Asynchronous Coarse-Grained Load Migration Scheme for IoT Applications in Fog Networks

Abstract

Fog computing provides distributed processing and storage solutions for real-time applications that demand low latency and fast response times. This makes fog solutions suitable for internet-of-things (IoT) devices that request various network functions. To offer multiple functions for IoT applications, fog nodes can leverage network function virtualization (NFV) for a scalable and elastic function modification, i.e., without the need for dedicated hardware. Despite the saliencies achieved from the synergistic combination between fog and NFV technologies, a key challenge here is the limited resources at the fog nodes. This makes the latter susceptible to rapid node saturation and network congestion at high traffic volumes. Along this, efficient resource utilization and load distribution mechanisms are necessary to enhance admission rates and quality-of-service (QoS). Hence, this paper proposes novel load migration schemes for NFV-based fog networks that aim to reduce the overhead of the migration process. Namely, a coarse-grained load diffusion scheme is adopted to reduce migration frequencies, incurred delay, and cost. Further, destination nodes are selected based on least-load (LL) or least-delay (LD) mechanisms to accommodate delay-sensitive, delay-tolerant, and computation-intensive IoT applications.