Reliable design for virtual network requests with location constraints in edge-of-things computing

Zhang, San-mei; Sangaiah, Arun Kumar

doi:10.1186/s13638-018-1075-8

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…The latency of any network is measured in milliseconds (ms) and is also known as RTT, i.e., Round Trip Time. The RTT of any network mainly depends on Processing Delay (PD), Queuing Delay (QD), Transmission Delay (TD), and Progression Delay (PRD) [10]. Processing Delay in a network depends on various device-level activities, including data acquisition, classification filtering, error detection, next-hop analysis, encryption operations, etc.…”

Section: Storage Intelligence and Latency Tradeoffmentioning

confidence: 99%

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

Aldribi¹,

Singh²,

Bre駉sa³

2023

Computer Systems Science and Engineering

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Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications. Internet of Things (IoT), fog computing, edge computing, cloud computing, and the edge of things are the spine of all real-time and scalable applications. Conspicuously, this study proposed a novel framework for a real-time and scalable application that changes dynamically with time. In this study, IoT deployment is recommended for data acquisition. The Pre-Processing of data with local edge and fog nodes is implemented in this study. The thresholdoriented data classification method is deployed to improve the intrusion detection mechanism's performance. The employment of machine learningempowered intelligent algorithms in a distributed manner is implemented to enhance the overall response rate of the layered framework. The placement of respondent nodes near the framework's IoT layer minimizes the network's latency. For economic evaluation of the proposed framework with minimal efforts, EdgeCloudSim and FogNetSim++ simulation environments are deployed in this study. The experimental results confirm the robustness of the proposed system by its improvised threshold-oriented data classification and intrusion detection approach, improved response rate, and prediction mechanism. Moreover, the proposed layered framework provides a robust solution for real-time and scalable applications that changes dynamically with time.

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Section: Storage Intelligence and Latency Tradeoffmentioning

confidence: 99%

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

Aldribi¹,

Singh²,

Bre駉sa³

2023

Computer Systems Science and Engineering

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“…It takes few people to do accomplish many tasks on a cloud, with a minimal learning curve on hardware and software issues. Moreover, cloud computing minimises the licensing of new software, such as stretch and grow, without the need to purchase expensive software licenses or programs; it also improves flexibility by changing directions without serious 'people' or 'financial' issues at stake [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Improved PSO Algorithm Integrated With Opposition-Based Learning and Tentative Perception in Networked Data Centres

Zhou

Abawajy

et al. 2020

IEEE Access

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Particle swarm optimization (PSO) algorithms have low-quality initial particle swarm, which is generated by a random method when handling the problem of task scheduling in networked data centres. Such algorithms also fall easily into local optimum when searching for the optimal solution. To address these problems, this study proposes combining opposition-based learning (OBL) and tentative perception (TP) with PSO; the proposed method is called OBL-TP-PSO. This algorithm uses reverse learning to generate the initial population, such that the quality of the initial particle swarm can be improved. Before the particle speed and location are updated, the TP method is used to search for the individual optimum around each particle, thereby reducing the possibility of missing the potential optimal solution during the process of searching. In this manner, the problem in which the PSO algorithm easily falls into the local optimal is effectively solved. To evaluate the performance of the proposed algorithm, simulation experiments are performed on CloudSim toolkit. Experimental results show that in comparison with other algorithms (namely, Min-Min, Max-Min and PSO algorithm), the proposed OBL-TP-PSO algorithm has better performance in terms of the total execution time, load balancing and quality of service. INDEX TERMS Big data processing, high-performance data processing, networked data centre, opposition-based learning, tentative perception.

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Ensuring the Security and Load Balancing in the Cloud Environment by BPA-SAB Method

Abraham

Fredrik

2022

Ambient Communications and Computer Systems

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Reliable design for virtual network requests with location constraints in edge-of-things computing

Cited by 8 publications

References 32 publications

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

Improved PSO Algorithm Integrated With Opposition-Based Learning and Tentative Perception in Networked Data Centres

Ensuring the Security and Load Balancing in the Cloud Environment by BPA-SAB Method

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