Emmanuel Thepie Fapi

Maryam Al Shami, Jun Yan, Emmanuel Thepie Fapi

The lack of a unified mechanism to coordinate and prioritize the actions of different applications can create three types of conflicts (direct, indirect, and implicit). Conflict management in O-RAN refers to the process of identifying and resolving conflicts between network applications. In our paper, we introduce a novel data-driven GCN-based method called GRAPH-based Intelligent xApp Conflict Prediction and Analysis (GRAPHICA) based on Graph Convolutional Network (GCN). It predicts three types of conflicts (direct, indirect, and implicit) and pinpoints the root causes (xApps). GRAPHICA captures the complex and hidden dependencies among the xApps, controlled parameters, and KPIs in O-RAN to predict possible conflicts. Then, it identifies the root causes (xApps) contributing to the predicted conflicts. The proposed method was tested on highly imbalanced synthesized datasets where conflict instances range from 40% to 10%. The model is tested in a setting that simulates real-world scenarios where conflicts are rare to assess its performance. Experimental results demonstrate a high F1-score over 98% for the synthesized datasets with different levels of class imbalance.

Anirudha Jitani, Aditya Mahajan, Zhongwen Zhu et al.

Mobile Edge Computing (MEC) refers to the concept of placing computational capability and applications at the edge of the network, providing benefits such as reduced latency in handling client requests, reduced network congestion, and improved performance of applications. The performance and reliability of MEC are degraded significantly when one or several edge servers in the cluster are overloaded. Especially when a server crashes due to the overload, it causes service failures in MEC. In this work, an adaptive admission control policy to prevent edge node from getting overloaded is presented. This approach is based on a recently-proposed low complexity RL (Reinforcement Learning) algorithm called SALMUT (Structure-Aware Learning for Multiple Thresholds), which exploits the structure of the optimal admission control policy in multi-class queues for an average-cost setting. We extend the framework to work for node overload-protection problem in a discounted-cost setting. The proposed solution is validated using several scenarios mimicking real-world deployments in two different settings - computer simulations and a docker testbed. Our empirical evaluations show that the total discounted cost incurred by SALMUT is similar to state-of-the-art deep RL algorithms such as PPO (Proximal Policy Optimization) and A2C (Advantage Actor Critic) but requires an order of magnitude less time to train, outputs easily interpretable policy, and can be deployed in an online manner.