On-Demand Computation Offloading Architecture in Fog Networks

Jin, Yeonjin; Lee, HyungJune

doi:10.3390/electronics8101076

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…This approach leverages Bayesian learning techniques to make informed decisions regarding the dynamic scaling of fog resources, determining both resource increases and decreases as needed. A policy for on-demand computation offloading in fog networks was presented in [13]. In this approach, a host node seeking additional computational resources beyond its own capabilities dynamically identifies accessible nodes and establishes an on-demand local resource provider network.…”

Section: Single-objective Approachesmentioning

confidence: 99%

On the Effectiveness of Fog Offloading in a Mobility-Aware Healthcare Environment

Sharifi,

Rasaii,

Pasdar

et al. 2023

Digital

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The emergence of fog computing has significantly enhanced real-time data processing by bringing computation resources closer to data sources. This adoption is very beneficial in the healthcare sector, where abundant time-sensitive processing tasks exist. Although such adoption is very promising, there is a challenge with the limited computational capacity of fog nodes. This challenge becomes even more critical when mobile IoT nodes enter the network, potentially increasing the network load. To address this challenge, this paper presents a framework that leverages a Many-to-One offloading (M2One) policy designed for modelling the dynamic nature and time-critical aspect of processing tasks in the healthcare domain. The framework benefits the multi-tier structure of the fog layer, making efficient use of the computing capacity of mobile fog nodes to enhance the overall computing capability of the fog network. Moreover, this framework accounts for mobile IoT nodes that generate an unpredictable volume of tasks at unpredictable intervals. Under the proposed policy, a first-tier fog node, called the coordinator fog node, efficiently manages all requests offloaded by the IoT nodes and allocates them to the fog nodes. It considers factors like the limited energy in the mobile nodes, the communication channel status, and low-latency demands to distribute requests among fog nodes and meet the stringent latency requirements of healthcare applications. Through extensive simulations in a healthcare scenario, the policy’s effectiveness showed an improvement of approximately 30% in average delay compared to cloud computing and a significant reduction in network usage.

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Section: Single-objective Approachesmentioning

confidence: 99%

On the Effectiveness of Fog Offloading in a Mobility-Aware Healthcare Environment

Sharifi,

Rasaii,

Pasdar

et al. 2023

Digital

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“…In [9] a scalable full-stack distributed engine tailored to energy data collection and forecasting in the smart-city context enabling the integration of the heterogeneous data measurements gathered from real-world systems and the forecasting of average power demand. Moreover, related to fog computing, in [10] the authors introduce a new architecture enabling on-demand computation to offload the computation in the cloud architecture, reducing unnecessary network overhead, by properly selecting the most effective edge devices as computation delegates.…”

Section: Related Workmentioning

confidence: 99%

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Panicucci¹,

Nikolakis

Cerquitelli

et al. 2020

Electronics

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Data management and processing to enable predictive analytics in cyber physical systems holds the promise of creating insight over underlying processes, discovering anomalous behaviours and predicting imminent failures threatening a normal and smooth production process. In this context, proactive strategies can be adopted, as enabled by predictive analytics. Predictive analytics in turn can make a shift in traditional maintenance approaches to more effective optimising their cost and transforming maintenance from a necessary evil to a strategic business factor. Empowered by the aforementioned points, this paper discusses a novel methodology for remaining useful life (RUL) estimation enabling predictive maintenance of industrial equipment using partial knowledge over its degradation function and the parameters that are affecting it. Moreover, the design and prototype implementation of a plug-n-play end-to-end cloud architecture, supporting predictive maintenance of industrial equipment is presented integrating the aforementioned concept as a service. This is achieved by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm. Obtained results show the effectiveness and the efficiency of the proposed methodology in supporting predictive analytics in the era of Industry 4.0.

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“…These cloud service providers have upgraded their infrastructure to produce more scalable and robust cloud computing to meet the demand with better availability and the increasing number of data centers [1]. The resources constraint faced by end-user devices ranging from IoT devices, computers, and physical servers has made cloud computing the providers of a preferred and flexible resource in deploying the solution [2], giving them an advantage on resources to focus on developing quality and fewer bugs software applications [3] before delivering to the end-users. Previously, in the early days of computing, software was installed and run separately and independently [4] in every machine.…”

Section: Introductionmentioning

confidence: 99%

Segmented Network Architecture for Promoting High Availability in Fog Computing through Middleware

Naim¹,

Zain²,

Jalil³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

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This paper proposes an architecture for deploying applications on a fog computing environment by adding another layer of fog nodes in a network segment that gains high software application availability. The conventional fog computing architecture would permanently shift the storage, applications, and data from cloud servers to fog nodes, thus reducing the dependency on the cloud. As a result, fog nodes are burdened with the task previously done by cloud servers and have become "mini cloud servers." Instead of permanently shifting the tasks from cloud servers to fog nodes, the proposed architecture would only do the shifting, when necessary, like if an internet outage. Additionally, this research also introduced the middleware application that acts as a detector and replacement if service outage so that the availability of the services is not interrupted, especially during the internet outage, by adding another layer of fog node in a network segment. The computational process occurs between end-users and the fog nodes without having to rely on cloud servers. An experiment was conducted to test the performance of the proposed architecture. From the experiment, it can be concluded that the deployment of fog nodes in a segmented network is possible and able to increase the availability of data and services if an internet outage.

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On-Demand Computation Offloading Architecture in Fog Networks

Cited by 8 publications

References 31 publications

On the Effectiveness of Fog Offloading in a Mobility-Aware Healthcare Environment

On the Effectiveness of Fog Offloading in a Mobility-Aware Healthcare Environment

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Segmented Network Architecture for Promoting High Availability in Fog Computing through Middleware

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