Optimal Service Caching and Pricing in Edge Computing: A Bayesian Gaussian Process Bandit Approach

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 Wirelesss Devices (WDs) can be active or inactive at any point in tim...

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Bibliographic Details
Published in:IEEE transactions on mobile computing Vol. 23; no. 1; pp. 705 - 718
Main Authors: Tutuncuoglu, Feridun, Dan, Gyorgy
Format: Magazine Article
Language:English
Published: Los Alamitos IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Bayesian analysis
Bayesian Gaussian process
bayesian optimization
Cache placement
Caching
combinatorial optimization
Computation offloading
Computational modeling
Computational modelling
Computer games
Computer programming
congestion games
Costs
Dynamic environments
Edge computing
Game
Game theory
Games
Gaussian distribution
Gaussian noise (electronic)
Gaussian process
Gaussian processes
Learning algorithms
Machine learning
Online learning
Optimization
Placement
Polynomial approximation
Polynomials
Pricing
Programming abstractions
Servers
stackelberg games
Task analysis
ISSN:1536-1233, 1558-0660, 1558-0660
Online Access:Get full text
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Summary: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 Wirelesss Devices (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.
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ISSN:1536-1233
1558-0660
1558-0660
DOI:10.1109/TMC.2022.3221465