A Scalable Software-Defined Edge Computing Model for Sustainable Smart City Internet of Things (IoT) Application

Apat, Hemant Kumar; Sahoo, Bibhudatta; Mohanty, Sagarika; Sahoo, Kshira Sagar

doi:10.1201/9781003213871-7

Cited by 3 publications

(4 citation statements)

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“…The proposed Neural-Hill algorithm is straightforward to integrate with the system. Another study by H. K. Apat et al addresses the increasing demand for computing resources for the Internet of Things (IoT) [24]. It aligns with one of the observations that motivate the development of the proposed Neural-Hill algorithm.…”

Section: Literature Reviewmentioning

confidence: 69%

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Neural-Hill: A Novel Algorithm for Efficient Scheduling IoT-Cloud Resource to Maintain Scalability

Achar

2023

IEEE Access

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The age of the Fourth Industrial Revolution (4IR) is the era of smart technologies and services. The Internet of Things (IoT) is at the heart of these smart services. The IoTs are resource-constrain devices. They act as middleware in intelligent systems and maintain communications between cloud servers and smart services. Processing related to intelligent decision-making, including data processing, cleaning, feature extraction, and analysis, is performed on the cloud servers. The IoT devices respond according to the decisions the applications run on the cloud servers make. The massive number of internet-connected devices is increasing by 8% per year. The cloud infrastructure backing these enormous numbers of IoT devices must be scheduled efficiently to maintain Quality of Service (QoS). An optimized scheduling scheme is essential to minimize the cost and enhance scalability. This paper proposes an innovative and novel algorithm, Neural-Hill, which combines the Deep Neural Network (DNN) and Random Restart variant of the Hill Climbing algorithm to schedule IoT-Cloud resources efficiently and ensure scalability. It is a preemptive scheduling algorithm designed to operate in dynamic task scheduling. The performance of the Neural-Hill algorithm has been evaluated in terms of optimal solution-finding time, execution time, routing overhead, and throughput. The experimental results demonstrate the significant quality of service improvement with the assurance of better scalability.

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Section: Literature Reviewmentioning

confidence: 69%

“…It aligns with one of the observations that motivate the development of the proposed Neural-Hill algorithm. However, the approach presented in [24] follows an edge computing paradigm focused on ad-hoc networks. S. B. Sangeetha et al address the increase in demand for cloud resources from media content [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Neural-Hill: A Novel Algorithm for Efficient Scheduling IoT-Cloud Resource to Maintain Scalability

Achar

2023

IEEE Access

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“…To address this challenge, Cisco introduced a new layer seamlessly integrated into cloud computing, forming fog computing (FC). FC integrates storage, computing, and networking at the network's edge, reducing data transfer to the cloud, lowering latency, and enhancing efficiency (Elbamby, Bennis et al 2018, Apat, Nayak et al 2023). This technology is crucial for decentralized computing, especially in real-time IoT applications.…”

Section: Introductionmentioning

confidence: 99%

“…The imperative drives the impetus for this research to meet the escalating demands of time-sensitive applications in a world characterized by the continuous proliferation of IoT devices. Cisco's introduction of FC, which seamlessly integrates storage, computing, and networking at the network's edge, is a notable development (Apat, Nayak et al 2023). With its capability to reduce data transfer to the cloud, diminish latency, and improve efficiency, FC represents a significant step forward.…”

Section: Introductionmentioning

confidence: 99%

HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks

Sulimani,

Ramezani

et al. 2023

Preprint

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Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SOS) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, prevalent dynamic offloading (DOS) solutions often come with hidden costs that impact sensitive applications, including decision time, networks congested and distance offloading. This paper introduces the Hybrid Offloading (HybOff) algorithm, which substantially enhances load balancing and resource utilization in fog networks, addressing issues in both static and dynamic approaches while leveraging clustering theory. Its goal is to create a uncomplicated low-cost offloading approach that enhances IoT application performance by eliminating the consequences of hidden costs regardless of network size. Experimental results using the iFogSim simulation tool show that HybOff significantly reduces offloading messages, distance, and decision-offloading consequences. It improves load balancing by 97%, surpassing SOS (64%) and DOS (88%). Additionally, it increases system utilization by an average of 50% and enhances system performance 1.6 times and 1.4 times more than SOS and DOS, respectively. In summary, HybOff substantially contributes to load balancing and offloading research in fog computing.

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A Study on Advanced Networking using Intelligent Systems Applications

Gadhamsetty,

Kuma

2023

2023 IEEE 8th International Conference for Convergence in Technology (I2CT)

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A Scalable Software-Defined Edge Computing Model for Sustainable Smart City Internet of Things (IoT) Application

Cited by 3 publications

References 0 publications

Neural-Hill: A Novel Algorithm for Efficient Scheduling IoT-Cloud Resource to Maintain Scalability

Neural-Hill: A Novel Algorithm for Efficient Scheduling IoT-Cloud Resource to Maintain Scalability

HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks

A Study on Advanced Networking using Intelligent Systems Applications

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