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EAICA: An energy-aware resource provisioning algorithm for Real-Time Cloud services
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1384-5323
School of ECE, University of Tehran, Iran.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-3375-6766
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-6132-7945
2016 (English)In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2016Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing is receiving an increasing attention when it comes to providing a wide range of cost-effective services. In this context, energy consumption of communication and computing resources contribute to a major portion of the cost of services. On the other hand, growing energy consumption not only results in a higher operational cost, but it also causes negative environmental impacts. A large number of cloud applications in, e.g., telecommunication, multimedia, and video gaming, have real-time requirements. A cloud computing system hosting such applications, that requires a strict timing guarantee for its provided services, is denoted a Real-Time Cloud (RTC). Minimizing energy consumption in a RTC is a complicated task as common methods that are used for decreasing energy consumption can potentially lead to timing violations. In this paper, we present an online energy-aware resource provisioning framework to reduce the deadline miss ratio for real-time cloud services. The proposed provisioning framework not only considers the energy consumption of servers but it also takes the energy consumption of the communication network into account, to provide a holistic solution. An extensive range of simulation results, based on real data, show a noticeable improvement regarding energy consumption while keeping the number of timing violations less than 1% in average.

Place, publisher, year, edition, pages
2016.
Keywords [en]
communication-aware allocation, energy-aware scheduling, quality of service, real-time cloud services, resource provisioning, Cloud computing, Cost effectiveness, Costs, Distributed database systems, Energy utilization, Environmental impact, Factory automation, Power management, Telecommunication services, Web services, Cloud services, Communication-aware, Computing resource, Deadline miss ratio, Provisioning framework, Real time requirement, Distributed computer systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-34045DOI: 10.1109/ETFA.2016.7733574ISI: 000389524200081Scopus ID: 2-s2.0-84996587275ISBN: 978-1-5090-1314-2 (print)OAI: oai:DiVA.org:mdh-34045DiVA, id: diva2:1053197
Conference
21st IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2016, 6 September 2016 through 9 September 2016
Available from: 2016-12-08 Created: 2016-12-08 Last updated: 2018-02-12Bibliographically approved
In thesis
1. Optimizing Timing-Critical Cloud Resources in a Smart Factory
Open this publication in new window or tab >>Optimizing Timing-Critical Cloud Resources in a Smart Factory
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis addresses the topic of resource efficiency in the context of timing critical components that are used in the realization of a Smart Factory.The concept of the smart factory is a recent paradigm to build future production systems in a way that is both smarter and more flexible. When it comes to realization of a smart factory, three principal elements play a significant role, namely Embedded Systems, Internet of Things (IoT) and Cloud Computing. In a smart factory, efficient use of computing and communication resources is a prerequisite not only to obtain a desirable performance for running industrial applications, but also to minimize the deployment cost of the system in terms of the size and number of resources that are required to run industrial applications with an acceptable level of performance. Most industrial applications that are involved in smart factories, e.g., automation and manufacturing applications, are subject to a set of strict timing constraints that must be met for the applications to operate properly. Such applications, including underlying hardware and software components that are used to run 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. To do so we need a time-predictable IoT/Cloud Computing framework to deal with the real-time constraints that are inherent in industrial applications running in a smart factory. Afterwards, with respect to the time predictable framework, the number of required computing and communication resources can and should be optimized such that the deployed system is cost efficient. In this thesis, to investigate and present solutions that provide and improve the resource efficiency of computing and communication resources in a smart factory, we conduct research following three themes: (i) multi-core embedded processors, which are the key element in terms of computing components embedded in the machinery of a smart factory, (ii) cloud computing data centers, as the supplier of a massive data storage and a large computational power, and(iii) IoT, for providing the interconnection of computing components embedded in the objects of a smart factory. Each of these themes are targeted separately to optimize resource efficiency. For each theme, we identify key challenges when it comes to achieving a resource-efficient design of the system. We then formulate the problem and propose solutions to optimize the resource efficiency of the system, while satisfying all timing constraints reflected in the model. We then propose a comprehensive resource allocation mechanism to optimize the resource efficiency in the whole system while considering the characteristics of each of these research themes. The experimental results indicate a clear improvement when it comes to timing-critical IoT / Cloud Computing resources in a smart factory. At the level of multi-core embedded devices, the total CPU usage of a quad-core processor is shown to be improved by 11.2%. At the level of Cloud Computing, the number of cloud servers that are required to execute a given set of real-time applications is shown to be reduced by 25.5%. In terms of network components that are used to collect sensor data, our proposed approach reduces the total deployment cost of thesystem by 24%. In summary these results all contribute towards the realization of a future smart factory.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2018
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 255
Keywords
Cloud Computing; Fog Computing; Edge Computing; Real-Time Systems; Resource Allocation
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-38659 (URN)978-91-7485-376-6 (ISBN)
Public defence
2018-03-08, Gamma, Mälardalens högskola, Västerås, 13:30 (English)
Opponent
Supervisors
Available from: 2018-02-13 Created: 2018-02-12 Last updated: 2018-06-12Bibliographically approved

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