Monitoring and Proactive Management of QoS Levels in Pervasive Applications
This work addresses the problem of ensuring reliable QoS for pervasive applications in dynamic Edge Computing settings, representing an incremental improvement in management approaches.
The paper tackles the challenge of maintaining high Quality of Service (QoS) in Edge Computing environments with limited computational capabilities by proposing a distributed and intelligent decision-making approach for task scheduling, which involves monitoring QoS levels and proactively offloading tasks to peers or the Cloud, with evaluation through multiple experimental scenarios showing performance benefits.
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.