Performance Evaluation of Some Adaptive Task Allocation Algorithms for Fog Networks

Abstract

Fog Computing brings resources closer to the end-user and improves user experience. Tasks with stringent QoS requirements can be processed locally in the Edge while the more elastic ones can be sent to the Cloud. For the benefits of this flexible architecture to be seen, task allocation algorithms should be dynamic and adapt to the load in the Fog and in the Cloud. Using a discrete-event simulation approach, we evaluate the performance of four simple adaptive algorithms based on congestion estimation and compare them with the standard nearest node algorithm that uses non adaptive routing. We consider a setting in which base stations (access nodes) forward traffic to computing nodes (Fog and Cloud nodes) in a distributed way without coordination and sharing of state-information between the access and computing nodes. The algorithms are tested for their adaptability to sudden changes in the arrival rate of requests (to model peak hours) as well as robustness to the variance of the request-size distributions to understand the advantages and drawbacks of each of them. They are shown to perform well in scenarios with and without offloading.