Serving at the Edge: An Edge Computing Service Architecture Based on ICN

2022 ◽  
Vol 22 (1) ◽  
pp. 1-27
Author(s):  
Zhenyu Fan ◽  
Wang Yang ◽  
Fan Wu ◽  
Jing Cao ◽  
Weisong Shi
Keyword(s):  
Completion Time ◽  
Large Scale ◽  
Edge Computing ◽  
End User ◽  
Service Architecture ◽  
Service Request ◽  
Service Completion ◽  
Dynamic Deployment ◽  
Network Status ◽  
Network Overhead

Different from cloud computing, edge computing moves computing away from the centralized data center and closer to the end-user. Therefore, with the large-scale deployment of edge services, it becomes a new challenge of how to dynamically select the appropriate edge server for computing requesters based on the edge server and network status. In the TCP/IP architecture, edge computing applications rely on centralized proxy servers to select an appropriate edge server, which leads to additional network overhead and increases service response latency. Due to its powerful forwarding plane, Information-Centric Networking (ICN) has the potential to provide more efficient networking support for edge computing than TCP/IP. However, traditional ICN only addresses named data and cannot well support the handle of dynamic content. In this article, we propose an edge computing service architecture based on ICN, which contains the edge computing service session model, service request forwarding strategies, and service dynamic deployment mechanism. The proposed service session model can not only keep the overhead low but also push the results to the computing requester immediately once the computing is completed. However, the service request forwarding strategies can forward computing requests to an appropriate edge server in a distributed manner. Compared with the TCP/IP-based proxy solution, our forwarding strategy can avoid unnecessary network transmissions, thereby reducing the service completion time. Moreover, the service dynamic deployment mechanism decides whether to deploy an edge service on an edge server based on service popularity, so that edge services can be dynamically deployed to hotspot, further reducing the service completion time.

scholarly journals Deep Learning at the Mobile Edge: Opportunities for 5G Networks

2020 ◽  
Vol 10 (14) ◽  
pp. 4735 ◽  
Author(s):  
Miranda McClellan ◽  
Cristina Cervelló-Pastor ◽  
Sebastià Sallent
Keyword(s):  
Large Scale ◽  
Edge Computing ◽  
Connected Vehicles ◽  
Mobile Edge Computing ◽  
5G Networks ◽  
End User ◽  
Network Resources ◽  
Mobility Modeling ◽  
Novel Applications ◽  
Adaptive Resource

Mobile edge computing (MEC) within 5G networks brings the power of cloud computing, storage, and analysis closer to the end user. The increased speeds and reduced delay enable novel applications such as connected vehicles, large-scale IoT, video streaming, and industry robotics. Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically scaling up network resources as needed. Together, mobile edge computing and ML enable seamless automation of network management to reduce operational costs and enhance user experience. In this paper, we discuss the state of the art for ML within mobile edge computing and the advances needed in automating adaptive resource allocation, mobility modeling, security, and energy efficiency for 5G networks.

scholarly journals Joint optimization of network selection and task offloading for vehicular edge computing

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He
Keyword(s):  
Large Scale ◽  
Vehicular Networks ◽  
Access Point ◽  
Edge Computing ◽  
Network Selection ◽  
Network Access ◽  
Task Execution ◽  
Point Selection ◽  
Vehicle Movement ◽  
Task Offloading

AbstractTaking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

scholarly journals A Proximal Algorithm for Fork-Choice in Distributed Ledger Technology for Context-Based Clustering on Edge Computing

2020 ◽  
Vol 2 (1) ◽  
pp. 92
Author(s):  
Rahim Rahmani ◽  
Ramin Firouzi ◽  
Sachiko Lim ◽  
Mahbub Alam
Keyword(s):  
Large Scale ◽  
Cluster Head ◽  
Edge Computing ◽  
Computing Technology ◽  
Proximal Algorithm ◽  
Data Intensive ◽  
Distributed Ledger ◽  
Smart Contract ◽  
Distributed Ledger Technology ◽  
Iot Devices

The major challenges of operating data-intensive of Distributed Ledger Technology (DLT) are (1) to reach consensus on the main chain as a set of validators cast public votes to decide on which blocks to finalize and (2) scalability on how to increase the number of chains which will be running in parallel. In this paper, we introduce a new proximal algorithm that scales DLT in a large-scale Internet of Things (IoT) devices network. We discuss how the algorithm benefits the integrating DLT in IoT by using edge computing technology, taking the scalability and heterogeneous capability of IoT devices into consideration. IoT devices are clustered dynamically into groups based on proximity context information. A cluster head is used to bridge the IoT devices with the DLT network where a smart contract is deployed. In this way, the security of the IoT is improved and the scalability and latency are solved. We elaborate on our mechanism and discuss issues that should be considered and implemented when using the proposed algorithm, we even show how it behaves with varying parameters like latency or when clustering.

scholarly journals A Secure IoT Service Architecture With an Efficient Balance Dynamics Based on Cloud and Edge Computing

