Proactive content caching by exploiting transfer learning for mobile edge computing

To address the vast multimedia traffic volume and requirements of user quality of experience in the next‐generation mobile communication system (5G), it is imperative to develop efficient content caching strategy at mobile network edges, which is deemed as a key technique for 5G. Recent advances in edge/cloud computing and machine learning facilitate efficient content caching for 5G, where mobile edge computing can be exploited to reduce service latency by equipping computation and storage capacity at the edge network. In this paper, we propose a proactive caching mechanism named learning‐based cooperative caching (LECC) strategy based on mobile edge computing architecture to reduce transmission cost while improving user quality of experience for future mobile networks. In LECC, we exploit a transfer learning‐based approach for estimating content popularity and then formulate the proactive caching optimization model. As the optimization problem is NP‐hard, we resort to a greedy algorithm for solving the cache content placement problem. Performance evaluation reveals that LECC can apparently improve content cache hit rate and decrease content delivery latency and transmission cost in comparison with known existing caching strategies.     

Cache Hit Ratio Maximization in Device-to-Device Communications Overlaying Cellular Networks

This paper investigates the content placement problem to maximize the cache hit ratio in device-to-device(D2D)communications overlaying cellular networks.We consider offloading contents by users themselves,D2D communications and multicast,and we analyze the relationship between these offloading methods and the cache hit ratio.Based on this relationship,we formulate the content placement optimization as a cache hit ratio maximization problem,and propose a heuristic algorithm to solve it.Numerical results demonstrate that the proposed scheme can outperform existing schemes in terms of the cache hit ratio.     

Dynamic Probabilistic Caching Algorithm with Content Priorities for Content‐Centric Networks

This paper presents a caching algorithm that offers better reconstructed data quality to the requesters than a probabilistic caching scheme while maintaining comparable network performance. It decides whether an incoming data packet must be cached based on the dynamic caching probability, which is adjusted according to the priorities of content carried by the data packet, the uncertainty of content popularities, and the records of cache events in the router. The adaptation of caching probability depends on the priorities of content, the multiplication factor adaptation, and the addition factor adaptation. The multiplication factor adaptation is computed from an instantaneous cache‐hit ratio, whereas the addition factor adaptation relies on a multiplication factor, popularities of requested contents, a cache‐hit ratio, and a cache‐miss ratio. We evaluate the performance of the caching algorithm by comparing it with previous caching schemes in network simulation. The simulation results indicate that our proposed caching algorithm surpasses previous schemes in terms of data quality and is comparable in terms of network performance.     

Multi-tier cooperative caching in fog radio access network

Aiming at the problem of reducing the load of the backward link in the edge buffer and fog wireless access network technology,a multi-tier cooperative caching scheme in F-RAN was proposed to further reduce the backhaul traffic load.In particular,by considering the network topology,content popularity prediction and link capacity,the optimization problem was decomposed into knapsack subproblems in multi-tiers,and effective greedy algorithms were proposed to solve the corresponding subproblems.Simulation results show that the proposed multi-tier cooperative caching scheme can effectively reduce the backhaul traffic and achieve relatively high cache hit rate.     

QoE-based video segments caching strategy in urban publictransportation system

With the rapid development of vehicle-based applications, entertainment videos have gained popularity for passengers on public vehicles. Therefore, how to provide high quality video service for passengers in typical public transportation scenarios is an essential problem. This paper proposes a quality of experience (QoE)-based video segments caching (QoE-VSC) strategy to guarantee the smooth watching experience of passengers. Consequently, this paper considers a jointly caching scenario where the bus provides the beginning segments of a video, and the road side unit (RSU) offers the remaining for passengers. To evaluate the effectiveness, QoE hit ratio is defined to represent the probability that the bus and RSUs jointly provide passengers with desirable video segments successfully. Furthermore, since passenger volume change will lead to different video preferences, a deep reinforcement learning (DRL) network is trained to generate the segment replacing policy on the video segments cached by the bus server. And the training target of DRL is to maximize the QoE hit ratio, thus enabling more passengers to get the required video. The simulation results prove that the proposed method has a better performance than baseline methods in terms of QoE hit ratio and cache costs.     

一种基于SDN的CCN集中控制缓存决策方法

在软件定义网络(SDN)和内容中心网络(CCN)融合架构下,为了充分利用控制层对网络拓扑和缓存资源的全局感知,在全网中实现缓存资源的优化使用,提出了一种集中控制的缓存决策优化方案.在该方案中,应用粒子群优化算法(PSO)并且根据节点边缘度、节点重要度以及内容流行度对缓存资源和内容进行集中缓存决策,使得内容在不同的节点进行合理的缓存.仿真结果表明,通过评估缓存大小对缓存性能的影响,PSO缓存决策方法取得了比LCE、PROB缓存决策策略更优的缓存命中率和路径延展率,明显降低了缓存节点的缓存替换数,使得缓存达到了整体缓存优化.     

