Assessment of the Suitability of Fog Computing in the Context of Internet of Things

Abstract

This work performs a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Internet of Things (IoT), by mathematically formulating the parameters and characteristics of fog computing -one of the first attempts of its kind. With the rapid increase in the number of Internetconnected devices, the increased demand of real-time, low-latency services is proving to be challenging for the traditional cloud computing framework. Also, our irreplaceable dependency on cloud computing demands the cloud data centers (DCs) always to be up and running which exhausts huge amount of power and yield tons of carbon dioxide (CO2) gas. In this work, we assess the applicability of the newly proposed fog computing paradigm to serve the demands of the latency-sensitive applications in the context of IoT. We model the fog computing paradigm by mathematically characterizing the fog computing network in terms of power consumption, service latency, CO2 emission, and cost, and evaluating its performance for an environment with high number of Internet-connected devices demanding realtime service. A case study is performed with traffic generated from the 100 highest populated cities being served by eight geographically distributed DCs. Results show that as the number of applications demanding real-time service increases, the fog computing paradigm outperforms traditional cloud computing. For an environment with 50% applications requesting for instantaneous, real-time services, the overall service latency for fog computing is noted to decrease by 50.09%. However, it is mentionworthy that for an environment with less percentage of applications demanding for low-latency services, fog computing is observed to be an overhead compared to the traditional cloud computing. Therefore, the work shows that in the context of IoT, with high number of latency-sensitive applications fog computing outperforms cloud computing.

Computational Viability of Fog Methodologies in IoT Enabled Smart City Architectures-A Smart Grid Case Study

“…However, it is obvious that in the IoT architecture the population of connected entities will overshoot the current growth drift and will jeopardize the normal computing configurations [8]. This will in turn cause an exponential escalation in data generation, handling of which is key task to ensure viable implementation any data aware infrastructure.…”

“…Efficient and robust data analytics setup that can establish a real-time cum intelligent decision making atmosphere at every edge services becomes the need of hour. An exhaustive review of existing control paradigms reveal the presence centralized coordination strategies such as cloud computing, grid computing etc [8], [9]. However the service demands of IoT architecture reflect that there needs computing schemes that can execute locally at the edge itself.…”

Softwarization and Optimization for Sustainable Future Mobile Networks: A Survey

“…In FRAN, shown in Figure 3, in addition to the central cloud of CRAN, edge devices such as RRHs and UEs can be used for local signal processing, cooperative radio resource management and content storage [23] [30]. Such paradig m of using edge devices for computing and storage is called fog computing [31][32] [33] [34] and it helps to partly address the fronthaul and central cloud heavy burdens in CRAN. Fog co mputing is gaining increasing interest recently as a complementary technology to alleviate cloud computing drawbacks.…”

An Integrated Architecture for Software Defined and Virtualized Radio Access Networks with Fog Computing

“…New technologies, such as software defined networks (SDNs) [2], network virtualization [3], and fog computing [4], have emerged to tackle these problems. SDNs detach control plane from data plane.…”

“…The research on fog computing is in its infancy stage with most efforts to specify definitions, architecture, and user cases. In [4], fog computing was compared with cloud computing in the context of Internet of things (IoTs). In [14], the authors proposed an architecture of SDN-based fog computing for vehicular ad hoc networks.…”