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An Efficient Scheduling of HPC Applications on Geographically Distributed Cloud Data Centers
University of Tehran, Tehran, Iran.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1384-5323
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-6132-7945
2013 (English)In: Computer Networks and Distributed Systems: International Symposium, CNDS 2013, Tehran, Iran, December 25-26, 2013, Revised Selected Papers, Springer, 2013, 155-167 p.Chapter in book (Refereed)
Abstract [en]

Cloud computing provides a flexible infrastructure for IT industries to run their High Performance Computing (HPC) applications. Cloud providers deliver such computing infrastructures through a set of data centers called a cloud federation. The data centers of a cloud federation are usually distributed over the world. The profit of cloud providers strongly depends on the cost of energy consumption. As the data centers are located in various corners of the world, the cost of energy consumption and the amount of CO2 emission in dif-ferent data centers varies significantly. Therefore, a proper allocation of HPC applications in such systems can result in a decrease of CO2 emission and a substantial increase of the providers’ profit. Reduction of CO2 emission also mitigates the destructive environmental impacts. In this paper, the problem of scheduling HPC applications on a geographically distributed cloud federation is scrutinized. To address the problem, we propose a two-level scheduler which is able to reach a good compromise between CO2 emission and the profit of cloud provider. The scheduler should also satisfy all HPC applications’ deadline and memory constraints. Simulation results based on a real intensive workload indi-cate that the proposed scheduler reduces the CO2 emission by 11% while at the same time it improves the provider’s profit in average.

Place, publisher, year, edition, pages
Springer, 2013. 155-167 p.
Series
Communications in Computer and Information Science, 428
Keyword [en]
Cloud Computing, Data Center, Energy-aware scheduling, CO2 emission, Multi-objective optimization
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-25130DOI: 10.1007/978-3-319-10903-9_13ISI: 000347888900013Scopus ID: 2-s2.0-84908543721Local ID: 978-3-319-10903-9ISBN: 978-3-319-10902-2 (print)OAI: oai:DiVA.org:mdh-25130DiVA: diva2:722824
Conference
International Symposium on Computer Networks and Distributed Systems CNDS'13, 25 Dec 2013, Tehran, Iran
Available from: 2014-06-09 Created: 2014-06-05 Last updated: 2017-09-18Bibliographically approved
In thesis
1. Resource Optimization in Multi-processor Real-time Systems
Open this publication in new window or tab >>Resource Optimization in Multi-processor Real-time Systems
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis addresses the topic of resource efficiency in multiprocessor systems in the presence of timing constraints. 

 Nowadays, almost wherever you look, you find a computing system. Most computing systems employ a multiprocessor platform. Multiprocessor systems can be found in a broad spectrum of computing systems ranging from a tiny chip hosting multiple cores to large geographically-distributed cloud data centers connected by the Internet. In multiprocessor systems, efficient use of computing resources is a substantial element when it comes to achieving a desirable performance for running software applications. 

 Most industrial applications, e.g., automotive and avionics applications, are subject to a set of real-time constraints that must be met. Such kinds of applications, along with the underlying hardware and software components running the application, constitute a real-time system. In real-time systems, the first and major concern of the system designer is to provide a solution where all timing constraints are met. Therefore, in multiprocessor real-time systems, not only resource efficiency, but also meeting all the timing requirements, is a major concern. 

 Industrie 4.0 is the current trend in automation and manufacturing when it comes to creating next generation of smart factories. Two categories of multiprocessor systems play a significant role in the realization of such a smart factory: 1) multi-core processors which are the key computing element of embedded systems, 2) cloud computing data centers as the supplier of a massive data storage and a large computational power. Both these categories are considered in the thesis, i.e., 1) the efficient use of embedded multi-core processors where multiple processors are located on the same chip, applied to execute a real-time application, and 2) the efficient use of multi-processors within a cloud computing data center. We address these two categories of multi-processor systems separately. 

 For each of them, we identify the key challenges to achieve a resource-efficient design of the system. We then formulate the problem and propose optimization solutions to optimize the efficiency of the system, while satisfying all timing constraints. Introducing a resource efficient solution for those two categories of multi-processor systems facilitates deployment of Industrie 4.0 in smart manufacturing factories where multi-core embedded processors and cloud computing data centers are two central cornerstones.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2017
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 263
National Category
Computer Science
Identifiers
urn:nbn:se:mdh:diva-35387 (URN)978-91-7485-336-0 (ISBN)
Presentation
2017-10-05, Paros, Mälardalens högskola, Västerås, 13:30 (English)
Opponent
Supervisors
Available from: 2017-09-14 Created: 2017-05-24 Last updated: 2017-09-18Bibliographically approved

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