An enhanced multi-objective fireworks algorithm for task scheduling in fog computing environment

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

doi:10.1007/s10586-021-03481-3

Cited by 25 publications

(8 citation statements)

References 33 publications

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“…The execution time of VMs is used as the evaluation of load balancing, as shown by Eqs. ( 13) and (14).…”

Section: Definition Of Load Balancingmentioning

confidence: 99%

“…Secondly, the execution time of each task is measured to balance the overloaded VMs, which can obtain the best utilization of system resources. Yadav et al [14] proposed an algorithm for fireworks display based on opposition learning and differential evolution technology. Kruekaew et al [15] proposed using a multi-objective task scheduling optimization based on the artificial bee colony algorithm with a Q-learning algorithm, which adopting Qlearning algorithm to strengthen the learning ability of artificial bee colony algorithm.…”

Section: Resource Scheduling Algorithmmentioning

confidence: 99%

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An optimized resource scheduling algorithm based on GA and ACO algorithm

Yin

Peng

et al. 2023

Preprint

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With the development of Internet of things (IoT) technology, fog computing has gradually come into people’s vision because of the characteristics of real-time and low delay. Providing computing and storage services at the edge of the network, fog computing can meet the needs of users in delay-sensitive applications as a highly virtualized platform. Resource scheduling plays an important role in fog computing, because it can meet the requirements of customers and improve the quality of service. However, if largescale service requests can not solve the resource scheduling problem effectively, it will reduce resource utilization and user satisfaction. Focus on the above problem, we establish a resource scheduling model named normalization processing, which can achieve the goal of the lowest total cost by finding the optimal pheromone. The optimal resource scheduling results can be obtained by changing the pheromone concentration of ants during the simulated foraging process. At the same time, we propose a new resource scheduling algorithm named new genetic ant colony optimization (NGACO) algorithm, which can update pheromone generation by roulette algorithm. The experimental results show that the resource scheduling performance of NGACO algorithm is 14.7%, 25%, 12.8% lower than that of ACO algorithm in makespan, economic cost, total cost respectively, and load balancing is 34.7% higher than ACO algorithm.

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“…The execution time of VMs is used as the evaluation of load balancing, as shown by Eqs. ( 13) and (14).…”

Section: Definition Of Load Balancingmentioning

confidence: 99%

Section: Resource Scheduling Algorithmmentioning

confidence: 99%

An optimized resource scheduling algorithm based on GA and ACO algorithm

Yin

Peng

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The fog computing in other words referred to as edge computing that address such drawbacks by offering elastic services and resources to the end‐users at the network's edge, but cloud computing focus on providing resources scattered throughout the network. Fog nodes are infrastructures or facilities that offer resources for services 23 …”

Section: Proposed Designmentioning

confidence: 99%

An optimal trust and secure model using deep metric learning for fog‐based VANET

Tripathi¹,

Sharma

2023

Trans Emerging Tel Tech

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Vehicular ad hoc network (VANET) is a technique that enhances road safety via communication between two different vehicles. Trust establishment in VANET in between the vehicles is the most significant factor that provides secure reliability and integrity. Reliability and trust assist the vehicles in gathering credible data from neighboring vehicles. On the other hand, incomplete and inaccurate data gathered by the vehicles contain interrupting effect on VANET. Therefore, this article proposes a modified deep metric learning‐based chaotic Henry gas solubility optimization (MDML‐CHGSO) approach to provide a trust‐based security design. The proposed trust model is based on plausibility as well as experience in securing vehicle networks. The proposed model verifies the received data's genuineness from the approved vehicle using various processes namely, authentication, lifetime verification, experience evaluation, plausibility determination, and accurate measurement. The adoption of the modified DML‐based CHGSO approach extracts the location of the event message accurately when the location of the message is located closer to the fog nodes. In addition to this, average latency, throughput and packet delivery ratio, are the metrics evaluated for rating the performance.

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“…Let us assume that first type requests have access to server and buffer, while second type requests -to server and orbit. Let us consider two types of priority scheduling algorithmspreemptive and non-preemptive scheduling [20], [21], [29], [30].…”

Section: Mathematical Modelmentioning

confidence: 99%

Performance analysis of queueing system model under priority scheduling algorithms within 5G networks slicing framework

Adou

Markova

Zhbankova

2022

Discrete and Continuous Models

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A new era is opening for the world of information and communication technologies with the 5G networks release. Indeed 5G networks appear in modern wireless systems as solutions to traditional networks inflexibility and lack of radio resources problems. Using these networks the operators can expand their services range at will and, therefore, manage daily operations by monitoring key performance indicators (KPIs) - helping meet the quality of service (QoS) requirements much easily. To meet the QoS requirements 5G networks can be implemented alongside priority scheduling algorithms. This paper considers the operation of a wireless network slicing model under two scheduling algorithms. A comparative analysis of main performance measures is provided.

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An enhanced multi-objective fireworks algorithm for task scheduling in fog computing environment

Cited by 25 publications

References 33 publications

An optimized resource scheduling algorithm based on GA and ACO algorithm

An optimized resource scheduling algorithm based on GA and ACO algorithm

An optimal trust and secure model using deep metric learning for fog‐based VANET

Performance analysis of queueing system model under priority scheduling algorithms within 5G networks slicing framework

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