With the rapid development of 5G technology,the proportion of video traffic on the Internet is increasing,bringing pressure on the network infrastructure.Edge computing technology provides a feasible solution for opti...With the rapid development of 5G technology,the proportion of video traffic on the Internet is increasing,bringing pressure on the network infrastructure.Edge computing technology provides a feasible solution for optimizing video content distribution.However,the limited edge node cache capacity and dynamic user requests make edge caching more complex.Therefore,we propose a recommendation-driven edge Caching network architecture for the Full life cycle of video streaming(FlyCache)designed to improve users’Quality of Experience(QoE)and reduce backhaul traffic consumption.FlyCache implements intelligent caching management across three key stages:before-playback,during-playback,and after-playback.Specifically,we introduce a cache placement policy for the before-playback stage,a dynamic prefetching and cache admission policy for the during-playback stage,and a progressive cache eviction policy for the after-playback stage.To validate the effectiveness of FlyCache,we developed a user behavior-driven edge caching simulation framework incorporating recommendation mechanisms.Experiments conducted on the MovieLens and synthetic datasets demonstrate that FlyCache outperforms other caching strategies in terms of byte hit rate,backhaul traffic,and delayed startup rate.展开更多
In this paper,unmanned aerial vehicle(UAV)is adopted to serve as aerial base station(ABS)and mobile edge computing(MEC)platform for wire-less communication systems.When Internet of Things devices(IoTDs)cannot cope wit...In this paper,unmanned aerial vehicle(UAV)is adopted to serve as aerial base station(ABS)and mobile edge computing(MEC)platform for wire-less communication systems.When Internet of Things devices(IoTDs)cannot cope with computation-intensive and/or time-sensitive tasks,part of tasks is offloaded to the UAV side,and UAV process them with its own computing resources and caching resources.Thus,the burden of IoTDs gets relieved under the satisfaction of the quality of service(QoS)require-ments.However,owing to the limited resources of UAV,the cost of whole system,i.e.,that is defined as the weighted sum of energy consumption and time de-lay with caching,should be further optimized while the objective function and the constraints are non-convex.Therefore,we first jointly optimize commu-nication resources B,computing resources F and of-floading rates X with alternating iteration and convex optimization method,and then determine the value of caching decision Y with branch-and-bound(BB)al-gorithm.Numerical results show that UAV assisting partial task offloading with content caching is supe-rior to local computing and full offloading mechanism without caching,and meanwhile the cost of whole sys-tem gets further optimized with our proposed scheme.展开更多
Existing wireless networks are flooded with video data transmissions,and the demand for high-speed and low-latency video services continues to surge.This has brought with it challenges to networks in the form of conge...Existing wireless networks are flooded with video data transmissions,and the demand for high-speed and low-latency video services continues to surge.This has brought with it challenges to networks in the form of congestion as well as the need for more resources and more dedicated caching schemes.Recently,Multi-access Edge Computing(MEC)-enabled heterogeneous networks,which leverage edge caches for proximity delivery,have emerged as a promising solution to all of these problems.Designing an effective edge caching scheme is critical to its success,however,in the face of limited resources.We propose a novel Knowledge Graph(KG)-based Dueling Deep Q-Network(KG-DDQN)for cooperative caching in MEC-enabled heterogeneous networks.The KGDDQN scheme leverages a KG to uncover video relations,providing valuable insights into user preferences for the caching scheme.Specifically,the KG guides the selection of related videos as caching candidates(i.e.,actions in the DDQN),thus providing a rich reference for implementing a personalized caching scheme while also improving the decision efficiency of the DDQN.Extensive simulation results validate the convergence effectiveness of the KG-DDQN,and it also outperforms baselines regarding cache hit rate and service delay.展开更多
Efficient edge caching is essential for maximizing utility in video streaming systems,especially under constraints such as limited storage capacity and dynamically fluctuating content popularity.Utility,defined as the...Efficient edge caching is essential for maximizing utility in video streaming systems,especially under constraints such as limited storage capacity and dynamically fluctuating content popularity.Utility,defined as the benefit obtained per unit of cache bandwidth usage,degrades when static or greedy caching strategies fail to adapt to changing demand patterns.To address this,we propose a deep reinforcement learning(DRL)-based caching framework built upon the proximal policy optimization(PPO)algorithm.Our approach formulates edge caching as a sequential decision-making problem and introduces a reward model that balances cache hit performance and utility by prioritizing high-demand,high-quality content while penalizing degraded quality delivery.We construct a realistic synthetic dataset that captures both temporal variations and shifting content popularity to validate our model.Experimental results demonstrate that our proposed method improves utility by up to 135.9%and achieves an average improvement of 22.6%compared to traditional greedy algorithms and long short-term memory(LSTM)-based prediction models.Moreover,our method consistently performs well across a variety of utility functions,workload distributions,and storage limitations,underscoring its adaptability and robustness in dynamic video caching environments.展开更多
Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(...Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(QoS).To overcome this,caching frequently requested content at fog-enabled Road Side Units(RSUs)reduces communication latency.Yet,the limited caching capacity of RSUs makes it impractical to store all contents with varying sizes and popularity.This research proposes an efficient content caching algorithm that adapts to dynamic vehicular demands on highways to maximize request satisfaction.The scheme is evaluated against Intelligent Content Caching(ICC)and Random Caching(RC).The obtained results show that our proposed scheme entertains more contentrequesting vehicles as compared to ICC and RC,with 33%and 41%more downloaded data in 28%and 35%less amount of time from ICC and RC schemes,respectively.展开更多
In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic natu...In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device(D2D)cooperative caching,limiting the reduction of transmission latency.To address this issue,this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning.First,a Transformer-based geolocation prediction model is designed,leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.Then,within a three-tier heterogeneous network,we formulate a latency minimization problem under a D2D cooperative caching architecture and develop a mobility-aware Deep Q-Network(DQN)caching strategy.This strategy takes predicted location information as state input and dynamically adjusts the content distribution across small base stations(SBSs)andmobile users(MUs)to reduce end-to-end delay inmulti-hop content retrieval.Simulation results show that the proposed DQN-based method outperforms other baseline strategies across variousmetrics,achieving a 17.2%reduction in transmission delay compared to DQNmethods withoutmobility integration,thus validating the effectiveness of the joint optimization of location prediction and caching decisions.展开更多
Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently r...Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently realize load balancing.However,such a ubiquitous caching approach may cause problems including duplicate caching and low data diversity,thus reducing the caching efficiency of NDN routers.To mitigate these caching problems and improve the NDN caching efficiency,in this paper,a hierarchical-based sequential caching(HSC)scheme is proposed.In this scheme,the NDN routers in the data transmission path are divided into various levels and data with different request frequencies are cached in distinct router levels.The aim is to cache data with high request frequencies in the router that is closest to the content requester to increase the response probability of the nearby data,improve the data caching efficiency of named data networks,shorten the response time,and reduce cache redundancy.Simulation results show that this scheme can effectively improve the cache hit rate(CHR)and reduce the average request delay(ARD)and average route hop(ARH).展开更多
With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has ensued.The emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also...With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has ensued.The emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also significantly improve user experience.Integrating with the MEC and satellite networks,the network is empowered popular content ubiquitously and seamlessly.Addressing the research gap between multilayer satellite networks and MEC,we study the caching placement problem in this paper.Initially,we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale networks.Considering the constraint on satellite capacity and content propagation delay,the cache placement problem is then formulated and transformed into a markov decision process(MDP),where the content coded caching mechanism is utilized to promote the efficiency of content delivery.Furthermore,a new generic metric,content delivery cost,is proposed to elaborate the performance of caching decision in large-scale networks.Then,we introduce a graph convolutional network(GCN)-based multi-agent advantage actor-critic(A2C)algorithm to optimize the caching decision.Finally,extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.展开更多
Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy....Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.展开更多
Edge caching has emerged as a promising application paradigm in 5G networks,and by building edge networks to cache content,it can alleviate the traffic load brought about by the rapid growth of Internet of Things(IoT)...Edge caching has emerged as a promising application paradigm in 5G networks,and by building edge networks to cache content,it can alleviate the traffic load brought about by the rapid growth of Internet of Things(IoT)services and applications.Due to the limitations of Edge Servers(ESs)and a large number of user demands,how to make the decision and utilize the resources of ESs are significant.In this paper,we aim to minimize the total system energy consumption in a heterogeneous network and formulate the content caching optimization problem as a Mixed Integer Non-Linear Programming(MINLP).To address the optimization problem,a Deep Q-Network(DQN)-based method is proposed to improve the overall performance of the system and reduce the backhaul traffic load.In addition,the DQN-based method can effectively solve the limitation of traditional reinforcement learning(RL)in complex scenarios.Simulation results show that the proposed DQN-based method can greatly outperform other benchmark methods,and significantly improve the cache hit rate and reduce the total system energy consumption in different scenarios.展开更多
