Proactive tasks management for Pervasive Computing Applications

Abstract

Current advances in the Internet of Things (IoT) and Edge Computing (EC) involve numerous devices/nodes present at both ‘layers’ being capable of performing simple processing activities close to end users. This approach targets to limit the latency that users face when consuming the provided services. The minimization of the latency requires for novel techniques that deliver efficient schemes for tasks management at the edge infrastructure and the management of the uncertainty related to the status of edge nodes during the decision making as proposed in this paper. Tasks should be executed in the minimum time especially when we aim to support real time applications. In this paper, we propose a new model for the proactive management of tasks’ allocation to provide a decision making model that results the best possible node where every task should be executed. A task can be executed either locally at the node where it is initially reported or in a peer node, if this is more efficient. We focus on the management of the uncertainty over the characteristics of peer nodes when the envisioned decisions should be realized. The proposed model aims at providing the best possible action for any incoming task. For such purposes, we adopt an unsupervised machine learning technique. We present the problem under consideration and specific formulations accompanied by the proposed solution. Our extensive experimental evaluation with synthetic and real data targets to reveal the advantages of the proposed scheme.