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Online Learning for Rate-Adaptive Task Offloading Under Latency Constraints in Serverless Edge Computing
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.ORCID iD: 0000-0002-6466-8304
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.ORCID iD: 0000-0002-4876-0223
2023 (English)In: IEEE/ACM Transactions on Networking, ISSN 1063-6692, E-ISSN 1558-2566, Vol. 31, no 2, p. 695-709Article in journal (Refereed) Published
Abstract [en]

We consider the interplay between latency constrained applications and function-level resource management in a serverless edge computing environment. We develop a game theoretic model of the interaction between rate adaptive applications and a load balancing operator under a function-oriented pay-as-you-go pricing model. We show that under perfect information, the strategic interaction between the applications can be formulated as a generalized Nash equilibrium problem, and use variational inequality theory to prove that the game admits an equilibrium. For the case of imperfect information, we propose an online learning algorithm for applications to maximize their utility through rate adaptation and resource reservation. We show that the proposed algorithm can converge to equilibria and achieves zero regret asymptotically, and our simulation results show that the algorithm achieves good system performance at equilibrium, ensures fast convergence, and enables applications to meet their latency constraints.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2023. Vol. 31, no 2, p. 695-709
Keywords [en]
Generalized Nash equilibrium problem, online learning, resource allocation, serverless edge computing, Computation theory, Data structures, E-learning, Edge computing, Game theory, Job analysis, Learning algorithms, Variational techniques, Wireless sensor networks, Computational modelling, FAA, Generalized Nash equilibrium problems, Resources allocation, Task analysis, Wireless communications
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-327031DOI: 10.1109/TNET.2022.3197669ISI: 000849231300001Scopus ID: 2-s2.0-85137579713OAI: oai:DiVA.org:kth-327031DiVA, id: diva2:1758612
Note

QC 20230523

Available from: 2023-05-23 Created: 2023-05-23 Last updated: 2023-05-23Bibliographically approved

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Tutuncuoglu, FeridunJosilo, SladanaDán, György

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