In this paper,we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video streaming.The aim is to reduce the average initial buffer delay and improve the q...In this paper,we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video streaming.The aim is to reduce the average initial buffer delay and improve the quality of user experience.Considering the difference between global and local video popularities and the time-varying characteristics of video popularity,a two-stage caching scheme is proposed to push popular videos closer to users and minimize the average initial buffer delay.Based on both long-term content popularity and short-term content popularity,the proposed caching solution is decouple into the proactive cache stage and the cache update stage.In the proactive cache stage,we develop a proactive cache placement algorithm that can be executed in an off-peak period.In the cache update stage,we propose a reactive cache update algorithm to update the existing cache policy to minimize the buffer delay.Simulation results verify that the proposed caching algorithms can reduce the initial buffer delay efficiently.展开更多
One of the challenges of Informationcentric Networking(ICN)is finding the optimal location for caching content and processing users’requests.In this paper,we address this challenge by leveraging Software-defined Netw...One of the challenges of Informationcentric Networking(ICN)is finding the optimal location for caching content and processing users’requests.In this paper,we address this challenge by leveraging Software-defined Networking(SDN)for efficient ICN management.To achieve this,we formulate the problem as a mixed-integer nonlinear programming(MINLP)model,incorporating caching,routing,and load balancing decisions.We explore two distinct scenarios to tackle the problem.Firstly,we solve the problem in an offline mode using the GAMS environment,assuming a stable network state to demonstrate the superior performance of the cacheenabled network compared to non-cache networks.Subsequently,we investigate the problem in an online mode where the network state dynamically changes over time.Given the computational complexity associated with MINLP,we propose the software-defined caching,routing,and load balancing(SDCRL)algorithm as an efficient and scalable solution.Our evaluation demonstrates that the SDCRL algorithm significantly reduces computational time while maintaining results that closely resemble those achieved by GAMS.展开更多
Mobile edge computing(MEC)is a promising paradigm by deploying edge servers(nodes)with computation and storage capacity close to IoT devices.Content Providers can cache data in edge servers and provide services for Io...Mobile edge computing(MEC)is a promising paradigm by deploying edge servers(nodes)with computation and storage capacity close to IoT devices.Content Providers can cache data in edge servers and provide services for IoT devices,which effectively reduces the delay for acquiring data.With the increasing number of IoT devices requesting for services,the spectrum resources are generally limited.In order to effectively meet the challenge of limited spectrum resources,the Non-Orthogonal Multiple Access(NOMA)is proposed to improve the transmission efficiency.In this paper,we consider the caching scenario in a NOMA-enabled MEC system.All the devices compete for the limited resources and tend to minimize their own cost.We formulate the caching problem,and the goal is to minimize the delay cost for each individual device subject to resource constraints.We reformulate the optimization as a non-cooperative game model.We prove the existence of Nash equilibrium(NE)solution in the game model.Then,we design the Game-based Cost-Efficient Edge Caching Algorithm(GCECA)to solve the problem.The effectiveness of our GCECA algorithm is validated by both parameter analysis and comparison experiments.展开更多
Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be dep...Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.展开更多
The emergence of various new services has posed a huge challenge to the existing network architecture.To improve the network delay and backhaul pressure,caching popular contents at the edge of network has been conside...The emergence of various new services has posed a huge challenge to the existing network architecture.To improve the network delay and backhaul pressure,caching popular contents at the edge of network has been considered as a feasible scheme.However,how to efficiently utilize the limited caching resources to cache diverse contents has been confirmed as a tough problem in the past decade.In this paper,considering the time-varying user requests and the heterogeneous content sizes,a user preference aware hierarchical cooperative caching strategy in edge-user caching architecture is proposed.We divide the caching strategy into three phases,that is,the content placement,the content delivery and the content update.In the content placement phase,a cooperative content placement algorithm for local content popularity is designed to cache contents proactively.In the content delivery phase,a cooperative delivery algorithm is proposed to deliver the cached contents.In the content update phase,a content update algorithm is proposed according to the popularity of the contents.Finally,the proposed caching strategy is validated using the MovieLens dataset,and the results reveal that the proposed strategy improves the delay performance by at least 35.3%compared with the other three benchmark strategies.展开更多
