## 3 Ubunye Proposal Overview
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **QoS or QoE Oriented** &
\begin{tabular}{c} **Resource Orchestration** \\ **Algorithm** \\ \end{tabular} & **Service Classification** & **Artificial Intelligence** & **Auction Algorithm** \\ \hline
[19] & QoS (latency guarantee) &
\begin{tabular}{c} Latency-guaranteed resource \\ double \\ auction (LGRDA) \\ \end{tabular} & No & Not mentioned & Double auction \\ \hline
[20] & QoS (service time reduction) & \
\begin{tabular}{c} Stackelberg game, \\ “many-to-many” and \\ “one-to-many” matching \\ \end{tabular} & No & Not mentioned &
\begin{tabular}{c} Stackelberg game (a type of \\ generalized auction) \\ \end{tabular} \\ \hline
[21] & QoS (service performance, \\ user QoS requirements) & \
\begin{tabular}{c} Service placement, \\ request scheduling \\ \end{tabular} & No & Not mentioned &
\begin{tabular}{c} Pricing mechanism \\ (related to auction) \\ \end{tabular} \\ \hline
[15] & QoS (low latency, high throughput, IoT reliability) &
\begin{tabular}{c} Double auction mechanism, \\ heuristic algorithms for \\ matching and \\ resource allocation \\ \end{tabular} & No & Not mentioned & Double auction (TAFS) \\ \hline
[22] & QoS (resource and \\ bandwidth constraints) & \
\begin{tabular}{c} Reverse auction mechanism, \\ resource allocation \\ based on monotonic \\ submodular function \\ \end{tabular} & No &
\begin{tabular}{c} Auction mechanism and \\ algorithms (related to AI) \\ \end{tabular} & Reverse auction \\ \hline
[10] & QoE (cache optimization for \\ user preferences) & \
\begin{tabular}{c} Offline deep \\ reinforcement learning \\ \end{tabular} & No &
\begin{tabular}{c} Offline deep reinforcement \\ learning, in the main stage of \\ cache optimization \\ \end{tabular} & Not mentioned \\ \hline
[6] & QoE (5G mobile \\ performance improvement) & MEC service framework & No &
\begin{tabular}{c} ML for QoE optimization in \\ video streaming algorithms \\ \end{tabular} & Not mentioned \\ \hline
[13] & Both QoE and QoS \\ (execution time, energy \\ consumption, user preferences) & Two-phase probabilistic \\ model checking & No &
\begin{tabular}{c} Probabilistic model checking \\ (not strictly considered AI) \\ \end{tabular} & Not mentioned \\ \hline
[23] & Both QoE and QoS (user \\ QoE, execution cost) & \
\begin{tabular}{c} Reverse auction mechanism, \\ IMaxQ and MinC greedy \\ algorithms \\ \end{tabular} & No &
\begin{tabular}{c} Auction mechanism and \\ greedy algorithms \\ (related to AI) \\ \end{tabular} & Reverse auction \\ \hline
[24] & Both QoE and QoS (latency \\ and redundant traffic) & \
\begin{tabular}{c} Dynamic adaptive streaming \\ over HTTP (DASH). \\ No comprehensive edge \\ node orchestration \\ \end{tabular} & Based on ML & Used on service classification & Not mentioned \\ \hline Ubunye & Both QoE and QoS (low latency and high \\ bandwidth constraints) &
\begin{tabular}{c} All-based service \\ classification and Ubunye \\ auction mechanism \\ \end{tabular} & Based on ML & Used on service classification & Ubunye auction \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative table between our proposal and related work. a more satisfactory and responsive service experience, especially applicable in regions with limited network capacity, which makes our solution attractive to network providers.