis. In the presence of timing, a real-time system's performance does not always improve as processor and speed increases. ACO performs quite well for scheduling real-time distributed systems during overloaded conditions. Earliest Deadline First (EDF) is the optimal scheduling algorithm for single processor real-time systems during under-loaded conditions. This chapter proposes an adaptive algorithm that takes advantage of EDF- and ACO-based algorithms and overcomes their limitations.

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