Cooperative Hierarchical Caching in 5G Cloud Radio Access Networks

Tran, Tuyen X.; Hajisami, Abolfazl; Pompili, Dario

doi:10.1109/mnet.2017.1600307

Cited by 104 publications

(58 citation statements)

References 15 publications

(21 reference statements)

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“…In [6], the authors study the effective capacity of caching using stochastic geometry and shed light on the main benefits of caching. The work in [7] proposes a novel cooperative hierarchical caching framework for the CRAN to improve the hit ratio of caching and reduce backhaul traffic load by jointly caching content at both the BBU level and RRH level. In [8], the authors analyzed the asymptotic limits of caching using meanfield theory.…”

Section: Introductionmentioning

confidence: 99%

Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks With Mobile Users

Chen

Saad

Yin

et al. 2017

IEEE Trans. Wireless Commun.

166

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In this paper, the problem of proactive caching is studied for cloud radio access networks (CRANs). In the studied model, the baseband units (BBUs) can predict the content request distribution and mobility pattern of each user, determine which content to cache at remote radio heads and BBUs. This problem is formulated as an optimization problem which jointly incorporates backhaul and fronthaul loads and content caching. To solve this problem, an algorithm that combines the machine learning framework of echo state networks with sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs can predict each user's content request distribution and mobility pattern while having only limited information on the network's and user's state. In order to predict each user's periodic mobility pattern with minimal complexity, the memory capacity of the corresponding ESN is derived for a periodic input. This memory capacity is shown to capture the maximum amount of user information needed for the proposed ESN model. Then, a sublinear algorithm is proposed to determine which content to cache while using limited content request distribution samples. Simulation results using real data from Youku and the Beijing University of Posts and Telecommunications show that the proposed approach yields significant gains, in terms of sum effective capacity, that reach up to 27.8% and 30.7%, respectively, compared to random caching with clustering and random caching without clustering algorithm.

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Section: Introductionmentioning

confidence: 99%

Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks With Mobile Users

Chen

Saad

Yin

et al. 2017

IEEE Trans. Wireless Commun.

166

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show abstract

“…For each simulation, we randomly generate 10, 000 requests at each BS. The end-to-end latency of fetching video content from the local BS, from a neighboring BS, and from the origin content server are randomly assigned following the uniform distribution in the ranges [5,10](ms), [20,50](ms), and [100, 200](ms), respectively [31]. The backhaul cost d j0 's and d jk 's are set equal to the corresponding delays.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Different from the ICN settings, considerable research efforts have focused on content caching in wireless networks [14]- [16], and on exploiting the backhaul links connecting the BSs for collaborative caching [17], [18]. Recently, the authors in [19], [20] proposes a cooperative hierarchical caching in a Cloud Radio Access Network (C-RAN) where the cloudcache is introduced as a bridging layer between the edgebased and core-based caching schemes. The authors propose a low complexity, online cache management strategy, consisting of a proactive cache distribution algorithm and a reactive cache replacement algorithm, to minimize the average delay cost of all content requests.…”

Section: Introductionmentioning

confidence: 99%

Collaborative multi-bitrate video caching and processing in Mobile-Edge Computing networks

Tran

Pandey

Hajisami

et al. 2017

2017 13th Annual Conference on Wireless on-Demand Network Systems and Services (WONS)

Self Cite

140

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Recently, Mobile-Edge Computing (MEC) has arisen as an emerging paradigm that extends cloud-computing capabilities to the edge of the Radio Access Network (RAN) by deploying MEC servers right at the Base Stations (BSs). In this paper, we envision a collaborative joint caching and processing strategy for on-demand video streaming in MEC networks. Our design aims at enhancing the widely used Adaptive BitRate (ABR) streaming technology, where multiple bitrate versions of a video can be delivered so as to adapt to the heterogeneity of user capabilities and the varying of network condition. The proposed strategy faces two main challenges: (i) not only the videos but their appropriate bitrate versions have to be effectively selected to store in the caches, and (ii) the transcoding relationships among different versions need to be taken into account to effectively utilize the processing capacity at the MEC servers. To this end, we formulate the collaborative joint caching and processing problem as an Integer Linear Program (ILP) that minimizes the backhaul network cost, subject to the cache storage and processing capacity constraints. Due to the NPcompleteness of the problem and the impractical overheads of the existing offline approaches, we propose a novel online algorithm that makes cache placement and video scheduling decisions upon the arrival of each new request. Extensive simulations results demonstrate the significant performance improvement of the proposed strategy over traditional approaches in terms of cache hit ratio increase, backhaul traffic and initial access delay reduction.Index Terms-Collaborative caching; adaptive bitrate streaming; multi-bitrate video; mobile-edge computing; joint caching and processing.

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“…The caching is a new opportunity in 5G wireless resource allocation, which researchers are working at [90,91]. The idea is to offload traffic by caching contents in the edge (i.e., radio access) of the network, such as the different base stations, the relays, and potentially even in the enduser devices.…”

Section: Cachingmentioning

confidence: 99%

Orchestration and Control in Software‐Defined 5G Networks: Research Challenges

Nencioni

Garroppo

Gonzalez

et al. 2018

Wireless Communications and Mobile Computing

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The fifth generation (5G) of cellular networks promises to be a major step in the evolution of wireless technology. 5G is planned to be used in a very broad set of application scenarios. These scenarios have strict heterogeneous requirements that will be accomplished by enhancements on the radio access network and a collection of innovative wireless technologies. Softwarization technologies, such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), will play a key role in integrating these different technologies. Network slicing emerges as a cost-efficient solution for the implementation of the diverse 5G requirements and verticals. The 5G radio access and core networks will be based on a SDN/NFV infrastructure, which will be able to orchestrate the resources and control the network in order to efficiently and flexibly and with scalability provide network services. In this paper, we present the up-to-date status of the software-defined 5G radio access and core networks and a broad range of future research challenges on the orchestration and control aspects.

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Cooperative Hierarchical Caching in 5G Cloud Radio Access Networks

Cited by 104 publications

References 15 publications

Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks With Mobile Users

Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks With Mobile Users

Collaborative multi-bitrate video caching and processing in Mobile-Edge Computing networks

Orchestration and Control in Software‐Defined 5G Networks: Research Challenges

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