AI Optimized QoS based On Demand Services with Incentive Mechanism in P2P Networks

Abstract

This paper presents a parallel buffer filling Video-on-Demand (VoD) streaming technique in Peer-to- Peer (P2P) environments based on Artificial Intelligence (AI). Peer-to-peer (P2P) energy trading is an innovative concept poised to transform energy demand management and utilization. EnergyShare AI is a powerful peer-to-peer energy exchange system that operates on a P2P model that integrates advanced machine learning with distributed energy sharing. Most notably, a P2P video-on- demand streaming technique has to handle peer churn and asynchronous arrival of peers efficiently. Our answer to the challenge provides robust recovery by parallel buffer filling method and encourage the peer to maintain the required connectivity through modified grade-based incentive mechanism. In this paper, we develop analytical models and categorize peer into different potential groups. Quality of service (QoS) is the use of mechanisms or technologies that work on a network to control traffic and ensure the performance of critical applications with limited network capacity. It enables organizations to adjust their overall network traffic by prioritizing specific high-performance applications. Design of parallel streaming into different buffer zones of the client peer with incentives, makes us to understand and control the interplay between efficiency, fairness and incentive in P2P video-on-demand, based on QoS services. Through analytical analysis, we show that robust P2PVoD can be achieved.