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Distributed Cost-Optimized Placement for Latency-Critical Applications in Heterogeneous Environments
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2018 (English)In: Proceedings of the IEEE 15th International Conference on Autonomic Computing (ICAC), IEEE Computer Society, 2018, p. 121-130Conference paper, Published paper (Refereed)
Abstract [en]

Mobile Edge Clouds (MECs) with 5G will create new opportunities to develop latency-critical applications in domains such as intelligent transportation systems, process automation, and smart grids. However, it is not clear how one can costefficiently deploy and manage a large number of such applications given the heterogeneity of devices, application performance requirements, and workloads. This work explores cost and performance dynamics for IoT applications, and proposes distributed algorithms for automatic deployment of IoT applications in heterogeneous environments. Placement algorithms were evaluated with respect to metrics including number of required runtimes, applications’ slowdown, and the number of iterations used to place an application. Iterative search-based distributed algorithms such as Size Interval Actor Assignment in Groups (SIAA G) outperformed random and bin packing algorithms, and are therefore recommended for this purpose. Size Interval Actor Assignment in Groups at Least Utilized Runtime (SIAA G LUR) algorithm is also recommended when minimizing the number of iterations is important. The tradeoff of using SIAA G algorithms is a few extra runtimes compared to bin packing algorithms.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018. p. 121-130
Series
Proceedings of the International Conference on Autonomic Computing, ISSN 2474-0764
Keywords [en]
Mobile Edge Clouds, Fog Computing, IoTs, Distributed algorithms
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:umu:diva-151457DOI: 10.1109/ICAC.2018.00022ISBN: 978-1-5386-5139-1 (electronic)OAI: oai:DiVA.org:umu-151457DiVA, id: diva2:1244992
Conference
2018 IEEE International Conference on Autonomic Computing, Trento, Italy, September 3-7, 2018
Available from: 2018-09-04 Created: 2018-09-04 Last updated: 2019-06-26Bibliographically approved
In thesis
1. Resource allocation for Mobile Edge Clouds
Open this publication in new window or tab >>Resource allocation for Mobile Edge Clouds
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Recent advances in Internet technologies have led to the proliferation of new distributed applications in the transportation, healthcare, mining, security, and entertainment sectors. The emerging applications have characteristics such as being bandwidth-hungry, latency-critical, and applications with a user population contained within a limited geographical area, and require high availability, low jitter, and security.

One way of addressing the challenges arising because of these emerging applications, is to move the computing capabilities closer to the end-users, at the logical edge of a network, in order to improve the performance, operating cost, and reliability of applications and services. These distributed new resources and software stacks, situated on the path between today's centralized data centers and devices in close proximity to the last mile network, are known as Mobile Edge Clouds (MECs). The distributed MECs provides new opportunities for the management of compute resources and the allocation of applications to those resources in order to minimize the overall cost of application deployment while satisfying end-user demands in terms of application performance.

However, these opportunities also present three significant challenges. The first challenge is where and how much computing resources to deploy along the path between today's centralized data centers and devices for cost-optimal operations. The second challenge is where and how much resources should be allocated to which applications to meet the applications' performance requirements while minimizing operational costs. The third challenge is how to provide a framework for application deployment on resource-constrained IoT devices in heterogeneous environments. 

This thesis addresses the above challenges by proposing several models, algorithms, and simulation and software frameworks. In the first part, we investigate methods for early detection of short-lived and significant increase in demand for computing resources (also called spikes) which may cause significant degradation in the performance of a distributed application. We make use of adaptive signal processing techniques for early detection of spikes. We then consider trade-offs between parameters such as the time taken to detect a spike and the number of false spikes that are detected. In the second part, we study the resource planning problem where we study the cost benefits of adding new compute resources based on performance requirements for emerging applications. In the third part, we study the problem of allocating resources to applications by formulating as an optimization problem, where the objective is to minimize overall operational cost while meeting the performance targets of applications. We also propose a hierarchical scheduling framework and policies for allocating resources to applications based on performance metrics of both applications and compute resources. In the last part, we propose a framework, Calvin Constrained, for resource-constrained devices, which is an extension of the Calvin framework and supports a limited but essential subset of the features of the reference framework taking into account the limited memory and processing power of the resource-constrained IoT devices.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2018. p. 30
Series
Report / UMINF, ISSN 0348-0542 ; 18.10
Keywords
Mobile Edge Clouds, Edge/Fog Computing, IoTs, Distributed Resource Allocation
National Category
Computer Systems
Research subject
Computer Science; Computer Systems
Identifiers
urn:nbn:se:umu:diva-151480 (URN)978-91-7601-925-2 (ISBN)
Public defence
2018-10-01, MA121, MIT-huset, Umeå, 13:30 (English)
Opponent
Supervisors
Available from: 2018-09-10 Created: 2018-09-04 Last updated: 2018-09-07Bibliographically approved

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Mehta, AmardeepElmroth, Erik

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