A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions

Kaur, Navjeet; Kumar, Ashok; Kumar, Rajesh

doi:10.1002/cpe.6432

Cited by 42 publications

(18 citation statements)

References 90 publications

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“…The task scheduling problem in cloud-fog computing has difficulty obtaining the optimal solution in polynomial time due to the many variables and constraints in the objective function [41]. To minimize the delay of all tasks and reduce the energy consumption of nodes, we combined the advantages of the MBO and ACO to design a hybrid heuristic algorithm.…”

Section: Task Scheduling Algorithm Designmentioning

confidence: 99%

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

Yin

et al. 2022

Sensors

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With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.

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Section: Task Scheduling Algorithm Designmentioning

confidence: 99%

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

Yin

et al. 2022

Sensors

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“…Cloud technology is described as an online service available to users at any time and in any location, involving, and variable capabilities. Cloud technology meets client requirements for programs, data, sharing facilities, and various equipment at the specified time, making services on request [1][2]. For web systems, most of the information requiring calculations, analysis, and storage has been transferred to internet services, which can affect latency, security, portability, and reliability [3].…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Hybrid Load Balancing Based Optimization Algorithm for Improving Fog Computing Performance

Gowri

Baranidharan

2023

Preprint

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Load balancing (LB) is a key characteristic that more or less prevents fog computing devices by regulating energy consumption, cost, speed, and latency. In this cloud environment, all current LB technologies are rarely effective. The fog computing system is now unable to predict the appearance of client inquiries about this confusing management. Because each system has its own set of functions, scheduling tasks across networks become an extremely challenging task. Many cloud experts recently switched to LB. This means resource administration which produces several methods for achieving such a goal including the performance measures, a proposed technique of improving fog computing capacity, power consumption, and computing cost. The above method is used in a fog computing state to improve energy consumption and response speed using a hybrid optimization method of Oppositional Sparrow Search with Gravitational Search Algorithm (OSS-GSA). The proposed technology is adaptable and fast and could be used to improve overall energy efficiency while reducing energy consumption. The hybrid proposal OSS-GSA optimization method used to improve LB performance measurements such as improving energy efficiency and overall cost occurrence has been reduced in fog environments.

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“…The data collected by IoT sensors is sent to nearby Fog computing nodes for execution instead of sending it to multi‐hop away cloud infrastructure 5 . The huge amount of data generated by billions of geo‐distributed IoT devices/appliances cause network congestion, high latency, high energy consumption, and poor quality of service (QoS).…”

Section: Introductionmentioning

confidence: 99%

“…The data collected by IoT sensors is sent to nearby Fog computing nodes for execution instead of sending it to multi-hop away cloud infrastructure. 5 The huge amount of data generated by billions of geo-distributed IoT devices/appliances cause network congestion, high latency, high energy consumption, and poor quality of service (QoS). Further, the Fog computing nodes have different architectures and are resource-constrained in terms of processing/computing power, bandwidth as well as storage capacity.…”

mentioning

confidence: 99%

CaPTS scheduler: A context‐aware priority tuple scheduling for Fog computing paradigm

Islam

Kumar

2022

Trans Emerging Tel Tech

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With the increase in real‐time latency‐sensitive Internet of Things (IoT) applications, a huge amount of data is generated in the Fog‐IoT paradigm. There is a need to schedule and execute this huge workload over Fog devices efficiently to support the increasing demand of these applications. But, Fog devices are resource‐constrained in terms of processing/computing power, bandwidth as well as storage capacity which makes tuple scheduling a challenging problem. Moreover, due to the rise in IoT devices per application, a sharp increase in service response time, network congestion, and inefficiency in terms of energy consumption, and execution cost has been observed. Consequently, an efficient tuple scheduling algorithm is desirable that can reduce latency and network usage and optimize energy consumption and cost. Therefore, in this work, CaPTS scheduler: A Context‐aware Priority Tuple Scheduling for Fog computing paradigm is designed and proposed. It takes into consideration various context‐aware parameters such as task load of application, networking requirement, and data flow rate to set the priority of tuples and schedule them across Fog computing nodes while ensuring quick service response time and satisfying quality of service requirements of end‐users. The CaPTS scheduler is implemented and evaluated using iFogSim toolkit on various performance metrics such as latency, network usage, energy consumption, and cost. Its performance is validated through a case study on the smart mining industry system. The results show that on an average the latency and network usage are minimized by 35.93% and 44.20%, while energy consumption and cost are optimized by 4.55% and 30.92%, respectively, in comparison with baseline techniques.

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A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions

Cited by 42 publications

References 90 publications

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

Multi Objective Hybrid Load Balancing Based Optimization Algorithm for Improving Fog Computing Performance

CaPTS scheduler: A context‐aware priority tuple scheduling for Fog computing paradigm

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