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A Generic Framework for Task Offloading in mmWave MEC Backhaul Networks
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013). (Disco, Distributed Systems and Communications)ORCID-id: 0000-0001-9866-8209
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013). (Disco, Distributed Systems and Communications)
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013). (Disco, Distributed Systems and Communications)ORCID-id: 0000-0002-9446-8143
2018 (engelsk)Inngår i: 2018 IEEE Global Communications Conference (GLOBECOM), 2018, s. 1-7Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

With the emergence of millimeter-Wave (mmWave) communication technology, the capacity of mobile backhaul networks can be significantly increased. On the other hand, Mobile Edge Computing (MEC) provides an appropriate infrastructure to offload latency-sensitive tasks. However, the amount of resources in MEC servers is typically limited. Therefore, it is important to intelligently manage the MEC task offloading by optimizing the backhaul bandwidth and edge server resource allocation in order to decrease the overall latency of the offloaded tasks. This paper investigates the task allocation problem in MEC environment, where the mmWave technology is used in the backhaul network. We formulate a Mixed Integer NonLinear Programming (MINLP) problem with the goal to minimize the total task serving time. Its objective is to determine an optimized network topology, identify which server is used to process a given offloaded task, find the path of each user task, and determine the allocated bandwidth to each task on mmWave backhaul links. Because the problem is difficult to solve, we develop a two-step approach. First, a Mixed Integer Linear Program (MILP) determining the network topology and the routing paths is optimally solved. Then, the fractions of bandwidth allocated to each user task are optimized by solving a quasi-convex problem. Numerical results illustrate the obtained topology and routing paths for selected scenarios and show that optimizing the bandwidth allocation significantly improves the total serving time, particularly for bandwidth-intensive tasks.

sted, utgiver, år, opplag, sider
2018. s. 1-7
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
URN: urn:nbn:se:kau:diva-71436DOI: 10.1109/GLOCOM.2018.8647559ISI: 000465774302096ISBN: 978-1-5386-4727-1 (digital)ISBN: 978-1-5386-6976-1 (tryckt)OAI: oai:DiVA.org:kau-71436DiVA, id: diva2:1293790
Konferanse
2018 IEEE Global Communications Conference (GLOBECOM) Abu Dhabi, United Arab Emirates, 9-13 dec
Prosjekter
Socra, 4840
Forskningsfinansiär
Knowledge FoundationTilgjengelig fra: 2019-03-05 Laget: 2019-03-05 Sist oppdatert: 2019-06-11bibliografisk kontrollert

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Alizadeh Noghani, KyoomarsGhazzai, HakimKassler, Andreas

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