Content delivery networks(CDNs)lead to fast content distribution through content caching at specific CDN servers near end users.However,existing CDNs based on infrastructure cannot be employed in special cases,such as...Content delivery networks(CDNs)lead to fast content distribution through content caching at specific CDN servers near end users.However,existing CDNs based on infrastructure cannot be employed in special cases,such as military operations.Thus,a temporary CDN without an existing infrastructure is required.To achieve this goal,we introduce a new CDN for drone-aided ad hoc networks,whereby multiple drones form ad hoc networks and quickly store specific content according to new caching algorithms.Unlike the typical CDN server,the content-caching algorithm in the proposed architecture considers the limited storage capacity of the drone.We present three content distribution algorithms that consider the constraints and mobility of drones.The main contribution of content caching for drone-aided ad hoc networks is to keep partial segments rather than whole content as well as move the drone near to area with a high volume of requests.The proposed scheme is evaluated to demonstrate its feasibility in terms of content acquisition time and utilization in several practical scenarios through simulations.Consequently,acquisition time in CDN to support drone movement is improved by approximately 50%and 40%rather than one in the proposed naive greedy approach as a function of content request interval and size,respectively.展开更多
Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible.In an MEC environment,servers are deployed closer to mobile termin...Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible.In an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user experience.However,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of users.Recently,techniques for caching content at the edge are becoming popular for addressing the above challenges.It is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud servers.However,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity data.To address this challenge,we introduce a novel method for content caching over MEC,i.e.,PRIME.It synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching schedules.Experimental results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.展开更多
The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content.In light of the data-centric aspect of contemporary communication,the info...The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content.In light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of location.If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of resources.Hence,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate prediction.The findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 s.Within a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G networks.These improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving congestion.By improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.展开更多
With the development of internet of vehicles,the traditional centralized content caching mode transmits content through the core network,which causes a large delay and cannot meet the demands for delay-sensitive servi...With the development of internet of vehicles,the traditional centralized content caching mode transmits content through the core network,which causes a large delay and cannot meet the demands for delay-sensitive services.To solve these problems,on basis of vehicle caching network,we propose an edge colla-borative caching scheme.Road side unit(RSU)and mobile edge computing(MEC)are used to collect vehicle information,predict and cache popular content,thereby provide low-latency content delivery services.However,the storage capa-city of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same time.Through content sharing,col-laborative caching can relieve the storage burden on caching servers.Therefore,we integrate RSU and collaborative caching to build a MEC-assisted vehicle edge collaborative caching(MVECC)scheme,so as to realize the collaborative caching among cloud,edge and vehicle.MVECC uses deep reinforcement learning to pre-dict what needs to be cached on RSU,which enables RSUs to cache more popular content.In addition,MVECC also introduces a mobility-aware caching replace-ment scheme at the edge network to reduce redundant cache and improving cache efficiency,which allows RSU to dynamically replace the cached content in response to the mobility of vehicles.The simulation results show that the pro-posed MVECC scheme can improve cache performance in terms of energy cost and content hit rate.展开更多
As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Informatio...As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)[Grant No.62072469].
文摘With the rapid development of 5G technology,the proportion of video traffic on the Internet is increasing,bringing pressure on the network infrastructure.Edge computing technology provides a feasible solution for optimizing video content distribution.However,the limited edge node cache capacity and dynamic user requests make edge caching more complex.Therefore,we propose a recommendation-driven edge Caching network architecture for the Full life cycle of video streaming(FlyCache)designed to improve users’Quality of Experience(QoE)and reduce backhaul traffic consumption.FlyCache implements intelligent caching management across three key stages:before-playback,during-playback,and after-playback.Specifically,we introduce a cache placement policy for the before-playback stage,a dynamic prefetching and cache admission policy for the during-playback stage,and a progressive cache eviction policy for the after-playback stage.To validate the effectiveness of FlyCache,we developed a user behavior-driven edge caching simulation framework incorporating recommendation mechanisms.Experiments conducted on the MovieLens and synthetic datasets demonstrate that FlyCache outperforms other caching strategies in terms of byte hit rate,backhaul traffic,and delayed startup rate.
基金supported by National Natural Science Foundation of China(No.61821001)Science and Technology Key Project of Guangdong Province,China(2019B010157001).
