Prioritized task scheduling in fog computing

CHOUDHARI, TEJASWINI; Moh, Melody; Moh, Teng-Sheng

doi:10.1145/3190645.3190699

Cited by 114 publications

(52 citation statements)

References 12 publications

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“…The applications where cloud computing is found as not enough viable, fog computing is used. This specific distributed approach helps to address the industrial IoT and IoT requirements [70]. The issues of extra time consumptions and smart sensor operators get resolved with implementation of fog computing in the education IoT system.…”

Section: Current Methodsmentioning

confidence: 99%

Utilizing technologies of fog computing in educational IoT systems: privacy, security, and agility perspective

Adel

2020

J Big Data

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Fog computing architecture is referred to the architecture that is distributed over the geographical area. This architectural arrangement mainly focuses on physical and logical network elements, and software for the purpose of implementing proper network. Fog computing architecture allows the users to have a flexible communication and also ensures that the storage services are maintained efficiently for the purpose of managing the data. However, it has been observed that in the field of education fog computing architecture has gained huge importance due to its real time application feature. The main objective of the survey is to develop a systematic literature review for the technology of fog computing in the education IoT system. The survey will also focus on evaluating the essential factors that has a crucial role in the fields of education as well as investigating the limitation and findings associated with the fog computing technologies in educational systems from the perspective of privacy, security, and agility.

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Section: Current Methodsmentioning

confidence: 99%

Utilizing technologies of fog computing in educational IoT systems: privacy, security, and agility perspective

Adel

2020

J Big Data

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“…[12]. The fog computing includes the virtualized layer which is located between end-users and the cloud data centers context [13]. There are numerous advantages of the cloud and fog-based model which reduces latency, reduced networking traffic, and improved energy efficiency.…”

Section: Figure 1 Evolution Of Industry Ages [2]mentioning

confidence: 99%

A Secure Industrial Internet of Things (IIoT) Framework for Resource Management in Smart Manufacturing

Abuhasel

Khan

2020

IEEE Access

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The Industrial Internet of Things (IIoT) can transform an existing isolated industrial system to a connected network. The IIoT and the related wireless connectivity requirements for industrial sensors are very significant. The deployed sensors in IIoT monitors the conditions of the industrial devices and machines. Therefore, reliability and security become the most important concerns in IIoT. This introduces many familiar and ever-increasing risks associated with the industrial system. The IIoT devices can be vulnerable to vast array of viruses, threats, and attacks. Therefore, an efficient protection strategy is required to ensure that the millions of IIoT devices are safe from these risks. However, resource constraint IIoT devices have not been designed to have effective security features. Due to this, in recent years, cloud, fog, and edge-based IIoT has received great attention in the research community. The computationally intensive tasks such as security, data analytics, decision making, and reporting are performed at the cloud or fog using a powerful computing infrastructure. The data security of the IIoT device has been provided by employing improved Rivest-Shamir-Adelman (RSA) and hash signatures. The proposed RSA algorithm has a four-prime number of 512-bits. The device authentication is performed by employing a hash signature. For long network life, an efficient clustering technique for the sensor devices which is based on node degree(N), distance from the cluster(D), residual energy(R), and fitness (NDRF) has been proposed. The fitness of the sensor nodes is computed using the Salp swarm algorithm (SSA). In order to reduce the latency and communication overhead for IIoT devices a resource scheduling using SoftMax deep neural network (DNN) is proposed. All the requests coming from the cluster head are classified using SoftMax-DNN for best resource scheduling on the basis of storage, computing, and bandwidth requirements. The proposed framework produces superior results, especially in terms of energy consumption, latency, and strength of security.

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“…Choudhari et al 70 presented a task scheduling algorithm based on priorities in the fog computing environment. The fog layer involves FNs, and each node includes small data centers.…”

Section: Organization Of the Task Schedulingmentioning

confidence: 99%

“…In addition, in Choudhari et al, 70 the scheduling algorithm was simulated on cloud analyst simulator. To investigate the effects of priority over performance in scheduling algorithms, ORT (Optimize Response Time) and RD (Reconfigure Dynamic) algorithms, both supported by cloud analyst simulator, were compared in cloud‐only environment with and without priority, and then in the next phase, they inspected the effects of communication (cost and delay) between fog and cloud layers.…”

Section: Organization Of the Task Schedulingmentioning

confidence: 99%

Task scheduling approaches in fog computing: A systematic review

Alizadeh

Khajehvand

Rahmani

et al. 2020

Int J Communication

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SummaryThe Internet of Things (IoT) interconnects billions of physical objects to collect and exchange information and makes available various applications. Despite all the advantages of the IoT, some of its applications are not feasible because of the existing restrictions in the IoT sensors. To overcome these restrictions, cloud computing has been developed in recent years. Although cloud computing has removed some of these restrictions, the cloud itself has faced some other challenges. One of these challenges is the distance between the cloud and the end‐devices, which is not ideal for delay‐sensitive applications and also leads to high communication costs and security problems. To handle these challenges, the fog computing has been introduced in literature, which places the resources and services at the edge of the network, close to the end‐devices. On the other hand, fog nodes suffer from heterogeneity, uncertainty, and dispersion of resources, and also processing, storage, and memory limitations. Thus, there is need for a proper task scheduling approach to use these resources optimally. In this study, the task scheduling algorithms proposed by different researchers for the cloud‐fog environment, their advantages and disadvantages, and also various tools and issues regarding the scheduling methods and their restrictions were discussed. The results indicated that about 58% of the scheduling algorithms use static scheduling and that the other 42% use dynamic scheduling, and the delay metric is the most noteworthy parameter considered in most studies with a share of nearly 17%. Finally, we identified the open issues related to the task scheduling based on cloud‐fog computing and provided some directions for future works.

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Prioritized task scheduling in fog computing

Cited by 114 publications

References 12 publications

Utilizing technologies of fog computing in educational IoT systems: privacy, security, and agility perspective

Utilizing technologies of fog computing in educational IoT systems: privacy, security, and agility perspective

A Secure Industrial Internet of Things (IIoT) Framework for Resource Management in Smart Manufacturing

Task scheduling approaches in fog computing: A systematic review

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