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Towards Energy-Aware Placement of Real-Time Virtual Machines in a Cloud Data Center
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-2670-3022
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.ORCID iD: 0000-0001-6132-7945
2015 (English)In: Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015, 2015, 1657-1662 p.Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing is an evolving paradigm which is becoming an adoptable technology for a variety of applications. However, cloud infrastructures must be able to fulfill application requirements before adopting cloud solutions. Cloud infrastructure providers communicate the characteristics of their services to their customers through Service Level Agreements (SLA). In order for a real-time application to be able to use cloud technology, cloud infrastructure providers have to be able to provide timing guarantees in the SLAs. In this paper, we present our ongoing work regarding a cloud solution in which periodic tasks are provided as a service in the Software as a Service (SaS) model. Tasks belonging to a certain application are mapped in a Virtual Machine (VM). We also study the problem of VMplacement on a cloud infrastructure. We propose a placement mechanism which minimizes the energy consumption of the data center by consolidating VMs in a minimum number of servers while respecting the timing requirement of virtual machines.

Place, publisher, year, edition, pages
2015. 1657-1662 p.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-29238DOI: 10.1109/HPCC-CSS-ICESS.2015.22ISI: 000380408100272Scopus ID: 2-s2.0-84949578819ISBN: 9781479989362 (print)OAI: oai:DiVA.org:mdh-29238DiVA: diva2:859242
Conference
17th IEEE International Conference on High Performance Computing and Communications, IEEE 7th International Symposium on Cyberspace Safety and Security and IEEE 12th International Conference on Embedded Software and Systems, HPCC-ICESS-CSS 2015; New York; United States; 24 August 2015 through 26 August 2015
Projects
ARROWS - Design Techniques for Adaptive Embedded SystemsPRESS - Predictable Embedded Software Systems
Available from: 2015-10-06 Created: 2015-09-29 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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