Proactive and Reactive Decision Based Agent Placement: Reliability and Latency Perspective
Sisay Tadesse Arzo, Mona Esmaeili, Yonatan Melese Worku, Zeinab Akhavan, Michael Devetsikiotis, Payman Zarkesh-Ha · GLOBECOM 2022 - 2022 IEEE Global Communications Conference · 2022
6G is aiming at fully incorporating in-network intelligence towards automated network management. In this regard, a multi-agent-based network automation architecture as a service design is proposed. The architecture introduces in-network intelligence, designing intelligent agents as the fundamental unit which is used as a building block in autonomous network system design. This work focuses on the dynamic agent placement problems in edge/cloud data centers. Agents are softwarized and intelligent versions of network functions that are traditionally implemented in hardware such as firewalls, packet gateways, etc. This paper, based on a combination of proactive and reactive solutions, considered decision accuracy in developing an intelligent decision algorithm that can be used in the prediction agent design. The proactive decision is based on a deep learning prediction algorithm using time-series workload forecasting. However, in case of unforeseen events that are missing from the historical dataset, the proactive decisions could be less reliable. Therefore, network-state feedback should be considered to determine the current network conditions using the change in instant arrival rate as a reactive decision. Then combining it with the proactive decision should be able to capture the unpredictable traffic spikes. The result is used to determine the number and type of agents to instantiate at a given time in the edge/cloud data centers. Using a public dataset in our algorithms, we predicted the workload request for a few days. The result shows improved decision accuracy over the existing solutions using the appropriate amount of dataset, machine learning models, and rate estimation.