M-DAFTO: Multi-Stage Deferred Acceptance Based Fair Task Offloading in IoT-Fog Systems

Abstract

Resource-constrained Internet of Things (IoT) devices depend on remote Cloud/Fog Nodes (FNs) to execute deadline-sensitive services. Offloading computations of real-time services to a remote cloud server results in intolerable latency due to intermittent channels, higher transmission delays, and scarce spectrum resources. Therefore, offloading to nearby FNs is preferable; however, it introduces several significant issues: ( i ) allocation of limited FN resources, ( ii ) deadline constraint of heterogeneous services, and ( iii ) requirement of computationally inexpensive and scalable strategies. This article proposes a M-DAFTO model to tackle the abovementioned issues and generate a fair offloading plan in polynomial time. The offloading problem is modeled as a many-to-one matching game with maximum and minimum quotas at each FN. Because the deferred acceptance (DA) algorithm fails to operate with minimum quotas, we adopt a variant of the DA algorithm, a multistage deferred acceptance (MSDA) algorithm, to solve the offloading problem. The overall goal of M-DAFTO is to reduce the aggregate offloading delay with increased assignment of tasks to FNs. Extensive simulation and analysis confirm a 30.26% and a 93.53% reduction in offloading delay and outages (unassigned tasks) compared to the baselines.