Cooperative Content Caching for Mobile Edge Computing With Network Coding

He, Weisheng; Su, Yuhan; Xu, Xueting; Luo, Zhaohui; Huang, Lianfen; Du, Xiaojiang

doi:10.1109/access.2019.2917977

Cited by 15 publications

(6 citation statements)

References 15 publications

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“…He et al built a mobile edge computing intelligent cache analysis platform, which can pre-determine which streaming media files are stored in the limited storage space of edge computing devices and provide users with an efficient, intelligent network experience. Then, the network coding and wireless relay cooperation method is used to simultaneously transmit the files extracted from the core network and the real-time regular communication network to the network's edge, thereby realizing the intelligent caching of mobile edge computing [33].…”

Section: Background and Related Workmentioning

confidence: 99%

Differentiated Content Acquisition Integrating Network Coding and Edge Computing for Industrial Internet

Wang

2023

Applied Sciences

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The construction of the Industrial Internet has become a concrete implementation and an essential starting point in accelerating the digital transformation and intelligent upgrading of industrial manufacturing enterprises. The problem of addressing and forwarding for resource-constrained devices restricts data acquisition and dissemination on the Industrial Internet. In order to retrieve content reasonably, we propose IDEANE, an identity-differentiated content acquisition and multipath forwarding scheme with network coding and edge computing. In IDEANE, content requests are disseminated based on the identity and location information carried in a multi-identifier network, which can improve the efficiency of interest message requests and reduce idle links in the network. Moreover, the content acquisition computation is offloaded to multiple edge nodes, and the encoded data are transmitted to the edge nodes for recovery. IDEANE offloads the less demanding computing tasks to the edge nodes close to the content requesters, to relieve the pressure on providers. In addition, a collaboration method among multiple edge nodes is also studied. Multiple edge nodes collaborate to support the mobility of content requesters, save energy consumption in terminal devices, reduce transmission latency, and ensure data security. The experimental results show that IDEANE can avoid duplicated transmission, reduce the network link overhead, improve network throughput, and enhance network robustness and reliability.

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Section: Background and Related Workmentioning

confidence: 99%

Differentiated Content Acquisition Integrating Network Coding and Edge Computing for Industrial Internet

Wang

2023

Applied Sciences

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“…Meanwhile, they perform the movie adaptation/encoding transforms in edge servers to recommend for heterogeneous mobile devices. In [68], the study proposes an edge computing intelligent cache by jointing an innovative composite recommendation algorithm to streaming media recommendation. The recommendation algorithm can decide which media files should be cached at the caching-limited edge servers for users to reduce latency and improve QoS.…”

Section: A Mobile Video Recommendationmentioning

confidence: 99%

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

Sun

et al. 2020

IEEE Access

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Under the explosive growth of information available on the Web, recommender systems have been used as an effective technology to filter useless information and attempt to recommend the most useful items. The proliferation of smart phones, smart wearable devices and other Internet of Thing (IoT) devices has gradually driven many novel emerging services which are latency-sensitive and computation-intensive with a higher quality-of-service. Under such circumstances, the data sources contain four key characteristics (i.e., sparsity, heterogeneity, mobility, volatility). The conventional recommender systems based on cloud computing are incapable of digging the information of user demands. Mobile edge computing is a novel computing paradigm via pushing computation/storage resource from the remote cloud servers to the network edge servers to provide more intelligent and personalized service. This paper comprehensively reviews the state of the art literature on the convergence of recommender systems and edge computing, and identify the future directions along this dimension. This paper can provide an array of new perspectives on the convergence for researchers, practitioners, and tap into the richness of this interdisciplinary research area.

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“…Each user decodes its part of content using simple XOR operations and side information [184]. Caching coded content at the edge of the network has been demonstrated to substantially increase system throughput and global caching gain [185], [186]. Unexplored opportunities exist for the application of ML techniques towards coded caching.…”

Section: B Coded Cachingmentioning

confidence: 99%

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

Shuja,

Bilal,

Alasmary

et al. 2020

Preprint

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Edge networks provide access to a group of proximate users who may have similar content interests. Caching popular content at the edge networks leads to lower latencies while reducing the load on backhaul and core networks with the emergence of high-speed 5G networks. Edge networks are complex and dynamic. User mobility, preferences, and content popularity are the dominant dynamic features of the edge networks. Temporal and social features of content, such as the number of views and likes are applied to estimate the popularity of content from a global perspective. However, such estimates may not be mapped to an edge network with particular social and geographic characteristics. In edge networks, machine learning techniques can be applied to predict content popularity based on user preferences, user mobility based on user location history, cluster users based on similar content interests, and optimize cache placement and replacement strategies provided a set of constraints and predictions about the state of the network. These applications of machine learning can help identify relevant content for an edge network to lower latencies and increase cache hits. This article surveys the application of machine learning techniques for caching content in edge networks. We survey recent state-of-the-art literature and formulate a comprehensive taxonomy based on (a) machine learning technique (application and objective), (b) caching strategy (policy, location, and objective), and edge network (type and delivery strategy). We further survey supporting concepts for optimal edge caching decisions that require the application of machine learning. These supporting concepts are social-awareness, popularity prediction, and community detection in edge networks. A comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy. Moreover, we debate research challenges and future directions for optimal caching decisions and the application of machine learning towards caching in edge networks.

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Cooperative Content Caching for Mobile Edge Computing With Network Coding

Cited by 15 publications

References 15 publications

Differentiated Content Acquisition Integrating Network Coding and Edge Computing for Industrial Internet

Differentiated Content Acquisition Integrating Network Coding and Edge Computing for Industrial Internet

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey

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