Resilience and Load Balancing in Fog Networks: A Multi-Criteria Decision Analysis Approach

TL;DR

This paper introduces a novel ELECTRE-based multi-criteria decision analysis method for load balancing in Fog Computing, significantly improving system performance and resilience compared to traditional algorithms.

Contribution

It is the first to apply ELECTRE MCDA for load balancing in Fog environments, considering multiple objectives for service selection.

Findings

Up to 67% performance improvement over baseline methods

Effective load distribution in heterogeneous Fog setups

Enhanced resilience and service availability

Abstract

The advent of Cloud Computing enabled the proliferation of IoT applications for smart environments. However, the distance of these resources makes them unsuitable for delay-sensitive applications. Hence, Fog Computing has emerged to provide such capabilities in proximity to end devices through distributed resources. These limited resources can collaborate to serve distributed IoT application workflows using the concept of stateless micro Fog service replicas, which provides resiliency and maintains service availability in the face of failures. Load balancing supports this collaboration by optimally assigning workloads to appropriate services, i.e., distributing the load among Fog nodes to fairly utilize compute and network resources and minimize execution delays. In this paper, we propose using ELECTRE, a Multi-Criteria Decision Analysis (MCDA) approach, to efficiently balance the load in Fog environments. We considered multiple objectives to make service selection decisions, including compute and network load information. We evaluate our approach in a realistic unbalanced topological setup with heterogeneous workload requirements. To the best of our knowledge, this is the first time ELECTRE-based methods are used to balance the load in Fog environments. Through simulations, we compared the performance of our proposed approach with traditional baseline methods that are commonly used in practice, namely random, Round-Robin, nearest node, and fastest service selection algorithms. In terms of the overall system performance, our approach outperforms these methods with up to 67% improvement.