  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.
