Data-Driven Type-2 Fuzzy Sets for Tasks Management at the Edge

Abstract

Tasks allocation at the edge of the network is a significant research topic for the upcoming new era of the intelligent edge mesh. One can easily detect interesting attempts to define novel algorithms for distributing tasks into a number of heterogeneous edge nodes. Nodes interact in very dynamic environments, thus, their availability/capability of efficiently executing tasks in real time varies. In this paper, we propose a model for allocating tasks under the uncertainty present in an edge computing environment. The uncertainty is related to the status of edge nodes and their availability for performing the requested processing activities. To manage this uncertainty, we adopt a Type-2 Fuzzy Logic system and propose a novel approach for delivering the appropriate fuzzy sets for input and output variables. Our methodology is fully adapted to nodes' status as exposed by statistical reports exchanged at pre-defined intervals. We propose a data-driven approach that delivers the upper and lower bounds of our Type-2 fuzzy sets and present the corresponding model. We incorporate the uncertainty management mechanism into the decision making model of edge nodes being responsible to select the most appropriate peers for offloading tasks that are not possible to be executed locally. We present the performance of the proposed model through an extensive experimental evaluation and reveal its pros and cons. © 2017 IEEE.