IoT application modules placement in heterogeneous fog–cloud infrastructure

Arora, Upma; Singh, Nipur

doi:10.1007/s41870-021-00672-4

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…These algorithms have also been investigated for real-time applications, such as smart cities, industrial IoT, and mobile microclouds. [32][33][34][35] Table 1 summarizes recent work, including application types (eg, eHealth IoT system, general IoT system, or embedded system) in the column Scope, types of brokers (Broker), proposed algorithms (Alg. ), target functions to optimize (Objective functions), components that the optimization algorithm can control to enhance the objective functions (Decision variables), validation tools used to test the algorithms, and types of computing environments (Env.).…”

Section: Related Workmentioning

confidence: 99%

“…In References 27‐31, several aspects of fog resource management are defined, including scheduling, placement, provisioning, allocation, and mapping for clients, virtual machines, services, and resources. These algorithms have also been investigated for real‐time applications, such as smart cities, industrial IoT, and mobile microclouds 32‐35 . Table 1 summarizes recent work, including application types (eg, eHealth IoT system, general IoT system, or embedded system) in the column Scope , types of brokers ( Broker ), proposed algorithms ( Alg.)…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the computing nodes accept workloads by matching their sizes with node specifications. Conversely, Figure 5 shows the proposed broker, which keeps the remaining components of the broker having similar functions as service manager (SM), service usage monitor (SUM), and service popularity monitor (SPM) 33 …”

Section: Proposed Architecture and Algorithmmentioning

confidence: 99%

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Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

Sulimani

Sajjad

Alghamdi

et al. 2022

Trans Emerging Tel Tech

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A decentralized service placement policy plays a key role in distributed systems, such as fog computing, where sharing workloads fairly among active computing nodes is critical. A decentralized policy is an inherent feature of the service placement process that may improve load balancing among computers and can reduce the latency in many real‐time Internet of Things (IoT) applications. This article proposes reinforcement optimization for a decentralized service placement policy, which attempts to mitigate some of the drawbacks of existing service placement policies. Matching task size with node specifications and the allocation of less popular but time‐sensitive applications in the fog layer are the primary contributions of this study. Extensive experimental comparisons are made between the proposed algorithm and other well‐known algorithms over service latency, network usage, and computing usage using the iFogSim simulator. A microservice‐based application with varying sizes of computing requests are tested experimentally and show that the proposed algorithm effectively serves computing instances that are closer to users, reducing service latency and network usage. Compared to the existing models, the proposed modified algorithm reduces service latency by 24.1%, network usage by 4%, and computing usage by 20%, thus highlighting positive outcomes when using the proposed algorithm for fog analytics in future real‐time IoT applications.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

Sulimani

Sajjad

Alghamdi

et al. 2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…The introduction of fog computing in healthcare applications is to bridge the gap between IoT devices and analytics [ 8 ]. Fog computing is a distributed computing platform for managing applications and services at the network edge [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Novel DLSNNC and SBS based framework for improving QoS in healthcare-IoT applications

Jyotsna

Nand

2022

Int. j. inf. tecnol.

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Health care system is intended to enhance one's health and as a result, one's quality of life. In order to fulfil its social commitment, health care must focus on producing social profit to sustain itself. Also, due to ever increasing demand of healthcare sector, there is drastic rise in the amount of patient data that is produced and needs to be stored for long duration for clinical reference. The risk of patient data being lost due to a data centre failure can be minimized by including a fog layer into the cloud computing architecture. Furthermore, the burden of such data produced is stored on the cloud. In order to increase service quality, we introduce fog computing based on deep learning sigmoid-based neural network clustering (DLSNNC) and score-based scheduling (SBS). Fog computing begins by collecting and storing healthcare data on the cloud layer, using data collected through sensors. Deep learning sigmoid based neural network clustering and score based Scheduling approaches are used to determine entropy for each fog node in the fog layer. Sensors collect data and send it to the fog layer, while the cloud computing tier is responsible for monitoring the healthcare system. The exploratory findings show promising results in terms of end-to-end latency and network utilization. Also, the proposed system outperforms the existing techniques in terms of average delay.

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Improving Energy Consumption for Respond to Requests in Internet of Things by Various Optimization Algorithm, Based on the Fog Computing

Tao,

Deye,

Yiguang

2024

Wireless Pers Commun

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IoT application modules placement in heterogeneous fog–cloud infrastructure

Cited by 10 publications

References 14 publications

Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

Novel DLSNNC and SBS based framework for improving QoS in healthcare-IoT applications

Improving Energy Consumption for Respond to Requests in Internet of Things by Various Optimization Algorithm, Based on the Fog Computing

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