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A Profit-aware Allocation of High Performance Computing Applications on Distributed Cloud Data Centers with Environmental Considerations
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1384-5323
University of Tehran, Tehran, Iran.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-6132-7945
2014 (English)In: CSI Journal on Computer Science and Engineering JCSE, Vol. 2, no 1, 28-38 p.Article in journal (Refereed) Published
Abstract [en]

A Set of Geographically Distributed Cloud data centers (SGDC) is a promising platform to run a large number of High Performance Computing Applications (HPCAs) in a cost-efficient manner. Energy consumption is a key factor affecting the profit of a cloud provider. In a SGDC, as the data centers are located in different corners of the world, the cost of energy consumption and the amount of CO2 emission significantly vary among the data centers. Therefore, in such systems not only a proper allocation of HPCAs results in CO2 emission reduction, but it also causes a substantial increase of the provider's profit. Furthermore, CO2 emission reduction mitigates the destructive environmental impacts. In this paper, the problem of allocation of a set of HPCAs on a SGDC is discussed where a two-level allocation framework is introduced to deal with the problem. The proposed framework is able to reach a good compromise between CO2 emission and the providers' profit subject to satisfy HPCAs deadlines and memory constraints. Simulation results based on a real intensive workload demonstrate that the proposed framework enhances the CO2 emission by 17% and the provider's profit by 9% in average.

Place, publisher, year, edition, pages
2014. Vol. 2, no 1, 28-38 p.
Keyword [en]
Cloud Computing, Data Center, Energy-aware allocation, CO2 emission, Multi-objective optimization, Live migration.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-35488OAI: oai:DiVA.org:mdh-35488DiVA: diva2:1104172
Projects
PREMISE - Predictable Multicore Systems
Available from: 2017-05-31 Created: 2017-05-31 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: 2017-09-29Bibliographically approved

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http://www.jcse.ir/Contents/vol10no24/4.pdf

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Faragardi, Hamid RezaNolte, thomas

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