Modeling and simulation of smart grid-aware edge computing federations

Abstract

Compute-intensive Internet of Things (IoTs) applications have led to the edge computing paradigm. Edge computing decentralizes the IT infrastructure in multiple edge data centers (EDCs) across the access networks to reduce latency and network congestion. Edge computing can benefit significantly from different aspects of smart grids to achieve lower energy consumption and greater resilience to electricity price fluctuations. This paper presents a modeling, simulation, and optimization (M&S&O) framework for analyzing and dimensioning smart grid-aware edge computing federations. This tool integrates aspects of a consumer-centric smart grid model to the resource management policies of the EDCs. To illustrate the benefits of this tool, we show a realistic case study for optimizing the energy consumption and operational expenses of an edge computing federation that provides service to a driver assistance IoT application. Results show that this approach can reduce the daily energy consumption by 20.3% and the electricity budget by 30.3%.

Edge Offloading in Smart Grid

“…The challenge is to make optimal computational orchestration decisions under uncertain and dynamic conditions [5] given by edge resource capacity demand (e.g., bandwidth and memory), failures (e.g., data network link), the latency of the network, energy consumption of resources, and lifecycle activities of applications. Automation is a key aspect in managing edge-offloading solutions in smart grids and is facilitated by recent advancements in application virtualization, semantic integration, and the data connectivity of edge devices [6].…”