Cost-Aware Task Scheduling in Fog-Cloud Environment

Nikoui, Tina Samizadeh; Balador, Ali; Rahmani, Amir Masoud; Bakhshi, Zeinab

doi:10.1109/rtest49666.2020.9140118

Cited by 36 publications

(20 citation statements)

References 28 publications

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“…However, more experiments on larger datasets are needed to validate the performance of the proposed algorithm under more difficult scenarios. In addition, the performance of the proposed algorithm should be compared to other real-time heuristic and metaheuristic scheduling algorithms to prove its worthiness in this category of scheduling algorithms [30].…”

Section: Task Scheduling In the Cloud-fog Computing Environmentmentioning

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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Section: Task Scheduling In the Cloud-fog Computing Environmentmentioning

confidence: 99%

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

2022

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“…Wang et al [33] • • eGA Abbasi et al [34] • NSGA2 Aburukba et al [35] • GA Ahmed et al [36] • GA Ali et al [37] • eNSGA2 Binh et al [38,39] • GA Hou et al [40] • eGA Li et al [41] • eGA Li et al [42] • Hyb. Liu et al [43] • eGA Ma et al [44] • eGA Mekki et al [45] • NSGA2 Nikoui et al [46] • GA Ren et al [47] • Hyb. Subramoney and Nyirenda [48] • Hyb.…”

Section: Task Schedulingmentioning

confidence: 99%

“…Nikoui et al [46] also propose using a classic GA. The main difference is that this proposal rejects the task execution when the deadline is not meet.…”

Section: Task Schedulingmentioning

confidence: 99%

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Genetic-based optimization in Fog Computing: current trends and research opportunities

Guerrero,

Lera,

Juiz

2021

Preprint

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Fog Computing is a new computational paradigm that emerges from the need of reducing the network usage and latency in Internet of Things (IoT). Fog can be understood as a continuum between the Cloud layer and the IoT users that allows to execute applications or store/process data in the networks devices of the infrastructure. This paper aims to review current trends in the use of Genetic Algorithm for the optimization of resource management in Fog architecture. Related papers are classified concerning the optimization scope and the genetic. Finally, future research lines are also presented.

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“…So in addition to CloudSim capabilities, it provides new classes to simulate edge and fog computing features. 185,186 Riverbed It is an event-driven simulator that can be used for modeling of distributed network systems. 187 Ptolemy II It is a Java-based simulation environment that is used to simulate sensor networks, and interaction between different components.…”

Section: P2psimmentioning

confidence: 99%

Internet of Things architecture challenges: A systematic review

Nikoui

Rahmani

Balador

et al. 2020

Int J Communication

Self Cite

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The Internet of Things (IoT) is a rapidly growing trend within many domains, such as automotive, avionics, automation, energy, and health. IoT architecture is the system of numerous elements, including sensors, protocols, actuators, cloud services, and layers. IoT architecture plays an important role to provide desired services. Nowadays, tens of IoT architectures are provided by the research community. Many challenges have been identified by the research community, including interoperability, security and privacy, reliability, energy constraints, scalability, and lack of common standards. However, to provide suitable IoT architecture, the importance and priority of requirements in different scenarios may vary, and requirement analysis should be regarded. To this end, this paper presents a systematic mapping survey to give a review of IoT architecture and provide a structured overview of research trends. Moreover, a technical taxonomy is presented for these challenges according to reviewed studies. This classification model can be used as a guideline for future works.

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Cost-Aware Task Scheduling in Fog-Cloud Environment

Cited by 36 publications

References 28 publications

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

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

Genetic-based optimization in Fog Computing: current trends and research opportunities

Internet of Things architecture challenges: A systematic review

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