Fog vs. Cloud Computing: Should I Stay or Should I Go?

Abstract

In this article, we work toward the answer to the question “is it worth processing a data stream on the device that collected it or should we send it somewhere else?”. As it is often the case in computer science, the response is “it depends”. To find out the cases where it is more profitable to stay in the device (which is part of the fog) or to go to a different one (for example, a device in the cloud), we propose two models that intend to help the user evaluate the cost of performing a certain computation on the fog or sending all the data to be handled by the cloud. In our generic mathematical model, the user can define a cost type (e.g., number of instructions, execution time, energy consumption) and plug in values to analyze test cases. As filters have a very important role in the future of the Internet of Things and can be implemented as lightweight programs capable of running on resource-constrained devices, this kind of procedure is the main focus of our study. Furthermore, our visual model guides the user in their decision by aiding the visualization of the proposed linear equations and their slope, which allows them to find if either fog or cloud computing is more profitable for their specific scenario. We validated our models by analyzing four benchmark instances (two applications using two different sets of parameters each) being executed on five datasets. We use execution time and energy consumption as the cost types for this investigation.

OffFog: An Approach to Support the Definition of Offloading Policies on Fog Computing

“…Alelaiwi [13] and Pisani et al [14] use the data offloading process to meet the requirements of their applications better. Thus, these authors consider strategies and policies that set the course for such a process, serving to mitigate the limitations of the fog environment.…”

“…In this approach, the offloading of edge data is only performed after carrying out deep learning techniques. Pisani et al [14] consider data that needs to be balanced across fog nodes to data offloading according to available storage. However, these studies do not discuss criteria related to the volume and nature of the data.…”

Leveraging Constrained Devices for Custom Code Execution in the Internet of Things

“…With petabytes of data being produced by IoT devices every day, the sheer volume of information will make transmitting every single byte to the cloud prohibitively expensive in terms of both time and money [Pisani et al 2019]. Furthermore, moving data streams from sensor nodes to servers will also have an impact on the energy consumption of devices that may have constricted power budgets (e.g., devices that are batteryoperated or depend on limited energy sources such as solar power).…”

Level control of coupled tanks system based on neural network techniques

“…In this chapter [69], we work toward the answer to the question "is it worth processing a data stream on the device that collected it or should we send it to be processed by more powerful and distant devices?". As it is often the case in Computer Science, the response is "it depends".…”

“…Even though storage space on the cloud can be scaled according to the needs of its users, with petabytes of data being produced by IoT devices every day, the sheer volume of information will make transmitting every single byte to the cloud prohibitively expensive in terms of both time and money [69]. Furthermore, moving data streams from sensor nodes to servers will not only present the aforementioned costs, but also have an impact on the energy consumption of devices that may have constricted power budgets (e.g., devices that are battery-operated or depend on limited energy sources such as solar power).…”