文摘In this paper,unmanned aerial vehicle(UAV)is adopted to serve as aerial base station(ABS)and mobile edge computing(MEC)platform for wire-less communication systems.When Internet of Things devices(IoTDs)cannot cope with computation-intensive and/or time-sensitive tasks,part of tasks is offloaded to the UAV side,and UAV process them with its own computing resources and caching resources.Thus,the burden of IoTDs gets relieved under the satisfaction of the quality of service(QoS)require-ments.However,owing to the limited resources of UAV,the cost of whole system,i.e.,that is defined as the weighted sum of energy consumption and time de-lay with caching,should be further optimized while the objective function and the constraints are non-convex.Therefore,we first jointly optimize commu-nication resources B,computing resources F and of-floading rates X with alternating iteration and convex optimization method,and then determine the value of caching decision Y with branch-and-bound(BB)al-gorithm.Numerical results show that UAV assisting partial task offloading with content caching is supe-rior to local computing and full offloading mechanism without caching,and meanwhile the cost of whole sys-tem gets further optimized with our proposed scheme.
基金supported by the National Natural Science Foundation of China(Nos.62201419,62372357)the Natural Science Foundation of Chongqing(CSTB2023NSCQ-LMX0032)the ISN State Key Laboratory.
文摘Existing wireless networks are flooded with video data transmissions,and the demand for high-speed and low-latency video services continues to surge.This has brought with it challenges to networks in the form of congestion as well as the need for more resources and more dedicated caching schemes.Recently,Multi-access Edge Computing(MEC)-enabled heterogeneous networks,which leverage edge caches for proximity delivery,have emerged as a promising solution to all of these problems.Designing an effective edge caching scheme is critical to its success,however,in the face of limited resources.We propose a novel Knowledge Graph(KG)-based Dueling Deep Q-Network(KG-DDQN)for cooperative caching in MEC-enabled heterogeneous networks.The KGDDQN scheme leverages a KG to uncover video relations,providing valuable insights into user preferences for the caching scheme.Specifically,the KG guides the selection of related videos as caching candidates(i.e.,actions in the DDQN),thus providing a rich reference for implementing a personalized caching scheme while also improving the decision efficiency of the DDQN.Extensive simulation results validate the convergence effectiveness of the KG-DDQN,and it also outperforms baselines regarding cache hit rate and service delay.
文摘Efficient edge caching is essential for maximizing utility in video streaming systems,especially under constraints such as limited storage capacity and dynamically fluctuating content popularity.Utility,defined as the benefit obtained per unit of cache bandwidth usage,degrades when static or greedy caching strategies fail to adapt to changing demand patterns.To address this,we propose a deep reinforcement learning(DRL)-based caching framework built upon the proximal policy optimization(PPO)algorithm.Our approach formulates edge caching as a sequential decision-making problem and introduces a reward model that balances cache hit performance and utility by prioritizing high-demand,high-quality content while penalizing degraded quality delivery.We construct a realistic synthetic dataset that captures both temporal variations and shifting content popularity to validate our model.Experimental results demonstrate that our proposed method improves utility by up to 135.9%and achieves an average improvement of 22.6%compared to traditional greedy algorithms and long short-term memory(LSTM)-based prediction models.Moreover,our method consistently performs well across a variety of utility functions,workload distributions,and storage limitations,underscoring its adaptability and robustness in dynamic video caching environments.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504).
文摘Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(QoS).To overcome this,caching frequently requested content at fog-enabled Road Side Units(RSUs)reduces communication latency.Yet,the limited caching capacity of RSUs makes it impractical to store all contents with varying sizes and popularity.This research proposes an efficient content caching algorithm that adapts to dynamic vehicular demands on highways to maximize request satisfaction.The scheme is evaluated against Intelligent Content Caching(ICC)and Random Caching(RC).The obtained results show that our proposed scheme entertains more contentrequesting vehicles as compared to ICC and RC,with 33%and 41%more downloaded data in 28%and 35%less amount of time from ICC and RC schemes,respectively.
基金supported by the Liaoning Provincial Education Department Fund,grant number JYTZD2023083.
