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How beneficial are intermediate layer Data Centers in Mobile Edge Networks?
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Distributed Systems)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Distributed Systems)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Distributed Systems)
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2016 (Engelska)Ingår i: 2016 IEEE 1st International Workshops on Foundations and Applications of Self-* Systems / [ed] Sameh Elnikety, Peter R. Lewis and Christian Müller-Schloer, 2016, s. 222-229Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

To reduce the congestion due to the future bandwidth-hungry applications in domains such as Health care, Internet of Things (IoT), etc., we study the benefit of introducing additional Data Centers (DCs) closer to the network edge for the optimal application placement. Our study shows that the edge layer DCs in a Mobile Edge Network (MEN) infrastructure is cost beneficial for the bandwidth-hungry applications having their strong demand locality and in the scenarios where large capacity is deployed at the edge layer DCs. The cost savings for such applications can go up to 67%. Additional intermediate layer DCs close to the root DC can be marginally cost beneficial for the compute intensive applications with medium or low demand locality. Hence, a Telecom Network Operator should start building an edge DC first having capacity up to hundreds of servers at the network edge to cater the emerging bandwidth-hungry applications and to minimize its operational cost.

Ort, förlag, år, upplaga, sidor
2016. s. 222-229
Nationell ämneskategori
Kommunikationssystem
Identifikatorer
URN: urn:nbn:se:umu:diva-125640DOI: 10.1109/FAS-W.2016.55ISI: 000391523100042ISBN: 978-1-5090-3651-6 (tryckt)OAI: oai:DiVA.org:umu-125640DiVA, id: diva2:969040
Konferens
FAS* Foundations and Applications of Self* Systems University of Augsburg, Augsburg, Germany, 12-16 September 2016
Tillgänglig från: 2016-09-13 Skapad: 2016-09-13 Senast uppdaterad: 2018-09-04Bibliografiskt granskad
Ingår i avhandling
1. Resource allocation for Mobile Edge Clouds
Öppna denna publikation i ny flik eller fönster >>Resource allocation for Mobile Edge Clouds
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Umeå: Umeå University, 2018. s. 30
Serie
Report / UMINF, ISSN 0348-0542 ; 18.10
Nyckelord
Mobile Edge Clouds, Edge/Fog Computing, IoTs, Distributed Resource Allocation
Nationell ämneskategori
Datorsystem
Forskningsämne
datalogi; datorteknik
Identifikatorer
urn:nbn:se:umu:diva-151480 (URN)978-91-7601-925-2 (ISBN)
Disputation
2018-10-01, MA121, MIT-huset, Umeå, 13:30 (Engelska)
Opponent
Handledare
Tillgänglig från: 2018-09-10 Skapad: 2018-09-04 Senast uppdaterad: 2018-09-07Bibliografiskt granskad

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Mehta, AmardeepKlein, CristianTordsson, JohanElmroth, Erik

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