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A QoS-aware, Proactive Tasks Offloading Model for Pervasive Applications

Edge Computing (EC) is a promising paradigm that provides multiple computation and analytics capabilities close to data sources while alleviating the drawbacks of centralized systems. Nonetheless, due to the limited computational resources of EC nodes and the expectation of ensuring high levels of QoS during tasks execution, innovative task management approaches are required. In this paper, we propose a distributed and intelligent decision-making scheme for tasks scheduling at the edge, considering multiple criteria/parameters. We enhance the behavior of EC nodes making them capable of securing high QoS levels during their functioning. Every EC node systematically evaluates the probability of violating the desired QoS levels and proactively decides some tasks to be offloaded when the aforementioned condition stands true. 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.