文摘In dynamic 5G network environments,user mobility and heterogeneous network topologies pose dual challenges to the effort of improving performance of mobile edge caching.Existing studies often overlook the dynamic nature of user locations and the potential of device-to-device(D2D)cooperative caching,limiting the reduction of transmission latency.To address this issue,this paper proposes a joint optimization scheme for edge caching that integrates user mobility prediction with deep reinforcement learning.First,a Transformer-based geolocation prediction model is designed,leveraging multi-head attention mechanisms to capture correlations in historical user trajectories for accurate future location prediction.Then,within a three-tier heterogeneous network,we formulate a latency minimization problem under a D2D cooperative caching architecture and develop a mobility-aware Deep Q-Network(DQN)caching strategy.This strategy takes predicted location information as state input and dynamically adjusts the content distribution across small base stations(SBSs)andmobile users(MUs)to reduce end-to-end delay inmulti-hop content retrieval.Simulation results show that the proposed DQN-based method outperforms other baseline strategies across variousmetrics,achieving a 17.2%reduction in transmission delay compared to DQNmethods withoutmobility integration,thus validating the effectiveness of the joint optimization of location prediction and caching decisions.
基金supported in part by the National Natural Science Foundation of China under Grant 61972424 and 62372479in part by the High Value Intellectual Property Cultivation Project of Hubei Province,China,under grant D2021002094+1 种基金in part by JSPS KAKENHI under Grants JP16K00117 and JP19K20250in part by the Leading Initiative for Excellent Young Researchers(LEADER),MEXT,Japan,and KDDI Foundation.
文摘Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently realize load balancing.However,such a ubiquitous caching approach may cause problems including duplicate caching and low data diversity,thus reducing the caching efficiency of NDN routers.To mitigate these caching problems and improve the NDN caching efficiency,in this paper,a hierarchical-based sequential caching(HSC)scheme is proposed.In this scheme,the NDN routers in the data transmission path are divided into various levels and data with different request frequencies are cached in distinct router levels.The aim is to cache data with high request frequencies in the router that is closest to the content requester to increase the response probability of the nearby data,improve the data caching efficiency of named data networks,shorten the response time,and reduce cache redundancy.Simulation results show that this scheme can effectively improve the cache hit rate(CHR)and reduce the average request delay(ARD)and average route hop(ARH).
基金supported by the National Key Research and Development Program of China under Grant 2020YFB1807700the National Natural Science Foundation of China(NSFC)under Grant(No.62201414,62201432)+2 种基金the Qinchuangyuan Project(OCYRCXM-2022-362)the Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University under Grant YJSJ24017the Guangzhou Science and Technology Program under Grant 202201011732。
文摘With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has ensued.The emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also significantly improve user experience.Integrating with the MEC and satellite networks,the network is empowered popular content ubiquitously and seamlessly.Addressing the research gap between multilayer satellite networks and MEC,we study the caching placement problem in this paper.Initially,we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale networks.Considering the constraint on satellite capacity and content propagation delay,the cache placement problem is then formulated and transformed into a markov decision process(MDP),where the content coded caching mechanism is utilized to promote the efficiency of content delivery.Furthermore,a new generic metric,content delivery cost,is proposed to elaborate the performance of caching decision in large-scale networks.Then,we introduce a graph convolutional network(GCN)-based multi-agent advantage actor-critic(A2C)algorithm to optimize the caching decision.Finally,extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.
基金supported by Jilin Provincial Science and Technology Department Natural Science Foundation of China(20210101415JC)Jilin Provincial Science and Technology Department Free exploration research project of China(YDZJ202201ZYTS642).
文摘Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.
基金supported in part by the National Natural Science Foundation of China under Grant 62172255in part by the Outstanding Youth Program of Hubei Natural Science Foundation under Grant 2022CFA080the Wuhan AI Innovation Program(2022010702040056)。
文摘Edge caching has emerged as a promising application paradigm in 5G networks,and by building edge networks to cache content,it can alleviate the traffic load brought about by the rapid growth of Internet of Things(IoT)services and applications.Due to the limitations of Edge Servers(ESs)and a large number of user demands,how to make the decision and utilize the resources of ESs are significant.In this paper,we aim to minimize the total system energy consumption in a heterogeneous network and formulate the content caching optimization problem as a Mixed Integer Non-Linear Programming(MINLP).To address the optimization problem,a Deep Q-Network(DQN)-based method is proposed to improve the overall performance of the system and reduce the backhaul traffic load.In addition,the DQN-based method can effectively solve the limitation of traditional reinforcement learning(RL)in complex scenarios.Simulation results show that the proposed DQN-based method can greatly outperform other benchmark methods,and significantly improve the cache hit rate and reduce the total system energy consumption in different scenarios.
