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Writing a Distributed Computing Application in 7 Minutes with Tasklets

Published: 12 December 2016 Publication History

Abstract

This demo paper introduces a middleware for distributed computation applications -- the Tasklet system. The Tasklet system allows developers to execute self-contained units of computation -- the so-called Tasklets -- in a pool of heterogeneous computing devices, including desktop computers, cloud resources, mobile devices, and graphical processing units. In this demonstration of the Tasklet system, we visualize the otherwise transparent process of computation offloading, starting from the development of an application until the actual distributed execution of tasks. While existing systems have high setup costs the Tasklet system emphasizes the ease of use and a seamless integration of various heterogeneous devices.
In the demonstration, we focus on three key benefits of the Tasklet system. First, we demonstrate the usability of the system by live developing a distributed computing application in less than ten minutes. Second, we show how heterogeneous devices can be set up and join the resource pool during the execution of Tasklets. With a monitoring tool we visualize how the computational workload is split up among these resources. Third, we introduce the concept of quality of computation to tailor the otherwise generic computing framework to the requirements of individual applications.

References

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D. P. Anderson. Boinc: A system for public-resource computing and storage. In Proc. of the Fifth IEEE/ACM Int. Workshop on Grid Computing, pages 4--10. IEEE, 2004.
[2]
B. Branner. The mandelbrot set. In Proc. of Symp. in Applied Mathematics, volume 39, pages 75--105, 1989.
[3]
M. J. Litzkow, M. Livny, and M. W. Mutka. Condor-a hunter of idle workstations. In 8th Int. Conf. on Distributed Computing Systems, pages 104--111. IEEE, 1988.
[4]
D. Schafer, J. Edinger, J. M. Paluska, S. VanSyckel, and C. Becker. Tasklets: "better than best-effort" computing. In 2016 25th International Conference on Computer Communication and Networks (ICCCN), pages 1--11, Aug 2016.

Cited By

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  • (2021)Voltaire: Precise Energy-Aware Code Offloading Decisions with Machine Learning2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PERCOM50583.2021.9439121(1-10)Online publication date: 22-Mar-2021
  • (2017)Developing distributed computing applications with Tasklets2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)10.1109/PERCOMW.2017.7917534(94-96)Online publication date: Mar-2017

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Published In

cover image ACM Conferences
Middleware Posters and Demos '16: Proceedings of the Posters and Demos Session of the 17th International Middleware Conference
December 2016
39 pages
ISBN:9781450346665
DOI:10.1145/3007592
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 December 2016

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  • Demonstration
  • Research
  • Refereed limited

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Middleware '16
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  • USENIX Assoc

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Cited By

View all
  • (2021)Voltaire: Precise Energy-Aware Code Offloading Decisions with Machine Learning2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PERCOM50583.2021.9439121(1-10)Online publication date: 22-Mar-2021
  • (2017)Developing distributed computing applications with Tasklets2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)10.1109/PERCOMW.2017.7917534(94-96)Online publication date: Mar-2017

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