Practical Recovery Solution for Information Loss in Real-Time Network Environment

TL;DR

This paper proposes a least squares estimation method to recover lost information in real-time network feedback algorithms, ensuring convergence despite packet drops caused by network congestion.

Contribution

It introduces a novel LS estimation approach to recover missing data, enhancing the robustness of network optimization algorithms under congestion.

Findings

LS estimation successfully recovers missing information during packet drops.

The method ensures convergence of feedback algorithms in congested network scenarios.

Simulation results confirm improved stability and reliability of network optimization.

Abstract

Feedback mechanism based algorithms are frequently used to solve network optimization problems. These schemes involve users and network exchanging information (e.g. requests for bandwidth allocation and pricing) to achieve convergence towards an optimal solution. However, in the implementation, these algorithms do not guarantee that messages will be delivered to the destination when network congestion occurs. This in turn often results in packet drops, which may cause information loss, and this condition may lead to algorithm failing to converge. To prevent this failure, we propose least square (LS) estimation algorithm to recover the missing information when packets are dropped from the network. The simulation results involving several scenarios demonstrate that LS estimation can provide the convergence for feedback mechanism based algorithm.