Towards Self-Adaptive Peer-to-Peer Monitoring for Fog Environments

TL;DR

This paper presents a self-adaptive peer-to-peer monitoring system for fog environments that dynamically adjusts its behavior to improve accuracy and efficiency, addressing heterogeneity and resource constraints.

Contribution

It introduces a novel self-adaptive P2P monitoring approach utilizing a lightweight rule-based system for fog environments, enhancing accuracy and resource efficiency.

Findings

Improved monitoring accuracy through adaptation

Reduced network and power consumption

Higher memory usage due to adaptation

Abstract

Monitoring is a critical component in fog environments: it promptly provides insights about the behavior of systems, reveals Service Level Agreements (SLAs) violations, enables the autonomous orchestration of services and platforms, calls for the intervention of operators, and triggers self-healing actions. In such environments, monitoring solutions have to cope with the heterogeneity of the devices and platforms present in the Fog, the limited resources available at the edge of the network, and the high dynamism of the whole Cloud-to-Thing continuum. This paper addresses the challenge of accurately and efficiently monitoring the Fog with a self-adaptive peer-to-peer (P2P) monitoring solution that can opportunistically adjust its behavior according to the collected data exploiting a lightweight rule-based expert system. Empirical results show that adaptation can improve monitoring accuracy, while reducing network and power consumption at the cost of higher memory consumption.