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Labeled graph partitioning scheme for distributed edge caching

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 153卷 492-504页

作者： Wang, Pengfei Li, Shiqi Sun, Geng Zhou, Changjun Gao, Chengxi Qiu, Sen Tao, Tiwei Zhang, Qiang Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China Jilin Univ Sch Comp Sci & Technol Changchun 130012 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321000 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Minist Educ Key Lab Intelligent Control & Optimizat Ind Equipm Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Dalian Univ Technol Sch Artificial Intelligence Dalian 116024 Peoples R China

distributed edge caching could address latency and congestion problems in large-scale data access effectively, improving system throughput and performance. However, the lack of specialized edge caching solutions for graph data resulted in low cache hit rates and high data access latency. This limitation stemmed from existing graph partitioning schemes that overlooked the specific requirements of edge caching, such as communication overhead between edge servers, data query frequency, and query patterns. To overcome these challenges, we proposed LGPE, a labeled graph partitioning scheme for distributed edge caching based on frequent query patterns. LGPE generated frequent query patterns according to the user's historical query subgraphs and then performed labeled graph partitioning based on the frequent pattern, ensuring that the labeled graph divided into edge servers had a high hit rate for frequent user queries while satisfying a minimum edge cut. Evaluation on real datasets from various application domains demonstrated that LGPE achieved approximately 40% higher cache hit rates compared to benchmarks like METIS and SCOTCH, and it also performed well in terms of edge cuts in special graph cases.

关键词： Labeled graph Graph partitioning distributed edge caching Frequent query patterns

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distributed edge caching with Content Recommendation in Fog-RANs via Deep Reinforcement Learning

Distributed Edge Caching with Content Recommendation in Fog-...

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IEEE International Conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Yan, Jie Jiang, Yanxiang Zheng, Fuchun Yu, F. Richard Gao, Xiqi You, Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Key Lab Wireless Sensor Network & Commun 865 Changning Rd Shanghai 200050 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Shenzhen 518055 Peoples R China Carleton Univ Sch Informat Technol Ottawa ON Canada

ISBN: (纸本)9781728174402

In this paper, the distributed edge caching problem with dynamic content recommendation is investigated in fog radio access networks (F-RANs). Firstly, the joint caching and recommendation policy is transformed into a 'single' caching policy by incorporating the recommendation policy into the cache policy and the corresponding training complexity is halved. Considering that there is no existing user requests dataset involving content recommendation, we propose a time-varying personalized user request model to describe the fluctuant demands of each user after content recommendation. Then, to maximize the long-term net profit of each fog access point (F-AP), we formulate the caching optimization problem and resort to a reinforcement learning (RL) framework. Finally, to circumvent the curse of dimensionality of RL and speed up the convergence, we propose a double deep Q-network (DDQN) based distributed edge caching algorithm to find the optimal caching policy with content recommendation. Simulation results show that the average net profit of our proposed algorithm is increased by nearly half compared with the traditional methods. Besides, content recommendation could indeed accelerate the convergence and increase cache efficiency.

关键词： Fog radio access networks distributed edge caching content recommendation deep reinforcement learning user request model

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A Mean Field Game-Based distributed edge caching in Fog Radio Access Networks

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IEEE TRANSACTIONS ON COMMUNICATIONS 2020年第3期68卷 1567-1580页

作者： Jiang, Yanxiang Hu, Yabai Bennis, Mehdi Zheng, Fu-Chun You, Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Key Lab Wireless Sensor Network & Commun Shanghai 200050 Peoples R China Univ Oulu Ctr Wireless Commun Oulu 90014 Finland Harbin Inst Technol Shenzhen Sch Elect & Informat Engn Shenzhen 518055 Peoples R China

In this paper, the edge caching optimization problem in fog radio access networks (F-RANs) is investigated. Taking into account time-variant user requests and ultra-dense deployment of fog access points (F-APs), we propose a distributed edge caching scheme to jointly minimize the request service delay and fronthaul traffic load. Considering the interactive relationship among F-APs, we model the optimization problem as a stochastic differential game (SDG) which captures the dynamics of F-AP states. To address both the intractability problem of the SDG and the caching capacity constraint, we propose to solve the optimization problem in a distributive manner. Firstly, a mean field game (MFG) is converted from the original SDG by exploiting the ultra-dense property of F-RANs, and the states of all F-APs are characterized by a mean field distribution. Then, an iterative algorithm is developed that enables each F-AP to obtain the mean field equilibrium and caching control without extra information exchange with other F-APs. Secondly, a fractional knapsack problem is formulated based on the mean field equilibrium, and a greedy algorithm is developed that enables each F-AP to obtain the final caching policy subject to the caching capacity constraint. Simulation results show that the proposed scheme outperforms the baselines.

关键词： Fog radio access networks distributed edge caching mean field game fractional Knapsack problem request service delay Fronthaul traffic load

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distributed edge caching via Reinforcement Learning in Fog Radio Access Networks 89

Distributed Edge Caching via Reinforcement Learning in Fog R...

