A bi-objective task scheduling approach in fog computing using hybrid fireworks algorithm

Yadav, Ashish Mohan; Tripathi, Kuldeep Narayan; Sharma, S. C.

doi:10.1007/s11227-021-04018-6

Cited by 43 publications

(20 citation statements)

References 42 publications

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“…Also, the monetary cost per second usage of the fog node is set to 0.0386$/s. 25 The tasks are varied from 1000 to 5000 with the size randomly set in the range between 100-500 MI and 100-1000 KB, respectively. In addition, deadline of the tasks is set in the range between 500 and 2500 ms. Further, we have categorized tasks into high priority and low priority tasks.…”

Section: Resultsmentioning

confidence: 99%

Deadline‐cost aware task scheduling algorithm in fog computing networks

Sultan Hajam

2024

Int J Communication

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SummaryThe Internet of Things (IoT) has provided us with various applications that need processing in real‐time. Fog computing has arisen as a highly effective computing paradigm that provides services closer to the IoT devices and hence fulfills the need for rapid response time and delay requirements of IoT applications. However, the heterogeneity of IoT tasks and the limited resources of fog nodes pose considerable challenges to task scheduling. Therefore, the effective utilization of fog resources is highly significant and necessitates an optimal and intelligent approach to task scheduling. This paper presents the “deadline‐cost aware task scheduling” (DCaTS) algorithm, which is designed for fog‐based IoT applications. The primary objective of this algorithm is to minimize the monetary cost while taking into account the deadline requirements and priority of tasks. The efficacy of DCaTS is assessed by analyzing various parameters, including completion time, monetary cost, response time, and waiting time. The experimental results show that in comparison with traditional policies, the proposed algorithm shows superiority in all the parameters. The proposed algorithm reduces the monetary cost up to 11.07% compared with the second best result. The results also indicate that DCaTS achieves a reduction of 10.05% in completion time, 10% in response time, and 10.12% in waiting time compared with the second best result.

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Section: Resultsmentioning

confidence: 99%

Deadline‐cost aware task scheduling algorithm in fog computing networks

Sultan Hajam

2024

Int J Communication

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“…IOA methods search iteratively for the optimal subset of variables, and the main differences among methods are the selection strategy and initialization of variables. Swarm intelligent and evolutionary methods, 50 such as the binary dragonfly algorithm, 51 cat swarm optimization, 52 cuckoo search, 53 harmony search, 54 GA, and firework algorithm, 55 can be used to search for the optimal combination of variables to maximize the model performance.…”

Section: Methodsmentioning

confidence: 99%

A hybrid wavelength selection strategy-based quantitative analysis model for LIBS data from standard ground samples of the Curiosity rover on Mars

Yao

Jian-ping

2022

J. Anal. At. Spectrom.

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“…In Equation (1), vectors within the Euclidean space can be represented as

x

italicub

, and

italiclb

representing the upper and the lower bounds of the problem correspondingly (Yadav et al, 2022).…”

Section: Proposed Intrusion Detection Schemementioning

confidence: 99%

Intrusion detection system using metaheuristic fireworks optimization based feature selection with deep learning on Internet of Things environment

Jayasankar,

Kiruba Buri,

Maheswaravenkatesh

2023

Journal of Forecasting

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Internet of Things (IoT), cloud computing, and other significant advancements in communication have created new security challenges. Due to these advancements and the ineffectiveness of the current security measures, cyber‐attacks are also increasing quickly. Recently, several artificial intelligence (AI)–based solutions have been presented for various secure applications, such as intrusion detection. This article proposes an intrusion detection system using dynamic search fireworks optimization–based feature selection with optimal deep recurrent neural network (DFWAFS‐ODRNN) model in IoT environment. The presented DFWAFS‐ODRNN model follows a two‐stage process, namely, feature selection and intrusion classification. In the first phase, the DFWAFS‐ODRNN model elects an optimal subset of features using the dynamic search fireworks optimization algorithm (DFWAFS) technique. Next, in the second stage, the intrusions are identified and categorized using the DRNN model. At last, the hyperparameters of the DRNN are optimally chosen by the Nadam optimizer. A detailed simulation analysis of the DFWAFS‐ODRNN model is validated on benchmark intrusion detection system (IDS) dataset, and the outcomes show the efficacy of intrusion detection. The proposed model efficiently detects the intrusion detection with an accuracy of 96.11%.

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A bi-objective task scheduling approach in fog computing using hybrid fireworks algorithm

Cited by 43 publications

References 42 publications

Deadline‐cost aware task scheduling algorithm in fog computing networks

Deadline‐cost aware task scheduling algorithm in fog computing networks

A hybrid wavelength selection strategy-based quantitative analysis model for LIBS data from standard ground samples of the Curiosity rover on Mars

Intrusion detection system using metaheuristic fireworks optimization based feature selection with deep learning on Internet of Things environment

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