An SDN-Based Caching Decision Policy for Video Caching in Information-Centric Networking

Zhang, Zhe; Lung, Chung-Horng; St-Hilaire, Marc; Lambadaris, Ioannis

doi:10.1109/tmm.2019.2935683

Cited by 33 publications

(14 citation statements)

References 36 publications

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“…[13] utilizes a scalable video coding scheme and proposes caching algorithms to improve the video delivery services. The content centric communication model is used not only for static networks but also for high dynamic networks [33][34][35]. In [36], the authors show the feasibilities of video streaming over CCN-based vehicular networks.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

An Adaptive Video Transmission Mechanism over MEC‐Based Content‐Centric Networks

Han

Zhao

Bao

et al. 2021

Wireless Communications and Mobile Computing

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The rapid growth of video traffic poses serious challenges to the current Internet. Content-Centric Networking (CCN) as a promising candidate has been proposed to reengineer the Internet architecture. The in-network caching and named content communication model of CCN can enhance the video streaming applications and reduce the network workload. Due to the bandwidth-consuming characteristic of video streaming, the aggressive transmission of video data will cause a reduction of overall network efficiency. In this paper, we present an adaptive video transmission mechanism over Mobile Edge Computing- (MEC-) based CCN. The computation and storage resources of the MEC server are utilized to facilitate the video delivery. Our mechanism adopts a scalable video coding scheme to adaptively control transmission rate to cope with the network condition variation. To analyse the equilibrium property of the proposed mechanism, an analytical model is deduced by using network utility function and convex programming. We also take into account the packet loss in wired and wireless links and present a MEC assistant loss recovery algorithm. The experiment results demonstrate the performance improvement of our proposed mechanism.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

An Adaptive Video Transmission Mechanism over MEC‐Based Content‐Centric Networks

Han

Zhao

Bao

et al. 2021

Wireless Communications and Mobile Computing

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“…On the other hand, caching-driven users need the forwarding of the edge BS when the requested streaming is from the cloud to the mobile user. To avoid redundant transmission by other requests of the same video content and to reduce the response latency, it is better that the edge BS caches some popular contents, which also requires large amount of storage resource [7]. Therefore, a problem arises: with limited memory resource equipped at the edge BSs, how much of them should be allocated to computation resource and caching resource respectively?…”

Section: System Modelmentioning

confidence: 99%

Machine learning enabled distributed mobile edge computing network

Chang

Fan

et al. 2019

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

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In this work, we propose to establish a mobile edge computing (MEC) network that considers computation, caching and communication jointly. Depending on the demanding categories, users in the network are partitioned into computation-driven and cachingdriven users, both of which need memory resource to improve their quality of experiences (QoEs). Thus, a memory resource allocation problem is aroused to maximize the performance of the whole network. Due to the fact that the users' characterization plays an important role to the resource allocation scheme and with the help of machine learning techniques, we propose to study and predict the users' patterns by distributed learning methods which take the heterogeneity of base station type and users' mobility, etc into consideration. The proposed machine learning based distributed MEC system can maximize the efficiency of the network by optimizing the resource allocation scheme and perfectly predicting users' pattern. CCS CONCEPTS • Networks → Network performance modeling; Network performance analysis.

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“…On the one hand, the mobile/multi-access edge computing (MEC) platform [1] provides the entity to perform services at the consumers' side. On the other hand, software-defined networking (SDN) [2] together with network function virtualization (NFV) [3] technology enables the network programmable and virtualizable. In the context of proactive caching, there are four interconnected subproblems that need to be noticed [4]: the first one focuses on selecting the proper position to cache contents; the second question regards the determination of caching content category and eviction; the third part discusses the caching space allocation; the last one considers the content delivery path design.…”

Section: Introductionmentioning

confidence: 99%

A Genetic Algorithm-based Proactive Caching Policy in Edge Networks

Yuan,

Wang,

Wang

et al. 2023

J. Phys.: Conf. Ser.

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The incredible growth in mobile traffic lays down a challenge to network design. Therefore, proactive caching technology has been proposed as an effective method to reduce network congestion and improve the quality of service. In this paper, we focus on the caching position problem in edge networks and use a mixed integer linear programming (MILP) model to provide the optimal selection. Due to the curse of dimensionality, a genetic algorithm-based caching policy is given to make a balance between performance and time complexity. Numerical investigations reveal that the proposed approach can reduce the total cost by around 20% compared with other widely-used caching methods.

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An SDN-Based Caching Decision Policy for Video Caching in Information-Centric Networking

Cited by 33 publications

References 36 publications

An Adaptive Video Transmission Mechanism over MEC‐Based Content‐Centric Networks

An Adaptive Video Transmission Mechanism over MEC‐Based Content‐Centric Networks

Machine learning enabled distributed mobile edge computing network

A Genetic Algorithm-based Proactive Caching Policy in Edge Networks

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