Multi-objective fuzzy approach to scheduling and offloading workflow tasks in Fog–Cloud computing

Mokni, Marwa; Yassa, Sonia; Hajlaoui, Jalel Eddine; Omri, Mohamed Nazih; Chelouah, Rachid

doi:10.1016/j.simpat.2022.102687

Cited by 37 publications

(10 citation statements)

References 15 publications

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“…• Heterogeneity in both computational resources and applications enhances both the solution space and optimal solution yield time in cloud workflow scheduling as an NP-hard problem, whereby metaheuristic algorithms have become well known in solving this problem [4,5]. Every metaheuristic algorithm has its strengths and limitations, and both affect the cloud workflow scheduling processes.…”

Section: Motivationmentioning

confidence: 99%

A Fault-tolerant Scheduling Strategy through Proactive and Clustering Techniques for Scientific Workflows in Cloud Computing

Farhood,

Khorsand,

Hussein

et al. 2024

Preprint

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Scientific workflow scheduling allocates many fine computational granularity tasks to the best appropriate cloud resources. The prevalence of failures in cloud computing is augmented by the substantial quantity of servers and components burdened with resource-intensive workloads. In addition, workflow tasks may face a higher failure risk than a job with the single task. To mitigate the likelihood of these potential failures, the workflow scheduling system should exhibit fault tolerance. In this paper, a fault-tolerant scheduling strategy through proactive and clustering techniques for scientific workflows is proposed in cloud computing. First, the problem of task clustering is formulated by combining several short-duration tasks into a single job to minimize scheduling overhead and enhance the runtime performance of workflow executions. Then, an autonomous framework for workflow scheduling is introduced based on the MAPE-K control model with four essential steps: monitoring, analyzing, planning, and executing, all supported by a shared knowledge base. In the monitoring step, clustered jobs and capabilities of available cloud resources are monitored. In the analyzing step, the failure prediction accuracy is increased by applying the group method of data handling (GMDH) neural network before fault /failure occurrence. In the planning step, (1) the reliability of application execution is assured through a re-clustering technique after fault /failure occurrence; (2) a new hybrid multi-objective algorithm is proposed based on MOPSO and adaptive SA, called MOPSO-aSA, to facilitate workflow scheduling in faulty execution environments. Last, according to the experimental results, it can be concluded that the suggested strategy outperforms other approaches in terms of makespan, total cost, energy consumption, and failure rate.

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Section: Motivationmentioning

confidence: 99%

A Fault-tolerant Scheduling Strategy through Proactive and Clustering Techniques for Scientific Workflows in Cloud Computing

Farhood,

Khorsand,

Hussein

et al. 2024

Preprint

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“…The main motive of this optimization is to maximize the number of task assignments in the Fog network (instead of the cloud) to meet the requirements of user applications. In [22], the authors suggested a method for creating an environment that integrates scheduling, sequencing, and partitioning algorithms while ensuring a multi-objective optimization of the competing needs of users and providers. A dynamicthreshold-based task scheduling technique was presented to reduce the transmission and energy consumption of the IoT devices [23].…”

Section: Task Scheduling Based On Cost and Power Consumptionmentioning

confidence: 99%

Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks

Baskar,

Periasamy

2024

Future Internet

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The exponential growth in data traffic in the real world has drawn attention to the emerging computing technique called Fog Computing (FC) for offloading tasks in fault-free environments. This is a promising computing standard that offers higher computing benefits with a reduced cost, higher flexibility, and increased availability. With the increased number of tasks, the occurrence of faults increases and affects the offloading of tasks. A suitable mechanism is essential to rectify the faults that occur in the Fog network. In this research, the fault-tolerance (FT) mechanism is proposed based on cost optimization and fault minimization. Initially, the faulty nodes are identified based on the remaining residual energy with the proposed Priority Task-based Fault-Tolerance (PTFT) mechanism. The Minimum-Cost Neighbour Candidate Node Discovery (MCNCND) algorithm is proposed to discover the neighbouring candidate Fog access node that can replace the faulty Fog node. The Replication and Pre-emptive Forwarding (RPF) algorithm is proposed to forward the task information to the new candidate Fog access node for reliable transmission. These proposed mechanisms are simulated, analysed, and compared with existing FT methods. It is observed that the proposed FT mechanism improves the utilization of an active number of Fog access nodes. It also saved a residual energy of 1.55 J without replicas, compared to the 0.85 J of energy that is used without the FT method.

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“…Ranumayee et al [43] used the evolutionary learning method to optimize energy, makespan, and cost and schedule tasks in the IoT-fog-cloud network. Mokni et al [44] have used the multi-objective fuzzy method to offload workflow in the fog-cloud network. Ranumayee et al [45] have used the WOA in order to allocate optimal resources and schedule efficient tasks in the IoT-fog-cloud network.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

Liu¹,

Kosarirad

Meisami

et al. 2023

Processes

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Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks.

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Multi-objective fuzzy approach to scheduling and offloading workflow tasks in Fog–Cloud computing

Cited by 37 publications

References 15 publications

A Fault-tolerant Scheduling Strategy through Proactive and Clustering Techniques for Scientific Workflows in Cloud Computing

A Fault-tolerant Scheduling Strategy through Proactive and Clustering Techniques for Scientific Workflows in Cloud Computing

Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks

An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

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