基金the National Natural Science Foundation of China under grants 61901078,61871062,and U20A20157in part by the China University Industry-University-Research Collaborative Innovation Fund(Future Network Innovation Research and Application Project)under grant 2021FNA04008+5 种基金in part by the China Postdoctoral Science Foundation under grant 2022MD713692in part by the Chongqing Postdoctoral Science Special Foundation under grant 2021XM2018in part by the Natural Science Foundation of Chongqing under grant cstc2020jcyj-zdxmX0024in part by University Innovation Research Group of Chongqing under grant CXQT20017in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN202000626in part by the Youth Innovation Group Support Program of ICE Discipline of CQUPT under grant SCIE-QN-2022-04.
文摘In this paper,we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video streaming.The aim is to reduce the average initial buffer delay and improve the quality of user experience.Considering the difference between global and local video popularities and the time-varying characteristics of video popularity,a two-stage caching scheme is proposed to push popular videos closer to users and minimize the average initial buffer delay.Based on both long-term content popularity and short-term content popularity,the proposed caching solution is decouple into the proactive cache stage and the cache update stage.In the proactive cache stage,we develop a proactive cache placement algorithm that can be executed in an off-peak period.In the cache update stage,we propose a reactive cache update algorithm to update the existing cache policy to minimize the buffer delay.Simulation results verify that the proposed caching algorithms can reduce the initial buffer delay efficiently.
文摘One of the challenges of Informationcentric Networking(ICN)is finding the optimal location for caching content and processing users’requests.In this paper,we address this challenge by leveraging Software-defined Networking(SDN)for efficient ICN management.To achieve this,we formulate the problem as a mixed-integer nonlinear programming(MINLP)model,incorporating caching,routing,and load balancing decisions.We explore two distinct scenarios to tackle the problem.Firstly,we solve the problem in an offline mode using the GAMS environment,assuming a stable network state to demonstrate the superior performance of the cacheenabled network compared to non-cache networks.Subsequently,we investigate the problem in an online mode where the network state dynamically changes over time.Given the computational complexity associated with MINLP,we propose the software-defined caching,routing,and load balancing(SDCRL)algorithm as an efficient and scalable solution.Our evaluation demonstrates that the SDCRL algorithm significantly reduces computational time while maintaining results that closely resemble those achieved by GAMS.
基金supported in part by Beijing Natural Science Foundation under Grant L232050in part by the Project of Cultivation for young topmotch Talents of Beijing Municipal Institutions under Grant BPHR202203225in part by Young Elite Scientists Sponsorship Program by BAST under Grant BYESS2023031.
文摘Mobile edge computing(MEC)is a promising paradigm by deploying edge servers(nodes)with computation and storage capacity close to IoT devices.Content Providers can cache data in edge servers and provide services for IoT devices,which effectively reduces the delay for acquiring data.With the increasing number of IoT devices requesting for services,the spectrum resources are generally limited.In order to effectively meet the challenge of limited spectrum resources,the Non-Orthogonal Multiple Access(NOMA)is proposed to improve the transmission efficiency.In this paper,we consider the caching scenario in a NOMA-enabled MEC system.All the devices compete for the limited resources and tend to minimize their own cost.We formulate the caching problem,and the goal is to minimize the delay cost for each individual device subject to resource constraints.We reformulate the optimization as a non-cooperative game model.We prove the existence of Nash equilibrium(NE)solution in the game model.Then,we design the Game-based Cost-Efficient Edge Caching Algorithm(GCECA)to solve the problem.The effectiveness of our GCECA algorithm is validated by both parameter analysis and comparison experiments.
基金supported by the Innovation Fund Project of Jiangxi Normal University(YJS2022065)the Domestic Visiting Program of Jiangxi Normal University.
文摘Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to users.In the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user experience.However,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource constraints.Considering the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant challenge.In response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms.This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content.Furthermore,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and persistence.Empirical results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.
