  The widespread adoption of edge computing has emerged as a prominent trend
for alleviating task processing delays and reducing energy consumption.
However, the dynamic nature of network conditions and the varying computation
capacities of edge servers (ESs) can introduce disparities between computation
loads and available computing resources in edge computing networks, potentially
leading to inadequate service quality. To address this challenge, this paper
investigates a practical scenario characterized by dynamic task offloading.
Initially, we examine traditional Multi-armed Bandit (MAB) algorithms, namely
the $\varepsilon$-greedy algorithm and the UCB1-based algorithm. However, both
algorithms exhibit certain weaknesses in effectively addressing the tidal data
traffic patterns. Consequently, based on MAB, we propose an adaptive task
offloading algorithm (ATOA) that overcomes these limitations. By conducting
extensive simulations, we demonstrate the superiority of our ATOA solution in
reducing task processing latency compared to conventional MAB methods. This
substantiates the effectiveness of our approach in enhancing the performance of
edge computing networks and improving overall service quality.
