A systematic review of task scheduling approaches in fog computing

Abstract

Due to increased use of IoT devices and data sensors a huge amount of data is being produced for processing in real-time. Fog computing has evolved as a solution for fast processing of data. To complete the data processing as per requirement of user, it is processed at fog nodes which are near the user. To complete work in a specified time with limited resources, task scheduling is performed. With the increased amount of data to be processed, the completion of task within given time in fog computing is a major challenge. So, scheduling of tasks and resources is a very important issue. A lot of research has been undertaken in recent years. In this survey, authors have reviewed various task scheduling algorithms suggested by researchers to meet the user requirements.The focus of this article is on diverse scheduling techniques being deployed in fog computing. An effort has been made to classify existing approaches, research issues and determine significant issues existing in this field. There are four major scheduling categories that are used by researchers namely, static, dynamic, heuristic, and hybrid. As per this study, 17% researchers deployed static, 23% dynamic, 47% heuristic, and 13% hybrid approaches, respectively.Analysis shows that for QoS (Quality of Service) parameters 19% researchers are focused on response time, 18% on cost and energy consumption, and 16% on makespan. For QoS parameter other factors have much smaller contribution in comparison to the above factors. In the tools used, researchers observed that 40% of the researches have used iFogSim, according to literature. Besides, this research article also discusses open issues and future work in the field of fog computing. This article also underlines various open issues and future directions in the field of fog computing.

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

“…These servers are faster at executing IoT device requests and have less delay in processing requests, and thus this technology is beneficial for tasks that are sensitive to delay. 6,7 Fog Computing differs from other computational methods due to the following features 4 :…”

“…2 Fog computing has attracted a great deal of attention to respond to location awareness and real-time response needs in different applications. 4,5 Nonetheless, fog devices have a nearly limited power supply, communication resources, and computing resources compared to cloud computing. Accordingly, solutions meeting real needs must be devised to address these challenges.…”

A Hybrid Particle Whale Optimization Algorithm for Workflow Scheduling in Cloud-Fog-Mobile Computing Environment

“…In this field, a lot of research work has been discussed by the researchers but still, there are many unresolved issues related to workflow scheduling algorithms. A variety of research papers have been studied to highlight the issues such as parameters, hybridization, scheduling categories [10], workflow, resources types, environment, and simulation tools used. Researchers have implemented many hybrid meta-heuristic type algorithms to solve the abovementioned issues but finding a universal solution to these problems is a challenging task.…”

“…The constant b is used to define the shape of a logarithmic spiral. D' and l will be calculated using equations (10) and (11).…”

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

“…The guidelines in [1], [17], [35] were followed in this study. We aim to have a comprehensive comparison to examine the limitations and possibilities of previous research and to have a systematic determination and taxonomy classification of the evidence on fuzzy theory in FC.…”