A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation
Jiaming Cheng, Duong Thuy Anh Nguyen, Lele Wang, Duong Tung Nguyen,, Vijay K. Bhargava

TL;DR
This paper introduces two novel online pricing algorithms based on multi-armed bandit models for efficient, real-time heterogeneous edge resource allocation without prior demand knowledge, improving platform profit and user experience.
Contribution
It develops two new bandit-based online pricing mechanisms, KL-UCB and Min-Max Optimal, tailored for edge resource allocation with no need for demand distribution assumptions.
Findings
Proposed algorithms outperform benchmark bandit schemes in numerical tests.
The mechanisms adapt dynamically to demand, enhancing profit and resource utilization.
Real-time operation without demand prior knowledge is feasible and effective.
Abstract
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…
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Age of Information Optimization · IoT and Edge/Fog Computing
