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Optimal Service Caching and Pricing in Edge Computing: a Bayesian Gaussian Process Bandit Approach
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: 0000-0002-4876-0223
2022 (English)In: IEEE Transactions on Mobile Computing, ISSN 1536-1233, E-ISSN 1558-0660, p. 1-15Article in journal (Refereed) Published
Abstract [en]

Motivated by the emergence of function-as-a-service (FaaS) as a programming abstraction for edge computing, we consider the problem of caching and pricing applications for edge computation offloading in a dynamic environment where (WDs) can be active or inactive at any point in time. We model the problem as a single leader multiple-follower Stackelberg game, where the service operator is the leader and decides what applications to cache and how much to charge for their use, while the WDs are the followers and decide whether or not to offload their computations. We show that the WDs' interaction can be modeled as a player-specific congestion game and show the existence and computability of equilibria. We then show that under perfect and complete information the equilibrium price of the service operator can be computed in polynomial time for any cache placement. For the incomplete information case, we propose a Bayesian Gaussian Process Bandit algorithm for learning an optimal price for a cache placement and provide a bound on its asymptotic regret. We then propose a Gaussian process approximation-based greedy heuristic for computing the cache placement. We use extensive simulations to evaluate the proposed learning scheme, and show that it outperforms state of the art algorithms by up to 50% at little computational overhead.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. p. 1-15
Keywords [en]
Computational modeling, Costs, Games, Gaussian processes, Pricing, Servers, Task analysis, computation offloading, Computer games, Computer programming, Gaussian distribution, Gaussian noise (electronic), Learning algorithms, Optimization, Polynomial approximation, Bayesian Gaussian process, Cache placement, Computational modelling, Dynamic environments, Edge computing, Game, Programming abstractions
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-328948DOI: 10.1109/TMC.2022.3221465ISI: 001136301500005Scopus ID: 2-s2.0-85141646376OAI: oai:DiVA.org:kth-328948DiVA, id: diva2:1767411
Note

QC 20230614

Available from: 2023-06-14 Created: 2023-06-14 Last updated: 2024-03-18Bibliographically approved

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Tutuncuoglu, FeridunDán, György

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