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89th IEEE Vehicular Technology Conference (VTC Spring)

作者： Lu, Liuyang Jiang, Yanxiang Bennis, Mehdi Ding, Zhiguo Zheng, Fu-Chun You, Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Jiangsu Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Key Lab Wireless Sensor Network & Commun 865 Changning Rd Shanghai 200050 Peoples R China Univ Oulu Ctr Wireless Commun Oulu 90014 Finland Univ Manchester Sch Elect & Elect Engn Manchester Lancs England Harbin Inst Technol Sch Elect & Informat Engn Shenzhen 518055 Peoples R China

ISBN: (纸本)9781728112176

In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated. By considering the unknown spatio-temporal content popularity and user preference, a user request model based on hidden Markov process is proposed to characterize the fluctuant spatio-temporal traffic demands in F-RANs. Then, the Q-learning method based on the reinforcement learning (RL) framework is put forth to seek the optimal caching policy in a distributed manner, which enables fog access points (F-APs) to learn and track the potential dynamic process without extra communications cost. Furthermore, we propose a more efficient Q-learning method with value function approximation (Q-VFA-learning) to reduce complexity and accelerate convergence. Simulation results show that the performance of our proposed method is superior to those of the traditional methods.

关键词： Fog radio access networks distributed edge caching Q-learning content popularity user preference

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distributed edge caching in Ultra-dense Fog Radio Access Networks: A Mean Field Approach 88

Distributed Edge Caching in Ultra-dense Fog Radio Access Net...

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88th IEEE Vehicular Technology Conference (VTC-Fall)

作者： Hu, Yabai Jiang, Yanxiang Bennis, Mehdi Zheng, Fu-Chun Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Jiangsu Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Key Lab Wireless Sensor Network & Commun 865 Changning Rd Shanghai 200050 Peoples R China Univ Oulu Ctr Wireless Commun Oulu 90014 Finland Harbin Inst Technol Sch Elect & Informat Engn Shenzhen 518055 Peoples R China

ISBN: (纸本)9781538663585

In this paper, the edge caching problem in ultra dense fog radio access networks (F-RAN) is investigated. Taking into account time-variant user requests and ultra-dense deployment of fog access points (F-APs), we propose a dynamic distributed edge caching scheme to jointly minimize the request service delay and fronthaul traffic load. Considering the interactive relationship among F-APs, we model the caching optimization problem as a stochastic differential game (SDG) which captures the temporal dynamics of F-AP states and incorporates user requests status. The SDG is further approximated as a mean field game (MFG) by exploiting the ultra-dense property of F-RAN. In the MFG, each F-AP can optimize its caching policy independently through iteratively solving the corresponding partial differential equations without any information exchange with other F-APs. The simulation results show that the proposed edge caching scheme outperforms the baseline schemes under both static and time-variant user requests.

关键词： Terms Fog radio access networks distributed edge caching mean field game request service delay fronthaul traffic load

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distributed caching Control Strategy in Mobile edge Computing: A Mean Field Game Approach 19

Distributed Caching Control Strategy in Mobile Edge Computin...

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19th Annual IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON)

作者： Feng, Hao Guo, Songtao Chen, Chao Jiao, Xianlong Southwest Univ Sch Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

ISBN: (数字)9781665486439

ISBN: (纸本)9781665486439

Mobile edge computing (MEC) is a novel computing paradigm that sinks the computing capacity of cloud servers into edge nodes to reduce network latency. By caching the popular content at small base station (SBS) can reduce the heavy backhaul load and the content retransmission in MEC. However, the dynamic and time-varying of the content requests may increase the network cost. In this paper, we study a distributed edge caching optimization problem in MEC scenario with the spatiotemporal requirements. The considered cache control is described as a stochastic differential game (SDG) in which each SBS defines a caching strategy to reduce the cost in terms of the service delay and backhaul link load. To reduce the computational complexity, the original problem can be transformed into a mean field game (MFG). We propose a caching iterative control algorithm that decouples the information interactions between the general SBS and others with the mean field distribution. In addition, we obtain the optimal caching strategy which achieves the existence and uniqueness of the mean field equilibrium (MFE). Simulation results demonstrate that our proposed algorithm can reduce more storage space and total cost compared to the Kim's approach.

关键词： Mobile edge computing distributed edge caching mean-field game approach spatio-temporal requirements optimal control

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Mean-Field Game Theory Based Optimal caching Control in Mobile edge Computing

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第11期22卷 6585-6598页

作者： Feng, Hao Guo, Songtao Liu, Defang Yang, Yuanyuan Southwest Univ Sch Elect & Informat Engn Chongqing 400715 Peoples R China Shanxi Univ Finance & Econ Sch Informat Taiyuan 030006 Shanxi Peoples R China Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Chongqing Univ Coll Bioengn Key Lab Biorheol Sci & Technol Minist Educ Chongqing 400030 Peoples R China SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA

Mobile edge computing (MEC) can use wireless access network (RAN) to provide users with nearby information technology (IT) services and cloud computing functions, which creates a high-performance and low latency service environment. By caching the popular content at small base station (SBS) can reduce the heavy backhaul load and the content retransmission. However, the time-varying and dynamic of the content requests may lead to the base station to cache the useless contents. In this paper, we study a distributed caching optimization problem in edge networks (ENs) with the spatio-temporal requirements. In the considered ENs, the cache control is described as a stochastic differential game (SDG) in which each SBS defines a caching strategy to reduce the total cost in terms of the service delay and backhaul link load. To reduce the computational complexity, the original optimization problem is transformed into a mean field game (MFG). We propose a distributed caching iterative control algorithm that decouples the information interaction between the general SBS and others through the mean field distribution. In addition, we obtain the optimal edge caching control strategy, while the existence and uniqueness of the mean field equilibrium (MFE) can also be guaranteed. Simulation results demonstrate that our proposed caching control algorithm can average reduce 27.12% storage cost and achieve better performance than other existing schemes.

关键词： Mobile edge computing distributed edge caching mean-field game theory dynamic requirements optimal control

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