基金supported by Natural Science Foundation of China(Grant 61901070,61801065,62271096,61871062,U20A20157 and 62061007)in part by the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant KJQN202000603 and KJQN201900611)+3 种基金in part by the Natural Science Foundation of Chongqing(Grant CSTB2022NSCQMSX0468,cstc2020jcyjzdxmX0024 and cstc2021jcyjmsxmX0892)in part by University Innovation Research Group of Chongqing(Grant CxQT20017)in part by Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)in part by the Chongqing Graduate Student Scientific Research Innovation Project(CYB22246)。
文摘The emergence of various new services has posed a huge challenge to the existing network architecture.To improve the network delay and backhaul pressure,caching popular contents at the edge of network has been considered as a feasible scheme.However,how to efficiently utilize the limited caching resources to cache diverse contents has been confirmed as a tough problem in the past decade.In this paper,considering the time-varying user requests and the heterogeneous content sizes,a user preference aware hierarchical cooperative caching strategy in edge-user caching architecture is proposed.We divide the caching strategy into three phases,that is,the content placement,the content delivery and the content update.In the content placement phase,a cooperative content placement algorithm for local content popularity is designed to cache contents proactively.In the content delivery phase,a cooperative delivery algorithm is proposed to deliver the cached contents.In the content update phase,a content update algorithm is proposed according to the popularity of the contents.Finally,the proposed caching strategy is validated using the MovieLens dataset,and the results reveal that the proposed strategy improves the delay performance by at least 35.3%compared with the other three benchmark strategies.
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-004)the Institute for Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-II221200,Convergence Security Core Talent Training Business(Chungnam National University)).
文摘Content delivery networks(CDNs)lead to fast content distribution through content caching at specific CDN servers near end users.However,existing CDNs based on infrastructure cannot be employed in special cases,such as military operations.Thus,a temporary CDN without an existing infrastructure is required.To achieve this goal,we introduce a new CDN for drone-aided ad hoc networks,whereby multiple drones form ad hoc networks and quickly store specific content according to new caching algorithms.Unlike the typical CDN server,the content-caching algorithm in the proposed architecture considers the limited storage capacity of the drone.We present three content distribution algorithms that consider the constraints and mobility of drones.The main contribution of content caching for drone-aided ad hoc networks is to keep partial segments rather than whole content as well as move the drone near to area with a high volume of requests.The proposed scheme is evaluated to demonstrate its feasibility in terms of content acquisition time and utilization in several practical scenarios through simulations.Consequently,acquisition time in CDN to support drone movement is improved by approximately 50%and 40%rather than one in the proposed naive greedy approach as a function of content request interval and size,respectively.
文摘Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible.In an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user experience.However,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of users.Recently,techniques for caching content at the edge are becoming popular for addressing the above challenges.It is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud servers.However,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity data.To address this challenge,we introduce a novel method for content caching over MEC,i.e.,PRIME.It synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching schedules.Experimental results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.
基金New Brunswick Innovation Foundation(NBIF)for the financial support of the global project.
文摘The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content.In light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of location.If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of resources.Hence,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate prediction.The findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 s.Within a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G networks.These improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving congestion.By improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.
基金supported by the Science and Technology Project of State Grid Corporation of China:Research and Application of Key Technologies in Virtual Operation of Information and Communication Resources.
文摘With the development of internet of vehicles,the traditional centralized content caching mode transmits content through the core network,which causes a large delay and cannot meet the demands for delay-sensitive services.To solve these problems,on basis of vehicle caching network,we propose an edge colla-borative caching scheme.Road side unit(RSU)and mobile edge computing(MEC)are used to collect vehicle information,predict and cache popular content,thereby provide low-latency content delivery services.However,the storage capa-city of a single RSU severely limits the edge caching performance and cannot handle intensive content requests at the same time.Through content sharing,col-laborative caching can relieve the storage burden on caching servers.Therefore,we integrate RSU and collaborative caching to build a MEC-assisted vehicle edge collaborative caching(MVECC)scheme,so as to realize the collaborative caching among cloud,edge and vehicle.MVECC uses deep reinforcement learning to pre-dict what needs to be cached on RSU,which enables RSUs to cache more popular content.In addition,MVECC also introduces a mobility-aware caching replace-ment scheme at the edge network to reduce redundant cache and improving cache efficiency,which allows RSU to dynamically replace the cached content in response to the mobility of vehicles.The simulation results show that the pro-posed MVECC scheme can improve cache performance in terms of energy cost and content hit rate.
基金supported by the Key R&D Program of Anhui Province in 2020 under Grant No.202004a05020078China Environment for Network Innovations(CENI)under Grant No.2016-000052-73-01-000515.
文摘As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.
