412 research outputs found
Coordinated Container Migration and Base Station Handover in Mobile Edge Computing
Offloading computationally intensive tasks from mobile users (MUs) to a
virtualized environment such as containers on a nearby edge server, can
significantly reduce processing time and hence end-to-end (E2E) delay. However,
when users are mobile, such containers need to be migrated to other edge
servers located closer to the MUs to keep the E2E delay low. Meanwhile, the
mobility of MUs necessitates handover among base stations in order to keep the
wireless connections between MUs and base stations uninterrupted. In this
paper, we address the joint problem of container migration and base-station
handover by proposing a coordinated migration-handover mechanism, with the
objective of achieving low E2E delay and minimizing service interruption. The
mechanism determines the optimal destinations and time for migration and
handover in a coordinated manner, along with a delta checkpoint technique that
we propose. We implement a testbed edge computing system with our proposed
coordinated migration-handover mechanism, and evaluate the performance using
real-world applications implemented with Docker container (an
industry-standard). The results demonstrate that our mechanism achieves 30%-40%
lower service downtime and 13%-22% lower E2E delay as compared to other
mechanisms. Our work is instrumental in offering smooth user experience in
mobile edge computing.Comment: 6 pages. Accepted for presentation at the IEEE Global Communications
Conference (Globecom), Taipei, Taiwan, Dec. 202
An Integrated, Virtualized Joint Edge and Fog Computing System with Multi-RAT Convergence
Notably, developing an innovative architectural network paradigm is essential to address the technical challenging of 5G applications' requirements in a unified platform. Forthcoming applications will provide a wide range ofnetworking, computing and storage capabilities closer to the endusers.In this context, the 5G-PPP Phase two project named "5GCORAL:A 5G Convergent Virtualized Radio Access Network Living at the Edge" aims at identifying and experimentally validating which are the key technology innovations allowing for the development of a convergent 5G multi-RAT access based on a virtualized Edge and Fog architecture being scalable, flexible and interoperable with other domains including transport, core network and distant Clouds. In 5G-CORAL, an architecture is proposed based on ETSI MEC and ETSI NFV frameworks in a unified platform. Then, a set of exemplary use cases benefiting from Edge and Fog networks in near proximity of the end-user are proposed for demonstration on top of connected car, shopping mall and high-speed train platforms.This work has been partially funded by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant num. 761586
Addressing the Challenges in Federating Edge Resources
This book chapter considers how Edge deployments can be brought to bear in a
global context by federating them across multiple geographic regions to create
a global Edge-based fabric that decentralizes data center computation. This is
currently impractical, not only because of technical challenges, but is also
shrouded by social, legal and geopolitical issues. In this chapter, we discuss
two key challenges - networking and management in federating Edge deployments.
Additionally, we consider resource and modeling challenges that will need to be
addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and
Paradigms; Editors Buyya, Sriram
Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment
In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to
empower various areas as a bridge between physical objects and the digital
world. Through virtualization and simulation techniques, multiple functions can
be achieved by leveraging computing resources. In this process, Mobile Cloud
Computing (MCC) and Mobile Edge Computing (MEC) have become two of the key
factors to achieve real-time feedback. However, current works only considered
edge servers or cloud servers in the DT system models. Besides, The models
ignore the DT with not only one data resource. In this paper, we propose a new
DT system model considering a heterogeneous MEC/MCC environment. Each DT in the
model is maintained in one of the servers via multiple data collection devices.
The offloading decision-making problem is also considered and a new offloading
scheme is proposed based on Distributed Deep Learning (DDL). Simulation results
demonstrate that our proposed algorithm can effectively and efficiently
decrease the system's average latency and energy consumption. Significant
improvement is achieved compared with the baselines under the dynamic
environment of DTs
AI-powered edge computing evolution for beyond 5G communication networks
Edge computing is a key enabling technology that is expected to play a crucial role in beyond 5G (B5G) and 6G communication networks. By bringing computation closer to where the data is generated, and leveraging Artificial Intelligence (AI) capabilities for advanced automation and orchestration, edge computing can enable a wide range of emerging applications with extreme requirements in terms of latency and computation, across multiple vertical domains. In this context, this paper first discusses the key technological challenges for the seamless integration of edge computing within B5G/6G and then presents a roadmap for the edge computing evolution, proposing a novel design approach for an open, intelligent, trustworthy, and distributed edge architecture.VERGE has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101096034.Peer ReviewedPostprint (author's final draft
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