A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation
This addresses the challenge of dynamic resource allocation in Edge Computing for platform operators, though it appears incremental as it adapts existing bandit methods to a specific domain.
The paper tackles the problem of efficiently allocating heterogeneous edge resources to maximize profit for an Edge Computing platform by proposing two novel online pricing mechanisms based on multi-armed bandit algorithms, which outperform traditional benchmarks like Epsilon-Greedy and Thompson Sampling in numerical results.
Edge Computing (EC) offers a superior user experience by positioning cloud resources in close proximity to end users. The challenge of allocating edge resources efficiently while maximizing profit for the EC platform remains a sophisticated problem, especially with the added complexity of the online arrival of resource requests. To address this challenge, we propose to cast the problem as a multi-armed bandit problem and develop two novel online pricing mechanisms, the Kullback-Leibler Upper Confidence Bound (KL-UCB) algorithm and the Min-Max Optimal algorithm, for heterogeneous edge resource allocation. These mechanisms operate in real-time and do not require prior knowledge of demand distribution, which can be difficult to obtain in practice. The proposed posted pricing schemes allow users to select and pay for their preferred resources, with the platform dynamically adjusting resource prices based on observed historical data. Numerical results show the advantages of the proposed mechanisms compared to several benchmark schemes derived from traditional bandit algorithms, including the Epsilon-Greedy, basic UCB, and Thompson Sampling algorithms.