Fast convergence caching replacement algorithm based on dynamic classification for content-centric networks

One of the key research fields of content-centric networking （CCN） is to develop more efficient cache replacement policies to improve the hit ratio of CCN in-network caching. However, most of existing cache strategies designed mainly based on the time or frequency of content access, can not properly deal with the problem of the dynamicity of content popularity in the network. In this paper, we propose a fast convergence caching replacement algorithm based on dynamic classification method for CCN, named as FCDC. It develops a dynamic classification method to reduce the time complexity of cache inquiry, which achieves a higher caching hit rate in comparison to random classification method under dynamic change of content popularity. Meanwhile, in order to relieve the influence brought about by dynamic content popularity, it designs a weighting function to speed up cache hit rate convergence in the CCN router. Experimental results show that the proposed scheme outperforms the replacement policies related to least recently used （LRU） and recent usage frequency （RUF） in cache hit rate and resiliency when content popularity in the network varies.     

命名数据网络中基于内容请求相关性的协作缓存算法

针对命名数据网络(Named Data Networking, NDN)存储空间的有效利用和应答内容的高效缓存问题,该文采用差异化缓存的方式,提出一种依据内容请求序列相关性的协作缓存算法。在内容请求中,预先发送对于后续相关数据单元的并行预测请求,增大内容请求的就近响应概率;缓存决策时,提出联合空间存储位置与缓存驻留时间的2维差异化缓存策略。根据内容活跃度的变化趋势,空间维度上逐跳推进内容存储位置,时间维度上动态调整内容缓存时间,以渐进式的方式将真正流行的请求内容推送至网络边缘存储。该算法减小了内容请求时延和缓存冗余,提高了缓存命中率,仿真结果验证了其有效性。     

Dynamic theoretic proactive caching placement game for energy saving in H‐CRAN

Edge caching is an effective feature of the next 5G network to guarantee the availability of the service content and a reduced time response for the user. However, the placement of the cache content remains an issue to fully take advantage of edge caching. In this paper, we address the proactive caching problem in Heterogeneous Cloud Radio Access Network (H‐CRAN) from a game theoretic point of view. The problem is formulated as a bargaining game where the remote radio heads (RRHs) dynamically negotiate and decide which content to cache in which RRH under energy saving and cache capacity constraints. The Pareto optimal equilibrium is proved for the cooperative game by the iterative Nash bargaining algorithm. We compare between cooperative and noncooperative proactive caching games and demonstrate how the selfishness of different players can affect the overall system performance. We also showed that our cooperative proactive caching game improves the energy consumption of 40% as compared with noncooperative game and of 68% to no‐game strategy. Moreover, the number of satisfied requests at the RRHs with the proposed cooperative proactive caching scheme is significantly increased.     

Cache‐at‐relay: energy‐efficient content placement for next‐generation wireless relays

Uploading and downloading content have recently become one of the major reasons for the growth of Internet traffic volume. With the increasing popularity of social networking tools and their video upload/download applications, as well as the connectivity enhancements in wireless networks, it has become a second nature for mobile users to access on‐demand content on‐the‐go. Urban hot spots, usually implemented via wireless relays, answer the bandwidth need of those users. On the other hand, the same popular contents are usually acquired by a large number of users at different times, and fetching those from the initial content source each and every time makes inefficient use of network resources. In‐network caching provides a solution to this problem by bringing contents closer to the users. Although in‐network caching has been previously studied from latency and transport energy minimization perspectives, energy‐efficient schemes to prolong user equipment lifetime have not been considered. To address this problem, we propose the cache‐at‐relay (CAR) scheme, which utilizes wireless relays for in‐network caching of popular contents with content access and caching energy minimization objectives. CAR consists of three integer linear programming models, namely, select relay, place content, and place relay, which respectively solve content access energy minimization, joint minimization of content access and caching energy, and joint minimization of content access energy and relay deployment cost problems. We have shown that place relay significantly minimizes the content access energy consumption of user equipments, while place content provides a compromise between the content access and the caching energy budgets of the network. Copyright © 2015 John Wiley & Sons, Ltd.     

Community oriented in‐network caching and edge caching for over‐the‐top services in adaptive network conditions to improve performance

Over‐the‐top (OTT) services such as Netflix, Amazon Prime, and YouTube generate the most dominant form of traffic on the Internet today. There is increasingly high demand for resource intensive 3D contents, interactive media, 360 media, and user‐generated contents. As the amount of contents keep increasing in multiple folds, it is important to cache contents intelligently. Caching algorithm needs to exploit in‐network caching, community‐based pre‐caching, and a combined approach. Hence, we survey CDN‐based edge caching infrastructures including OpenConnect (Netflix) and Google Edge, followed by CCN based in‐network caching. We implement and compare four different approaches for caching contents including (1) in‐network caching, (2) edge caching, (3) community‐based in‐network caching, and (4) community‐based edge caching. We run our algorithms on adaptive network conditions with different topologies, cache size, content popularity, and request arrivals in and compared the delay for all these four approaches. We verify our model by calculating important performance parameters including hop count, redundancy, and hop count variances. Hopcount is an important performance parameter as it influences the processing, queuing, and transmission delays. We focus on determining if an in‐network caching approach is any better than edge caching. We reach several conclusions. First, in most of the scenarios, community‐based in‐network caching performs the best. Second, if the cache size is lesser than 30% of the total content size then community‐based edge caching is better for less popular contents. Finally, our statistical analysis also reveals that a community‐based edge caching mechanism is least affected by varying cache sizes and dynamic user behavior, which makes it a better choice for providing Service Level Agreement.     

