Abstract
The fast-paced development of power systems necessitates the smart grid (SG) to facilitate real-time control and monitoring with bidirectional communication and electricity flows. In order to meet the computational requirements for SG applications, cloud computing (CC) provides flexible resources and services shared in network, parallel processing, and omnipresent access. Even though CC model is considered to be efficient for SG, it fails to guarantee the Quality-of-Experience (QoE) requirements for the SG services, viz. latency, bandwidth, energy consumption, and network cost. Fog Computing (FC) extends CC by deploying localized computing and processing facilities into the edge of the network, offering location-awareness, low latency, and latency-sensitive analytics for mission critical requirements of SG applications. By deploying localized computing facilities at the premise of users, it pre-stores the cloud data and distributes to SG users with fast-rate local connections. In this paper, we first examine the current state of cloud based SG architectures and highlight the motivation(s) for adopting FC as a technology enabler for real-time SG analytics. We also present a three layer FC-based SG architecture, characterizing its features towards integrating massive number of Internet of Things (IoT) devices into future SG. We then propose a cost optimization model for FC that jointly investigates data consumer association, workload distribution, virtual machine placement and Quality-of-Service (QoS) constraints. The formulated model is a Mixed-Integer Nonlinear Programming (MINLP) problem which is solved using Modified Differential Evolution (MDE) algorithm. We evaluate the proposed framework on real world parameters and show that for a network with approximately 50% time critical applications, the overall service latency for FC is nearly half to that of cloud paradigm. We also observed that the FC lowers the aggregated power consumption of the generic CC model by more than 44%.
Highlights
A smart grid (SG) is a pervasive network of densely distributed energy and resource-limited wireless things, all capable of gathering and transferring in real-time large volumes of heterogeneous environmental data
While cloud resources are usually placed in centralized data centers, the Fog Computing Nodes (FCN) may be distributed in a rather wider area having heterogeneous network topology, making it more important to take into account data transfer times and cost in Fog Computing (FC)
The distinguishing geo-distributed intelligence provided by FC deployments make it more viable for security constrained services as the critical and sensitive tasks are selectively processed on local fog nodes and are kept within the user control, instead of offloading the whole universe of datasets into the vendor regulated mega data centers
Summary
A smart grid (SG) is a pervasive network of densely distributed energy and resource-limited wireless things (e.g., smart devices), all capable of gathering and transferring in real-time large volumes of heterogeneous environmental data. Fog computing (FC) is a distributed computing framework installed on the intermediary network switches, devices sensors and machines to eliminate the connectivity, bandwidth, and latency issues prevalent in CC. The result is a smart network of devices that are able to make decisions themselves and react in real-time to a changing environment or supply chain such as SG. A cost-efficient optimization framework for cumulative assessment of user to Fog Computing Node (FCN) association, workload allocation, and VM placement constraints, is proposed towards viable deployment of FC.
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