An Adaptive Offloading Method for an IoT-Cloud Converged Virtual Machine System Using a Hybrid Deep Neural Network

Son, Yunsik; Jeong, Junho; Lee, Yang Sun

doi:10.3390/su10113955

Cited by 10 publications

(8 citation statements)

References 20 publications

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“…Figure 1 demonstrates a basic structure of multiple energy harvesting clients and a multi-server mobile edge computing system. Son, Y et al [27] presented an adaptive method for offloading in a cloud converged virtual machine system. They used a hybrid deep neural network approach to obtain context required for synchronization from cloud-based offloading service.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

et al. 2020

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Conserving energy resources and enhancing computation capability have been the key design challenges in the era of the Internet of Things (IoT). The recent development of energy harvesting (EH) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling such challenges. Computation offloading enables executing the heavy computation workloads at the powerful MEC servers. Hence, the quality of computation experience, for example, the execution latency, could be significantly improved. In a situation where mobile devices can move arbitrarily and having multi servers for offloading, computation offloading strategies are facing new challenges. The competition of resource allocation and server selection becomes high in such environments. In this paper, an optimized computation offloading algorithm that is based on integer linear optimization is proposed. The algorithm allows choosing the execution mode among local execution, offloading execution, and task dropping for each mobile device. The proposed system is based on an improved computing strategy that is also energy efficient. Mobile devices, including energy harvesting (EH) devices, are considered for simulation purposes. Simulation results illustrate that the energy level starts from 0.979 % and gradually decreases to 0.87 % . Therefore, the proposed algorithm can trade-off the energy of computational offloading tasks efficiently.

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Section: Related Workmentioning

confidence: 99%

Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

et al. 2020

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“…However, these solutions do not provide an integrated approach to support the entire IoT system development process from analysis to implementation and often tend to solve a specific problem simultaneously. However, this partial approach led to poor interoperability of the "intranet" system, insufficient scalability or manageability of applications, leaving the original IoT embedded vision [23][24][25]. Therefore, we will develop a horizontal environment concept for interacting networked, physical, and networked physical systems, providing a comprehensive, application-independent methodological approach to developing IoT systems.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Approach towards the Design and Implementation of Virtual Network Based on Controller in Future IoT Applications

et al. 2020

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The Internet of Things refers (IoT) to the billions of physical devices around the globe that are connected to the Internet, collecting and sharing data. The overall Internet of Things market is projected to be worth more than 50.6 billion U.S. dollars in 2020. IoT devices possess low processing capabilities, limited memory, limited storage, and minimal network protocol support. With the help of cloud computing technology, we can overcome the limited resources of IoT devices. A lot of research has been conducted on IoT device virtualization to facilitate remote access and control. The concept of virtualization in IoT is to provide a virtual representation of physical devices in the form of virtual objects. IoT devices are more likely to be accessed and communicate through virtual objects in the near future. In this paper, we present the design and implementation of building a virtual IoT network for a smart home. The virtual network is based on virtual objects and IoT controller. We derived the concept from Software Defined Network (SDN) and separated the control plane and data plane in the virtual IoT network. This enhanced the rapid development of diverse applications on top of the virtualization layer by establishing a dynamic end-to-end connection between IoT devices. This article briefly explains the design and development of the virtual network. Results achieved during experiments and performance analysis show that IoT controller enhances the capabilities of a virtual network by dynamically controlling the traffic congestion, handling mapping requests, and routing mechanisms.

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“…In ITS scenarios, mobile devices with constraints on computational and energy resources are often used to cite an example. To circumvent this issue, we can employ a traditional approach that moves computational tasks to the cloud, allowing mobile devices to transfer the burden of running the DNN [5]. This solution, however, has challenges when considering network load and inference time.…”

Section: Introductionmentioning

confidence: 99%

Towards Edge Computing Using Early-Exit Convolutional Neural Networks

Pacheco,

Bochie,

Gilbert

et al. 2021

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In computer vision applications, mobile devices can transfer the inference of Convolutional Neural Networks (CNNs) to the cloud due to their computational restrictions. Nevertheless, besides introducing more network load concerning the cloud, this approach can make unfeasible applications that require low latency. A possible solution is to use CNNs with early exits at the network edge. These CNNs can pre-classify part of the samples in the intermediate layers based on a confidence criterion. Hence, the device sends to the cloud only samples that have not been satisfactorily classified. This work evaluates the performance of these CNNs at the computational edge, considering an object detection application. For this, we employ a MobiletNetV2 with early exits. The experiments show that the early classification can reduce the data load and the inference time without imposing losses to the application performance.

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An Adaptive Offloading Method for an IoT-Cloud Converged Virtual Machine System Using a Hybrid Deep Neural Network

Cited by 10 publications

References 20 publications

Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

A Novel Approach towards the Design and Implementation of Virtual Network Based on Controller in Future IoT Applications

Towards Edge Computing Using Early-Exit Convolutional Neural Networks

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