Monitoring and Proactive Management of QoS Levels in Pervasive Applications

TL;DR

This paper proposes a distributed, intelligent approach for monitoring and managing QoS levels in Edge Computing, enabling proactive task offloading to maintain high service quality in dynamic environments.

Contribution

It introduces a novel distributed decision-making scheme that continuously monitors QoS and proactively offloads tasks to peers or the cloud to ensure high QoS levels in EC.

Findings

The scheme effectively maintains QoS in dynamic EC environments.

Proactive offloading improves task completion rates.

Monitoring reduces QoS violations.

Abstract

The advent of Edge Computing (EC) as a promising paradigm that provides multiple computation and analytics capabilities close to data sources opens new pathways for novel applications. Nonetheless, the limited computational capabilities of EC nodes and the expectation of ensuring high levels of QoS during tasks execution impose strict requirements for innovative management approaches. Motivated by the need of maintaining a minimum level of QoS during EC nodes functioning, we elaborate a distributed and intelligent decision-making approach for tasks scheduling. Our aim is to enhance the behavior of EC nodes making them capable of securing high QoS levels. We propose that nodes continuously monitor QoS levels and systematically evaluate the probability of violating them to proactively decide some tasks to be offloaded to peer nodes or Cloud. We present, describe and evaluate the proposed scheme through multiple experimental scenarios revealing its performance and the benefits of the envisioned monitoring mechanism when serving processing requests in very dynamic environments like the EC.