2019 ◽  
Vol 6 (3) ◽  
pp. 4831-4843 ◽  
Author(s):  
Tian Wang ◽  
Guangxue Zhang ◽  
Anfeng Liu ◽  
Md Zakirul Alam Bhuiyan ◽  
Qun Jin
Keyword(s):  
Edge Computing ◽  
Service Architecture ◽  
Balance Dynamics

Evaluation of Large Scale RoI Mining Applications in Edge Computing Environments

2021 ◽  
Author(s):  
Loris Belcastro ◽  
Alberto Falcone ◽  
Alfredo Garro ◽  
Fabrizio Marozzo
Keyword(s):  
Large Scale ◽  
Edge Computing ◽  
Computing Environments

Client-Centric Cloud Service Composition

2017 ◽  
pp. 144-166
Author(s):  
Vivek Gaur ◽  
Praveen Dhyani ◽  
Om Prakash Rishi
Keyword(s):  
Service Composition ◽  
Large Scale ◽  
Business Processes ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Quality Parameters ◽  
End User ◽  
Cloud Service Composition ◽  
Tabu Search Methods

Recent computing world has seen rapid growth of the number of middle and large scale enterprises that deploy business processes sharing variety of services available over cloud environment. Due to the advantage of reduced cost and increased availability, the cloud technology has been gaining unbound popularity. However, because of existence of multiple cloud service providers on one hand and varying user requirements on the other hand, the task of appropriate service composition becomes challenging. The conception of this chapter is to consider the fact that different quality parameters related to various services might bear varied importance for different user. This chapter introduces a framework for QoS-based Cloud service selection to satisfy the end user needs. A hybrid algorithm based on genetic algorithm (GA) and Tabu Search methods has been developed, and its efficacy is analysed. Finally, this chapter includes the experimental analysis to present the performance of the algorithm.

scholarly journals Edge Computing-Empowered Large-Scale Traffic Data Recovery Leveraging Low-Rank Theory

2020 ◽  
Vol 7 (4) ◽  
pp. 2205-2218 ◽  
Author(s):  
Chaocan Xiang ◽  
Zhao Zhang ◽  
Yuben Qu ◽  
Dongyu Lu ◽  
Xiaochen Fan ◽  
...  
Keyword(s):  
Large Scale ◽  
Edge Computing ◽  
Data Recovery ◽  
Low Rank ◽  
Traffic Data

The Enterprise Systems Approach

2010 ◽  
pp. 1-30 ◽  
Author(s):  
Andrew Targowski
Keyword(s):  
Network Architecture ◽  
Large Scale ◽  
Service Oriented Architecture ◽  
Systems Approach ◽  
Enterprise Systems ◽  
Enterprise Information ◽  
Service Architecture ◽  
Business Component ◽  
Service Oriented ◽  
Architectural Planning

The enterprise system approach is defined by its evolution and major milestones of architectural planning. The ES architectures are multi-faceted solutions, hence it is defined in the scope of the enterprise organization architecture (EOA), enterprise functional architecture (EFA), enterprise processive architecture (EPA), enterprise information architecture (EIA), enterprise software architecture (ESA), enterprise network architecture (ENA), enterprise service architecture (ESA), business component architecture (BCA), enterprise information infrastructure (EII), and enterprise configurations. A composite ES architecture is presented as a transitional architecture, which is currently practiced by most enterprises. The near future of the ES approach will be rather limited to the ways of delivering ES’ applications within a framework of service-oriented architecture (SOA) and the cloud computing, which satisfies effective large-scale operations. The progressive process of organization/business virtualization and the urgent need for more sustainable enterprise development should lead to new development of enterprise systems.

scholarly journals Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World

2019 ◽  
Vol 11 (4) ◽  
pp. 100 ◽  
Author(s):  
Maurizio Capra ◽  
Riccardo Peloso ◽  
Guido Masera ◽  
Massimo Ruo Roch ◽  
Maurizio Martina
Keyword(s):  
Internet Of Things ◽  
Mobile Devices ◽  
Information Exchange ◽  
Large Scale ◽  
Operating Time ◽  
Edge Computing ◽  
The Internet ◽  
Power Load ◽  
Battery Capacity ◽  
Iot Devices

In today’s world, ruled by a great amount of data and mobile devices, cloud-based systems are spreading all over. Such phenomenon increases the number of connected devices, broadcast bandwidth, and information exchange. These fine-grained interconnected systems, which enable the Internet connectivity for an extremely large number of facilities (far beyond the current number of devices) go by the name of Internet of Things (IoT). In this scenario, mobile devices have an operating time which is proportional to the battery capacity, the number of operations performed per cycle and the amount of exchanged data. Since the transmission of data to a central cloud represents a very energy-hungry operation, new computational paradigms have been implemented. The computation is not completely performed in the cloud, distributing the power load among the nodes of the system, and data are compressed to reduce the transmitted power requirements. In the edge-computing paradigm, part of the computational power is moved toward data collection sources, and, only after a first elaboration, collected data are sent to the central cloud server. Indeed, the “edge” term refers to the extremities of systems represented by IoT devices. This survey paper presents the hardware architectures of typical IoT devices and sums up many of the low power techniques which make them appealing for a large scale of applications. An overview of the newest research topics is discussed, besides a final example of a complete functioning system, embedding all the introduced features.

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