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

Yin, Zhenyu; Xu, Fulong; Li, Yue; Fan, Chao; Zhang, Feiqing; Han, Guangjie; Bi, Yuanguo

doi:10.3390/s22041555

Cited by 23 publications

(6 citation statements)

References 48 publications

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“…In [28], task scheduling was formulated as a multiobjective problem using task priority to reduce delay and energy consumption. The improved variants ACO and Monarch Butterfly Optimization were combined for scheduling of independent tasks.…”

Section: Related Workmentioning

confidence: 99%

Ripple-Induced Whale Optimization Algorithm for Independent Tasks Scheduling on Fog Computing

Ali Khan,

Abdul Aziz

2024

IEEE Access

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Due to the revolution of Internet of Things (IoT), the amount of data generation has been redoubling, leading to higher latency and network traffic. This has resulted in delays in services and increased energy consumption of cloud servers. Fog computing tackles the issues associated with long geographical distance between end-users and cloud servers by extending service provision closer to the network edge, reducing latency and makespan, and optimizing energy consumption during workload execution. Instead of offloading all tasks to the cloud, delay-sensitive tasks are executed at fog nodes, while others are offloaded to the cloud. However, the resources at the fog layer are limited, posing a challenge for task scheduling in fog computing, particularly as a multi-objective optimization problem. Meta-heuristic algorithms have been potent to find an optimal solution for such problems within a reasonable amount of time. The Whale Optimization Algorithm (WOA) is a relatively new meta-heuristic algorithm that has received significant attention from researchers due to its impressive optimization characteristics. However, being an exploitationoriented technique, it falls into local optima due to a lack of generating new solutions over time. Limited exploration capabilities also compromise the diversity of the solution space and prolong convergence time. Therefore, in this study, an enhanced Ripple-induced Whale Optimization Algorithm (RWOA) is proposed, utilizing ripple effects to schedule independent tasks in fog computing. It aims to minimize makespan and energy consumption while maximizing throughput in a fog-cloud infrastructure by improving poor solutions through substantial changes. Extensive simulations are performed to assess the effectiveness of the proposed algorithm. The proposed RWOA outperformed TCaS, HFSGA, MGWO, and WOAmM on two workload datasets: Random and NASA Ames iPSC. The statistical significance of the results is validated by the Friedman test and Wilcoxon Signed-rank test.

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Section: Related Workmentioning

confidence: 99%

Ripple-Induced Whale Optimization Algorithm for Independent Tasks Scheduling on Fog Computing

Ali Khan,

Abdul Aziz

2024

IEEE Access

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“…Yet, the corresponding energy consumption was not discussed. Another study [114], formulated a multi-objective task scheduling problem based on task priority to minimize energy consumption and delay. ACO and Monarch Butterfly Optimization (MBO) were individually enhanced and subsequently hybridized to schedule independent tasks to minimize task completion rate in a real time environment.…”

Section: ) Hybrid Variants Of Ant Colony Optimization and Genetic Alg...mentioning

confidence: 99%

A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions

Khan,

Aziz,

Osman

et al. 2023

IEEE Access

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Efficient task scheduling on the cloud is critical for optimal utilization of resources in data centers. It became even more challenging with the emergence of 5G and IoT applications that generate massive number of tasks with stringent latency requirements. This gives birth to fog/edge computing -a complementary layer to cloud. Tasks latency in fog computing can be reduced as processing in the network is done closer to the end devices and users, but every task cannot be scheduled in the fog due to limited resources availability. Conventional scheduling algorithms often fail to exploit the heterogeneous resources; therefore, specially designed and well-tuned scheduling algorithms are desired for achieving better quality of service. In this study, the state-of-the-art task scheduling algorithms in the cloud and fog environments are investigated in a diverse set of dimensions. Among the relevant studies published between 2018-2022 and indexed in the Web-of-Science (WOS), SCOPUS, and Google Scholar databases, eighteen studies are selected for both the cloud and fog domains from WOS and Scopus, while seventeen studies are chosen for both the cloud and fog domains from Google Scholar. Thus, a total of 106 studies are included in this survey for the detail investigation. The scheduling algorithms are broadly classified into three categories such as heuristic, meta-heuristic, and hybrid meta-heuristic followed by detailed critical analysis. It has been observed that most of the scheduling algorithms are dynamic and non-preemptive in nature, while the higher fraction of the tasks is independent in comparison to bag of tasks and workflows. Similarly, 97% of the studies focus on multiple objectives and 68% of the techniques are non-deterministic. Further, a total of twenty different scheduling objectives are identified with makespan, resource utilization, delay, load balancing, and energy consumption as the most significant metrics. The evaluation methods including simulations (51%), real experiments (4%), analytical equations (2%), and datasets (43%) etc. are surveyed. At the end, the open issues, challenges, and future directions are argued. Cloud computing, fog computing, task scheduling, workflow, Internet of Things (IoT). INDEX TERMS I. INTRODUCTION Cloud computing offers different kinds of scalable resources to end users based on pay-per-use basis by providing costThe associate editor coordinating the review of this manuscript and approving it for publication was Zhiwu Li . savings, security, flexibility, and automatic software updates etc. The users can concentrate on their personal and business objectives without bothering about the infrastructure and supporting services as the vendor is responsible for managing it. There are three service models and four deployment models provided by the cloud providers [1]. Though it is

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“…The MGWO algorithm performs in terms of make time and resource utilization, and experimental results suggest that it operates well in dynamic circumstances. Yin et al (2022) proposed a method for scheduling tasks in cloud-fog computing environments based on combining the monarch butterfly optimization algorithm and the improved ant colony optimization algorithm (HMA). To identify near-optimal solutions, the authors framed the scheduling problem as an optimization task and used HMA to solve it.…”

Section: Literature Surveymentioning

confidence: 99%

AGWO: Cost Aware Task Scheduling in Cloud Fog Environment Using Hybrid Metaheuristic Algorithm

Medishetty,

2023

IJERR

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In IoT concepts, efficient approaches like cloud-fog computing are emerging, enhancing system benefits. The performance and output of such frameworks can be greatly improved by optimized scheduling of Internet of Things (IoT) task requests. This study presents a novel technique for scheduling Internet of Things requests in a cloud-fog environment, based on an adaption of ant grey wolf optimisation (AGWO). By combining the operators of ant colony optimisation (ACO) and grey wolf optimisation (GWO), AGWO aims to improve the speed and quality of ACO's solution discovery. The suggested AGWO approach is evaluated using numerous datasets of varying sizes, both synthetic and real-world. The effectiveness of AGWO is further investigated by comparing it to standard metaheuristic methods. The experimental results demonstrate that AGWO is superior to competing strategies in terms of makespan time and cost while resolving the task scheduling problem.

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

Cited by 23 publications

References 48 publications

Ripple-Induced Whale Optimization Algorithm for Independent Tasks Scheduling on Fog Computing

Ripple-Induced Whale Optimization Algorithm for Independent Tasks Scheduling on Fog Computing

A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions

AGWO: Cost Aware Task Scheduling in Cloud Fog Environment Using Hybrid Metaheuristic Algorithm

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