Queueing Analysis of Incentive-Based Extreme Edge Service Systems

Abstract

In Edge Computing, computation is pushed towards the end-user to reduce backhaul load, address nascent privacy issues, and enable a range of low latency applications. Extreme Edge Service systems (EES) are a subset of Edge Computing in which services are deployed on user-owned devices in the proximity of the end-user. In this work, we model and analyze an orchestrator-based EES in which users' devices are recruited in exchange for an incentive. We propose to model the incentives' impact on performance using Incentive-Vacation Queueing (IVQ), a vacation queueing model in which server vacations are a proxy for incentives. Moreover, we derive closed-form expressions to evaluate the performance and directly link the performance to incentives, showing the impact of each one of the system parameters.