Joint QoS-aware and Cost-efficient Task Scheduling for Fog-cloud Resources in a Volunteer Computing System

Hoseiny, Farooq; Azizi, Sadoon; Shojafar, Mohammad; Tafazolli, Rahim

doi:10.1145/3418501

Cited by 47 publications

(32 citation statements)

References 32 publications

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“…In this work, the dynamic voltage and frequency scaling (DVFS) technique is also run on each solution to save energy. Scheduling of IoT tasks with volunteer fog–cloud resources was proposed in Reference 48. First, the problem has been formulated as a mixed integer linear programming (MILP) model with the aim of optimizing the total cost of computation, communication, and delay violation.…”

Section: Related Workmentioning

confidence: 99%

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

Salimi,

Azizi,

Abawajy

2024

Trans Emerging Tel Tech

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Virtualized fog–cloud computing (VFCC) has emerged as an optimal platform for processing the increasing number of emerging Internet of Things (IoT) applications. VFCC resources are provisioned to IoT applications in the form of virtual machines (VMs). Effectively utilizing VMs for diverse IoT tasks with varying requirements poses a significant challenge due to their heterogeneity in processing power, communication delay, and energy consumption. In addressing this challenge, in this article, we propose a system model for scheduling IoT tasks in VFCCs, considering not only individual task deadlines but also the system's overall energy consumption. Subsequently, we employ a greedy randomized adaptive search procedure (GRASP) to determine the optimal assignment of IoT tasks among VMs. GRASP, a metaheuristic‐based technique, offers appealing characteristics, including simplicity, ease of implementation, a limited number of tuning parameters, and the potential for parallel implementation. Our comprehensive experiments evaluate the effectiveness of the proposed method, comparing its performance with the most advanced algorithms. The results demonstrate that the proposed approach outperforms the existing methods in terms of deadline satisfaction ratio, average response time, energy consumption, and makespan.

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Section: Related Workmentioning

confidence: 99%

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

Salimi,

Azizi,

Abawajy

2024

Trans Emerging Tel Tech

Self Cite

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“…Louail et al 30 designed and suggested a dynamic task scheduling approach for FNs in industrial IoT (IIoT) for smart factories aimed at minimizing the frequency of tasks and limiting the deadline based on the frequency of task‐performing. Hoseiny et al 31 proposed two efficient heuristic algorithms to minimize computational and communications costs as well as deadline violations in the systems of volunteer fog‐cloud computing. Almutairi and Aldossary 32 developed and suggested a fuzzy logic‐based approach for scheduling loaded tasks in cloud‐edge environments aimed at minimizing the overall service time of latency‐sensitive applications.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The number of the members of set Prob i = {P i 1 , P i 2 , … , P i m } is also equal to the number of FNs. The received task can be sent to each of the nodes with a known probability rate, and according to Equation (31), the sum of the probabilities of the actions is equal to 1 where P m represents the probability of action m, and m is a subset of actions that exist in set 𝜑.…”

Section: F I G U R Ementioning

confidence: 99%

The improvement of wavefront cellular learning automata for task scheduling in fog computing

Jassbi

Teymori

2023

Trans Emerging Tel Tech

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The rapid advancement of the “Internet of Things” (IoT) devices has led to the emergence of different types of IoT applications that need immediate response and low delay to operate. The emergence of fog computing has provided a proper platform to process fast‐emerging IoT applications. Nevertheless, to name the disadvantages of fog computing devices, it can be said that they are typically distributed, dynamic, and resource‐limited. Therefore, it seems a substantial challenge to schedule fog computational resources effectively to perform heterogeneous and delay‐sensitive IoT tasks. The problem of scheduling tasks aimed at minimizing the energy consumption of fog nodes is formulated in this article, while meeting the requirements of the quality of service (QoS) of IoT tasks, including response time. Minimizing the deadline time and balancing the network load are also considered in the mathematical model. In the next stage, a new algorithm is introduced based on a wavefront cellular learning automata (WCLA) called the wavefront cellular learning automata improved by genetic algorithm (WCLA + GA). WCLA + GA is indeed a modified version of WCLA that has been improved using the genetic algorithm. In this version, the WCLA reinforcement signal is regulated by a genetic algorithm that accelerates the automata convergence rate. WCLA + GA is then utilized to schedule fog tasks. Simulating the proposed method followed by comparing it with other methods demonstrates that WCLA + GA performs task scheduling significantly better in terms of response time, energy consumption, and percentage of tasks that meet their deadline.

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“…Cloud and fog nodes have different processing power values measured in MIPS, memory capacity values measured in Megabytes (MB), bandwidth values measured in Megabytes Per Second (MBPS), and resource usage costs represented in Grid Dollars (G$). G$ is a currency unit used in the simulation to substitute for real money using a predefined ratio [2,35]. The virtual charge can be easily mapped to any pricing model ( 18)…”

Section: Experimental Settingsmentioning

confidence: 99%

Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog Environment

2022

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IoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure on the resources of traditional cloud data centers. This prevents cloud data centers from fulfilling the requirements of IoT applications, particularly delay-sensitive applications. Fog computing is a relatively recent computing paradigm that extends cloud resources to the edge of the network. However, task scheduling in this computing paradigm is still a challenge. In this study, a semidynamic real-time task scheduling algorithm is proposed for bag-of-tasks applications in the cloud–fog environment. The proposed scheduling algorithm formulates task scheduling as a permutation-based optimization problem. A modified version of the genetic algorithm is used to provide different permutations for arrived tasks at each scheduling round. Then, the tasks are assigned, in the order defined by the best permutation, to a virtual machine, which has sufficient resources and achieves the minimum expected execution time. A conducted optimality study reveals that the proposed algorithm has a comparative performance with respect to the optimal solution. Additionally, the proposed algorithm is compared with first fit, best fit, the genetic algorithm, and the bees life algorithm in terms of makespan, total execution time, failure rate, average delay time, and elapsed run time. The experimental results show the superiority of the proposed algorithm over the other algorithms. Moreover, the proposed algorithm achieves a good balance between the makespan and the total execution cost and minimizes the task failure rate compared to the other algorithms. Graphical Abstract

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Joint QoS-aware and Cost-efficient Task Scheduling for Fog-cloud Resources in a Volunteer Computing System

Cited by 47 publications

References 32 publications

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

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

The improvement of wavefront cellular learning automata for task scheduling in fog computing

Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog Environment

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