Task Scheduling Mechanisms for Fog Computing: A Systematic Survey

Abstract

In the Internet of Things (IoT) ecosystem, some processing is done near data production sites at higher speeds without the need for high bandwidth by combining Fog Computing (FC) and cloud computing. Fog computing offers advantages for real-time systems that require high speed internet connectivity. Due to the limited resources of fog nodes, one of the most important challenges of FC is to meet dynamic needs in real-time. Therefore, one of the issues in the fog environment is the optimal assignment of tasks to fog nodes. An efficient scheduling algorithm should reduce various qualitative parameters such as cost and energy consumption, taking into account the heterogeneity of fog nodes and the commitment to perform tasks within their deadlines. This study provides a detailed taxonomy to gain a better understanding of the research issues and distinguishes important challenges in existing work. Therefore, a systematic overview of existing task scheduling techniques for cloud-fog environment, as well as their benefits and drawbacks, is presented in this article. Four main categories are introduced to study these techniques, including machine learningbased, heuristic-based, metaheuristic-based, and deterministic mechanisms. A number of papers are studied in each category. This survey also compares different task scheduling techniques in terms of execution time, resource utilization, delay, network bandwidth, energy consumption, execution deadline, response time, cost, uncertainty, and complexity. The outcomes revealed that 38% of the scheduling algorithms use metaheuristicbased mechanisms, 30% use heuristic-based, 23% use machine learning algorithms, and the other 9% use deterministic methods. The energy consumption is the most significant parameter addressed in most articles with a share of 19%. Finally, a number of important areas for improving the task scheduling methods in the FC in the future are presented.INDEX TERMS Fog computing, cloud computing, task scheduling, methods, quality of service.

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

“…However, limited resources in fog nodes necessitate efficient task scheduling to meet dynamic demands. This survey analyzes existing techniques, categorized into machine learning, heuristic, metaheuristic, and deterministic approaches, evaluating them based on execution time, resource utilization, and various other parameters is presented by Hosseinzadeh et al ( 2023 ). It reveals that metaheuristic-based methods are most common (38%), followed by heuristic (30%), machine learning (23%), and deterministic (9%).…”

Machine learning-driven task scheduling with dynamic K-means based clustering algorithm using fuzzy logic in FOG environment

“…The many scheduling methods now in use may be placed in one of three major categories: stochastic (Bhaumik et al, 2016;Guo, 2017), deterministic (Hosseinzadeh et al, 2023;Reddy and Sudhakar, 2023), or hybrid (Saif et al, 2023;Tran-Dang and Kim, 2023). Non-deterministic algorithms, often known as stochastic algorithms, are those that create a result depending on a degree of randomness and an objective function.…”

“…Memory and CPU utilization indicators have been included in their load utilization model, however the time aspect has not been taken into consideration. Consumption of both energy and time has been regarded as the most important elements in the research carried out by Hosseinzadeh et al (2023). The authors have successfully resolved the issues with scheduling and commitment by using the fireworks method.…”

A greedy randomized adaptive search procedure for scheduling IoT tasks in virtualized fog–cloud computing

“…Therefore, task scheduling methods that simultaneously optimize the resource utilization of fog and cloud resources and the QoS requirements of IoT activities have gained a great deal of attention in recent years. [15][16][17][18] Since task scheduling optimization is known as an NP-hard problem, numerous heuristic-, metaheuristic-, and machine learning-based approaches have been developed to solve it by considering different optimization goals. For example, in Reference 19, efficient heuristic rules and a priority-aware algorithm are respectively proposed for the local decision-making process and scheduling of the offloaded tasks among edge cloud servers in mobile edge computing environments.…”