Optimized provisioning of edge computing resources with heterogeneous workload in IoT networks

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dc.contributor.author Kherraf, Nouha
dc.contributor.author Alameddine, Hyame Assem
dc.contributor.author Sharafeddine, Sanaa
dc.contributor.author Assi, Chadi
dc.contributor.author Ghrayeb, Ali
dc.date.accessioned 2019-05-06T09:54:33Z
dc.date.available 2019-05-06T09:54:33Z
dc.date.copyright 2019 en_US
dc.identifier.issn 1932-4537 en_US
dc.identifier.uri http://hdl.handle.net/10725/10552
dc.description.abstract The proliferation of smart connected Internet of Things (IoT) devices is bringing tremendous challenges in meeting the performance requirement of their supported real-time applications due to their limited resources in terms of computing, storage, and battery life. In addition, the considerable amount of data they generate brings extra burden to the existing wireless network infrastructure. By enabling distributed computing and storage capabilities at the edge of the network, Multi-access Edge Computing (MEC) serves delay sensitive, computationally intensive applications. Managing the heterogeneity of the workload generated by IoT devices, especially in terms of computing and delay requirements, while being cognizant of the cost to network operators requires an efficient dimensioning of the MEC-enabled network infrastructure. Hence, in this paper, we study and formulate the problem of MEC Resource Provisioning and Workload Assignment for IoT services (RPWA) as a Mixed Integer Program (MIP) to jointly decide on the number and the location of edge servers and applications to deploy, in addition to the workload assignment. Given its complexity, we propose a decomposition approach to solve it which consists of decomposing RPWA into the Delay Aware Load Assignment (DALA) sub-problem and the Mobile Edge Servers Dimensioning (MESD) sub-problem. We analyze the effectiveness of the proposed algorithm through extensive simulations and highlight valuable performance trends and trade-offs as a function of various system parameters. en_US
dc.language.iso en en_US
dc.title Optimized provisioning of edge computing resources with heterogeneous workload in IoT networks en_US
dc.type Article en_US
dc.description.version Published en_US
dc.author.school SAS en_US
dc.author.idnumber 200502746 en_US
dc.author.department Computer Science And Mathematics en_US
dc.description.embargo N/A en_US
dc.relation.journal IEEE Transactions on Network and Service Management en_US
dc.journal.volume 16 en_US
dc.journal.issue 2 en_US
dc.article.pages 459-474 en_US
dc.keywords Internet of Things en_US
dc.keywords Cloud computing en_US
dc.keywords Servers en_US
dc.keywords Delays en_US
dc.keywords Edge computing en_US
dc.keywords Task analysis en_US
dc.keywords Resource management en_US
dc.identifier.doi http://dx.doi.org/10.1109/TNSM.2019.2894955 en_US
dc.identifier.ctation Kherraf, N., Alameddine, H. A., Sharafeddine, S., Assi, C., & Ghrayeb, A. (2019). Optimized Provisioning of Edge Computing Resources with Heterogeneous Workload in IoT Networks. IEEE Transactions on Network and Service Management, 16 (2), 459-474 en_US
dc.author.email sanaa.sharafeddine@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url https://ieeexplore.ieee.org/abstract/document/8624371/keywords#keywords en_US
dc.orcid.id https://orcid.org/0000-0001-6548-1624 en_US
dc.author.affiliation Lebanese American University en_US

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