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Utility-based Allocation of Industrial IoT Applications in Mobile Edge Clouds
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2018 (English)In: 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC), IEEE, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Mobile Edge Clouds (MECs) create new opportunities and challenges in terms of scheduling and running applications that have a wide range of latency requirements, such as intelligent transportation systems, process automation, and smart grids. We propose a two-tier scheduler for allocating runtime resources to Industrial Internet of Things (IIoT) applications in MECs. The scheduler at the higher level runs periodically - monitors system state and the performance of applications - and decides whether to admit new applications and migrate existing applications. In contrast, the lower-level scheduler decides which application will get the runtime resource next. We use performance based metrics that tells the extent to which the runtimes are meeting the Service Level Objectives (SLOs) of the hosted applications. The Application Happiness metric is based on a single application's performance and SLOs. The Runtime Happiness metric is based on the Application Happiness of the applications the runtime is hosting. These metrics may be used for decision-making by the scheduler, rather than runtime utilization, for example. We evaluate four scheduling policies for the high-level scheduler and five for the low-level scheduler. The objective for the schedulers is to minimize cost while meeting the SLO of each application. The policies are evaluated with respect to the number of runtimes, the impact on the performance of applications and utilization of the runtimes. The results of our evaluation show that the high-level policy based on Runtime Happiness combined with the low-level policy based on Application Happiness outperforms other policies for the schedulers, including the bin packing and random strategies. In particular, our combined policy requires up to 30% fewer runtimes than the simple bin packing strategy and increases the runtime utilization up to 40% for the Edge Data Center (DC) in the scenarios we evaluated.

Place, publisher, year, edition, pages
IEEE, 2018.
Series
IEEE International Performance Computing and Communications Conference (IPCCC), ISSN 1097-2641
National Category
Computer Systems Computer Engineering
Identifiers
URN: urn:nbn:se:umu:diva-160322DOI: 10.1109/PCCC.2018.8711075ISI: 000469326500052ISBN: 978-1-5386-6808-5 (electronic)ISBN: 978-1-5386-6807-8 (print)ISBN: 978-1-5386-6809-2 (print)OAI: oai:DiVA.org:umu-160322DiVA, id: diva2:1325697
Conference
37th IEEE International Performance Computing and Communications Conference (IPCCC), Orlando, FL, November 17-19, 2018
Funder
EU, Horizon 2020, ICT30Available from: 2019-06-17 Created: 2019-06-17 Last updated: 2019-06-17Bibliographically approved

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Mehta, AmardeepBayuh Lakew, EwnetuTordsson, JohanElmroth, Erik

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Citation style
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