Experimental evaluation of fog computing techniques to reduce latency in LTE networks

García-Pérez, Cesar A.; Merino, Pedro

doi:10.1002/ett.3201

Cited by 24 publications

(20 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By applying FC, García‐Pérez and Merino proposed two solutions to reduce latency in Long‐Term Evolution (LTE) networks. The first solution, named Fog Gateway, works based on analyzing data packets and sending them to local servers which present fog services.…”

Section: Classification Of the Selected Approachesmentioning

confidence: 99%

Quality of service‐aware approaches in fog computing

Kashani

Rahmani

Navimipour

2020

Int J Communication

View full text Add to dashboard Cite

Summary In recent years, fog computing, a novel paradigm, has emerged for location and latency‐sensitive applications. It is a powerful complement for cloud computing that enables provisioning services and resources outside the cloud near the end devices. In a fog system, the existence of several nonhomogenous devices, which are potentially mobile, led to quality of service (QoS) worries. QoS‐aware approaches are presented in various parts of the fog system, and several different QoS factors are taken into account. In spite of the importance of QoS in fog computing, no comprehensive study on QoS‐aware approaches exists in fog computing. Hence, this paper reviews the current research used to guarantee QoS in fog computing. This paper investigates the QoS‐ensuring techniques that fall into three categories: service/resource management, communication management, and application management (published between 2013 and October 2018). Regarding the selected approaches, this paper represents merits, demerits, tools, evaluation types, and QoS factors. Finally, on the basis of the reviewed studies, we suggest some open issues and challenges which are worth further studying and researching in QoS‐aware approaches in fog computing.

show abstract

Section: Classification Of the Selected Approachesmentioning

confidence: 99%

Quality of service‐aware approaches in fog computing

Kashani

Rahmani

Navimipour

2020

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Network softwarization and the advancements in the edge computing are paving the way forward for low‐latency 5G networks . In this section, we will elaborate the motivating scenario for the NSCs in the 5G environments, leveraging SDN and network services at the edge.…”

Section: From Nfv To Nsc: Service Execution At the Edgementioning

confidence: 99%

Composing network service chains at the edge: A Resilient and adaptive software‐defined approach

Kathiravelu

Roy

Veiga

2018

Trans Emerging Tel Tech

View full text Add to dashboard Cite

The scale of data and the demand for high data rates in applications are rapidly increasing over the years. By moving the computing resources closer to the users, edge computing minimizes the latency caused by otherwise pulling the data between the user and a centralized platform. Thus, data centers and computing nodes at the edge continue to overtake or replace traditional centralized cloud platforms in various 5G applications and use cases. Network functions virtualization virtualizes dedicated hardware network middleboxes into virtual network functions (VNFs) and deploys them on top of servers and data centers. Due to the inherent bandwidth cost associated with the distance between the network services and the end user, more and more third‐party VNF providers choose to deploy their services at the edge, rather than in a centralized cloud. A typical network service chaining (NSC) consumes various network services, where one's output becomes the input of another. As network services are often latency sensitive, NSC needs to be carried out, maintaining proximity to the user as well as among the services composing the NSC. In this paper, we present the design of a resilient and adaptive framework, following an approach inspired by software‐defined networking, for the remote users to construct network service chains in the mobile and edge computing environments. Our evaluations highlight the efficiency and optimality of the framework in finding the best fit among several third‐party edge VNFs in deploying the user network service chains, abiding by various user policies.

show abstract

“…With this being said, many researchers have started investigating the opportunities that can be gained from this integration. Farris et al and García‐Pérez and Merino have looked at latency as an outcome of using 5G and edge computing. In the work of García‐Pérez and Merino, the authors presented a thorough discussion on splitting latency in LTE networks and have evaluated the proposed solution with experiments using an LTE network testbed.…”

Section: Introductionmentioning

confidence: 99%

A collaborative mobile edge computing and user solution for service composition in 5G systems

Ridhawi

Aloqaily

Kotb

et al. 2018

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Mobile edge computing (MEC) is an emergent technology that has revolutionized traditional cloud service solutions. Mobile edge computing extends cloud computing by providing processing, storage, and networking capabilities at the edge of the mobile network. Delay‐sensitive and context‐aware applications are able to execute within close proximity of mobile users. Additionally, today's cloud services are not tailored to user specifications, but rather diversified toward a group of users. To guarantee delivery of user‐specific services in 5G networks, service composition techniques should be incorporated. This article envisions a real‐time, context‐aware, service‐composition collaborative framework that lies at the edge of the network, comprising MEC and user devices for fast composite service delivery. The proposed solution decomposes cloud data into a set of files and services, which are then replicated to MEC nodes. Frequently requested files and services are further cached onto user mobile devices for faster access. Both MEC nodes and mobile users advertise their services onto the collaborative edge/user space, where services are delivered either composite or unrendered according to users' requests. Service composition is achieved through a learning‐based workflow‐net approach that relies on previous composition results to build service composition models to be used for new compositions. The presented solution provides guaranteed and fast delivery of the requested cloud composite services to end users while sustaining QoS requirements and load balancing among edge and mobile nodes.

show abstract

Experimental evaluation of fog computing techniques to reduce latency in LTE networks

Cited by 24 publications

References 29 publications

Quality of service‐aware approaches in fog computing

Quality of service‐aware approaches in fog computing

Composing network service chains at the edge: A Resilient and adaptive software‐defined approach

A collaborative mobile edge computing and user solution for service composition in 5G systems

Contact Info

Product

Resources

About