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A profiling based task scheduling approach for multicore network processors

Tang, Feilong, You, Ilsun, Tang, Can and Yu, Sui 2015, A profiling based task scheduling approach for multicore network processors, Concurrency computation : practice and experience, vol. 27, no. 4, Special issue paper, pp. 855-869.

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Title A profiling based task scheduling approach for multicore network processors
Author(s) Tang, Feilong
You, Ilsun
Tang, Can
Yu, Sui
Journal name Concurrency computation : practice and experience
Volume number 27
Issue number 4
Season Special issue paper
Start page 855
End page 869
Total pages 15
Publisher John Wiley & Sons
Place of publication London, England
Publication date 2015-03
ISSN 1532-0626
Keyword(s) computational process
distributed computing
multicore processor
task scheduling
Summary  Multicore network processors have been playing an increasingly important role in computational processes, which emphasize on scalability and parallelism of the systems, in distributed environments especially in Internet-based delay-sensitive applications. It is an important but unsolved issue, however, to efficiently schedule tasks in network processors with multicore and multithread for improving the system throughput as much as possible. Profiling can gather runtime environment information and guide the compiler to optimize programs through scheduling tasks based on the runtime context. This paper proposes a profiling-based task scheduling approach, targeting on improving the throughput of multicore network processor (Intel IXP) systems in the balanced pipeline way. In this work, we investigate a profiling-based task scheduling framework, a task scheduling algorithm, and a set of performance models. Our task allocation scheme maps tasks onto the pipeline architecture and multiple threads of network processors in parallel, which incorporates the profiling context and global thread refinement. We evaluate our task scheduling algorithm by implementing representative network applications on the Intel IXP network processor. Experimental results demonstrate that our algorithm is able to schedule tasks in a balanced pipeline fashion and achieve the high throughput and data transmission rate. Copyright © 2012 John Wiley & Sons, Ltd.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, John Wiley & Sons
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Document type: Journal Article
Collection: School of Information Technology
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