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Towards Energy-Aware Resource Scheduling to Maximize Reliability in Cloud Computing Systems
Mälardalen University, School of Innovation, Design and Engineering. (IS (Embedded Systems))ORCID iD: 0000-0002-1384-5323
University of Tehran, Tehran, Iran.
University of Tehran, Tehran, Iran.
Mälardalen University, School of Innovation, Design and Engineering. (IS (Embedded Systems))ORCID iD: 0000-0001-6132-7945
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing has become increasingly popular due to deployment of cloud solutions that will enable enterprises to cost reduction and more operational flexibility. Reliability is a key metric for assessing performance in such systems. Fault tolerance methods are extensively used to enhance reliability in Cloud Computing Systems (CCS). However, these methods impose extra hardware and/or software cost. Proper resource allocation is an alternative approach which can significantly improve system reliability without any extra overhead. On the other hand, contemplating reliability irrespective of energy consumption and Quality of Service (QoS) requirements is not desirable in CCSs. In this paper, an analytical model to analyze system reliability besides energy consumption and QoS requirements is introduced. Based on the proposed model a new online resource allocation algorithm to find the right compromise between system reliability and energy consumption while satisfies QoS requirement is suggested. The algorithm is a new swarm intelligence technique based on imperialist competition which elaborately combines the strengths of some well-known meta-heuristic algorithms with an effective fast local search. A wide range of simulation results, based on real data clearly demonstrate high efficiency of the proposed algorithm.

Place, publisher, year, edition, pages
2013.
Keywords [en]
cloud computing, reliability, analytical model, resource allocation, quality of service, energy-aware scheduling
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-23562OAI: oai:DiVA.org:mdh-23562DiVA, id: diva2:679782
Conference
5th IEEE International Conference on High Performance Computing and Communications,(HPCC 2013),Zhangjiajie, China, November 13-15, 2013
Projects
PREMISE - Predictable Multicore SystemsAUTOSAR for Multi-Core in Automotive and Automation IndustriesAvailable from: 2013-12-16 Created: 2013-12-16 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 Sciences
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: 2018-01-13Bibliographically approved

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Faragardi, Hamid RezaNolte, Thomas

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