Search results for "edge computing"
showing 10 items of 42 documents
Designing a multi-layer edge-computing platform for energy-efficient and delay-aware offloading in vehicular networks
2021
Abstract Vehicular networks are expected to support many time-critical services requiring huge amounts of computation resources with very low delay. However, such requirements may not be fully met by vehicle on-board devices due to their limited processing and storage capabilities. The solution provided by 5G is the application of the Multi-Access Edge Computing (MEC) paradigm, which represents a low-latency alternative to remote clouds. Accordingly, we envision a multi-layer job-offloading scheme based on three levels, i.e., the Vehicular Domain, the MEC Domain and Backhaul Network Domain. In such a view, jobs can be offloaded from the Vehicular Domain to the MEC Domain, and even further o…
Performance Analysis of Memory Cloning Solutions in Mobile Edge Computing
2018
This paper deals with the problem of service migration in the emerging scenarios of Mobile Edge Computing. Mobile edge computing is achieved by moving the traditional cloud infrastructures, exploited by many today applications, close to the network edge in order to reduce the response times in the so called tactile-internet. However, because of user mobility, such an application architecture may pose the problem of service migration in case of handover from one server site to another. After introducing the current solutions for dealing with service migration and, in particular, the approaches based on service decomposition into multiple layers, we quantify the migration time and the service…
NFVMon: Enabling Multioperator Flow Monitoring in 5G Mobile Edge Computing
2018
With the advances of new-generation wireless and mobile communication systems such as the fifth-generation (5G) mobile networks and Internet of Things (IoT) networks, demanding applications such as Ultra-High-Definition video applications is becoming ever popular. These applications require real-time monitoring and processing to meet the mission-critical quality of service requirements and are expected to be supported by the emerging fog or edge computing paradigms. This paper presents NFVMon, a novel monitoring architecture to enable flow monitoring capabilities of network traffic in a 5G multioperator mobile edge computing environment. The proposed NFVMon is integrated with the management…
Low-Latency Infrastructure-Based Cellular V2V Communications for Multi-Operator Environments With Regional Split
2021
Mobile network operators are interested in providing Vehicle-to-Vehicle (V2V) communication services using their cellular infrastructure. Regional split of operators is one possible approach to support multi-operator infrastructure-based cellular V2V communication. In this approach, a geographical area is divided into non-overlapping regions, each one served by a unique operator. Its main drawback is the communication interruption motivated by the inter-operator handover in border areas, which prevents the fulfillment of the maximum end-to-end (E2E) latency requirements of fifth generation (5G) V2V services related to autonomous driving. In this work, we enable a fast inter-operator handove…
Deep Learning-Based Sign Language Digits Recognition From Thermal Images With Edge Computing System
2021
The sign language digits based on hand gestures have been utilized in various applications such as human-computer interaction, robotics, health and medical systems, health assistive technologies, automotive user interfaces, crisis management and disaster relief, entertainment, and contactless communication in smart devices. The color and depth cameras are commonly deployed for hand gesture recognition, but the robust classification of hand gestures under varying illumination is still a challenging task. This work presents the design and deployment of a complete end-to-end edge computing system that can accurately provide the classification of hand gestures captured from thermal images. A th…
A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing
2019
Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by applicati…
Resource Allocation for Edge Computing-Based Blockchain: A Game Theoretic Approach
2020
Blockchain has been progressively applied to various Internet of Things (IoT) platforms. As the efficiency of the blockchain depends on its computing capability, how to make sure the acquisition of the computational resources and participation of the devices would be the driving force. In this work, an edge computing-based blockchain network is considered, where the edge service provider (ESP) offers computational resources for the miners. The focus is to investigate an efficient incentive mechanism for the miners to purchase the computational resources. Accordingly, a two-stage Stackelberg game is formulated between the miners and ESP. By exploring the Stackelberg equilibrium of the optima…
BEGIN: Big Data Enabled Energy-Efficient Vehicular Edge Computing
2018
Vehicular edge computing is essential to support future emerging multimedia-rich and delay-sensitive applications in vehicular networks. However, the massive deployment of edge computing infrastructures induces new problems including energy consumption and carbon pollution. This motivates us to develop BEGIN (Big data enabled EnerGy-efficient vehIcular edge computiNg), a programmable, scalable, and flexible framework for integrating big data analytics with vehicular edge computing. In this article, we first present a comprehensive literature review. Then the overall design principle of BEGIN is described with an emphasis on computing domain and data domain convergence. In the next section, …
Multi-Layer Offloading at the Edge for Vehicular Networks
2020
This paper proposes a multi-layer platform for job offloading in vehicular networks. Offloading is performed from vehicles in the Vehicular Domain towards Multi-Access Edge Computing (MEC) Servers deployed at the edge of the network, and between MEC Servers. Offloading decisions at both domains are challenging for the overall system performance. Optimization at the MEC Layer domain is obtained by model-based Reinforcement Learning, while a strategy to decide the best offloading rate from the Vehicular Domain is defined to achieve the desired trade-off between costs and performance. Numerical analysis shows the achieved performance.
Energy-Efficient Edge Computing Service Provisioning for Vehicular Networks: A Consensus ADMM Approach
2019
In vehicular networks, in-vehicle user equipment (UE) with limited battery capacity can achieve opportunistic energy saving by offloading energy-hungry workloads to vehicular edge computing nodes via vehicle-to-infrastructure links. However, how to determine the optimal portion of workload to be offloaded based on the dynamic states of energy consumption and latency in local computing, data transmission, workload execution and handover, is still an open issue. In this paper, we study the energy-efficient workload offloading problem and propose a low-complexity distributed solution based on consensus alternating direction method of multipliers. By incorporating a set of local variables for e…