Cache Management for 5G Cloud Radio Access Networks

Tsai, Chin; Moh, Melody

doi:10.1145/3164541.3164559

Cited by 15 publications

(28 citation statements)

References 16 publications

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“…By using MDS codes, it is possible to obtain the optimal caching location and reduce the backhaul rate [48]. To efficiently manage caches in a 5G Cloud Radio Access Networks (C-RAN) environment, exponential decay and the Analytical Hierarchy Process (AHP) are applied to achieve multi-level mobility and the smallest amount of feedback [15]. Although it is possible to derive a more optimal caching method by considering various factors and a new design, it is more important for an algorithm to be lightweight and agile as edge nodes have limited resources.…”

Section: Edge Caching and Edge Data Management Techniques Analysismentioning

confidence: 99%

“…Instead, it has studied applying relatively heavy tasks to edge nodes, such as machine learning [1][2][3][4][5][6], high-quality video streaming [2][3][4][5][7][8][9][10][11][12][13][14], and AR/VR [1,3,7]. Furthermore, existing caching research has focused on pre-deploying data to an edge node without fully considering the dynamic scale-up and -down of the cache [3,[15][16][17][18][19], which are related to the pay-as-you-go pricing policy of cloud computing [20,21].…”

Section: Introductionmentioning

confidence: 99%

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EDCrammer: An Efficient Caching Rate-Control Algorithm for Streaming Data on Resource-Limited Edge Nodes

Kim

Huh

2019

Applied Sciences

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This paper explores data caching as a key factor of edge computing. State-of-the-art research of data caching on edge nodes mainly considers reactive and proactive caching, and machine learning based caching, which could be a heavy task for edge nodes. However, edge nodes usually have relatively lower computing resources than cloud datacenters as those are geo-distributed from the administrator. Therefore, a caching algorithm should be lightweight for saving computing resources on edge nodes. In addition, the data caching should be agile because it has to support high-quality services on edge nodes. Accordingly, this paper proposes a lightweight, agile caching algorithm, EDCrammer (Efficient Data Crammer), which performs agile operations to control caching rate for streaming data by using the enhanced PID (Proportional-Integral-Differential) controller. Experimental results using this lightweight, agile caching algorithm show its significant value in each scenario. In four common scenarios, the desired cache utilization was reached in 1.1 s on average and then maintained within a 4–7% deviation. The cache hit ratio is about 96%, and the optimal cache capacity is around 1.5 MB. Thus, EDCrammer can help distribute the streaming data traffic to the edge nodes, mitigate the uplink load on the central cloud, and ultimately provide users with high-quality video services. We also hope that EDCrammer can improve overall service quality in 5G environment, Augmented Reality/Virtual Reality (AR/VR), Intelligent Transportation System (ITS), Internet of Things (IoT), etc.

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Section: Edge Caching and Edge Data Management Techniques Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EDCrammer: An Efficient Caching Rate-Control Algorithm for Streaming Data on Resource-Limited Edge Nodes

Kim

Huh

2019

Applied Sciences

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“…So, the idea is to increase the evaluation of the proposed algorithms, which is followed by the conclusion section. This work is a continuation of our research on cloud computing [8,16], CRAN [10,11,20,22,23,24], edge and fog computing [3,5,14], and IOT, mobile and 5G networks [14,18,19,21]. Figure 1 [24,25].…”

Section: List Of Abbreviationsmentioning

confidence: 90%

“…These technologies are introduced to handle the traffic data volume caused by the rapidly growing Internet of Things (IoT) and mobile devices [7,24]. There is a cache involved in both CRAN and MEC technologies to increase the speed of cellular network services [7,11,24,26].…”

Section: List Of Abbreviationsmentioning

confidence: 99%

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Dynamic Hierarchical Cache Management for Cloud RAN and Multi- Access Edge Computing in 5G Networks

Rajendiran¹

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Cloud Radio Access Networks (CRAN) and Multi-Access Edge Computing (MEC) are two of the many emerging technologies that are proposed for 5G mobile networks. CRAN provides scalability, flexibility, and better resource utilization to support the dramatic increase of Internet of Things (IoT) and mobile devices. MEC aims to provide low latency, high bandwidth and realtime access to radio networks. Cloud architecture is built on top of traditional Radio Access Networks (RAN) to bring the idea of CRAN and in MEC, cloud computing services are brought near users to improve the user's experiences. A cache is added in both CRAN and MEC architectures to speed up the mobile network services. This research focuses on cache management of CRAN and MEC because there is a necessity to manage and utilize this limited cache resource efficiently. First, a new cache management algorithm, H-EXD-AHP (Hierarchical Exponential Decay and Analytical Hierarchy Process), is proposed to improve the existing EXD-AHP algorithm. Next, this paper designs three dynamic cache management algorithms and they are implemented on the proposed algorithm: H-EXD-AHP and an existing algorithm: H-PBPS (Hierarchical Probability Based Popularity Scoring). In these proposed designs, cache sizes of the different Service Level Agreement (SLA) users are adjusted dynamically to meet the guaranteed cache hit rate set for their corresponding SLA users. The minimum guarantee of cache hit rate is for our setting. Net neutrality, prioritized treatment will be in common practice. Finally, performance evaluation results show that these designs achieve the guaranteed cache hit rate for differentiated users according to their SLA. ACKNOWLEDGMENTS I would like to express my deepest gratitude to my project advisor, Dr. Melody Moh, for her excellent guidance, and encouragement throughout the course of this project. My sincere thanks to my committee members, Dr. Robert Chun, and Dr. Teng Moh for their useful comments, and their valuable time for reviewing my work. Special thanks to my husband, for his endless support, and understanding, and for never giving up on me. Special love to my son for his patience, and being flexible to my schedule, and his love for me helped me to keep going. I would like to say my heartily thank you to my mom for her motivation and blessings. Last but not the least, a big thank you for my wonderful family and friends for their moral support. vi

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Adaptive Hierarchical Cache Management for Cloud RAN and Multi-access Edge Computing in 5G Networks

Rajendiran

Moh

2019

Advances in Intelligent Systems and Computing

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Cache Management for 5G Cloud Radio Access Networks

Cited by 15 publications

References 16 publications

EDCrammer: An Efficient Caching Rate-Control Algorithm for Streaming Data on Resource-Limited Edge Nodes

EDCrammer: An Efficient Caching Rate-Control Algorithm for Streaming Data on Resource-Limited Edge Nodes

Dynamic Hierarchical Cache Management for Cloud RAN and Multi- Access Edge Computing in 5G Networks

Adaptive Hierarchical Cache Management for Cloud RAN and Multi-access Edge Computing in 5G Networks

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