A unifying co‐operative web caching architecture

Network caching of objects has become a standard way of reducing network traffic and latency in the web. However, web caches exhibit poor performance with a hit rate of about 30%. A solution to improve this hit rate is to have a group of proxies form co‐operation where objects can be cached for later retrieval. A co‐operative cache system includes protocols for hierarchical and transversal caching. The drawback of such a system lies in the resulting network load due to the number of messages that need to be exchanged to locate an object. This paper proposes a new co‐operative web caching architecture, which unifies previous methods of web caching. Performance results shows that the architecture achieve up to 70% co‐operative hit rate and accesses the cached object in at most two hops. Moreover, the architecture is scalable with low traffic and database overhead. Copyright © 2002 John Wiley & Sons, Ltd.     

内容中心网络中基于内容迁移的协作缓存机制

内容中心网络(CCN)是为了适应未来网络通信模式的转变,提供对可扩展和高效内容获取的原生支持而提出一种新型的网络体系架构,内容缓存机制是其研究的关键问题之一。现有机制在缓存节点的选择时往往过于集中,缓存负载分布严重不均,大大降低了网络资源利用率以及系统的缓存性能。该文提出一种基于缓存迁移的协作缓存机制,首先在缓存节点选择时考虑节点的中心性保证内容尽可能缓存在位置更重要的节点。同时,在缓存压力过大时,通过可用缓存空间大小、缓存替换率以及网络连接的稳定性等信息选择合适的邻居节点进行缓存内容的转移,充分利用邻居资源实现负载分担。仿真结果表明该机制能有效地改善缓存负载在节点上分布的均衡性,提高缓存命中率和缓存资源利用率并降低平均接入代价。     

Incentive edge caching in software‐defined internet of vehicles: A Stackelberg game approach

In this paper, we investigate an incentive edge caching mechanism for an internet of vehicles (IoV) system based on the paradigm of software‐defined networking (SDN). We start by proposing a distributed SDN‐based IoV architecture. Then, based on this architecture, we focus on the economic side of caching by considering competitive cache‐enablers market composed of one content provider (CP) and multiple mobile network operators (MNOs). Each MNO manages a set of cache‐enabled small base stations (SBS). The CP incites the MNOs to store its popular contents in cache‐enabled SBSs with highest access probability to enhance the satisfaction of its users. By leasing their cache‐enabled SBSs, the MNOs aim to make more monetary profit. We formulate the interaction between the CP and the MNOs, using a Stackelberg game, where the CP acts first as the leader by announcing the popular content quantity that it which to cache and fixing the caching popularity threshold, a minimum access probability under it a content cannot be cached. Then, MNOs act subsequently as followers responding by the content quantity they accept to cache and the corresponding caching price. A noncooperative subgame is formulated to model the competition between the followers on the CP's limited content quantity. We analyze the leader and the follower's optimization problems, and we prove the Stackelberg equilibrium (SE). Simulation results show that our game‐based incentive caching model achieves optimal utilities and outperforms other incentive caching mechanisms with monopoly cache‐enablers whilst enhancing 30% of the user's satisfaction and reducing the caching cost.     

Packet‐level‐based traffic aggregation to optimize NDN content delivery

In recent years, named data networking (NDN) has been accepted as the most popular future paradigm and attracted much attention, of which the routing model contains interest forwarding and content delivery. However, interest forwarding is far from the bottleneck of routing optimization; instead, the study on content delivery can greatly promote routing performance. Although many proposals on content delivery have been investigated, they have not considered packet‐level caching and deep traffic aggregation, which goes against the performance optimization of content delivery. In this paper, we propose a packet‐level‐based traffic aggregation (PLTA) scheme to optimize NDN content delivery. At first, the packet format is devised, and data plane development kit (DPDK) is used to ensure same size for each packet. Then, the whole delivery scheme with traffic aggregation consideration is presented. The simulation is driven by the real YouTube dataset over Deltacom, NSFNET, and CERNET topologies, and the experimental results demonstrate that the proposed PLTA has better delivery performance than three baselines in terms of cache hit ratio, delivery delay, network load, and energy efficiency.     

Replication enabled distributed cache invalidation method: replication enabled distributed cache management system for wireless mobile networks

This work proposes a replication scheme that is implemented on top of a previously proposed system for MANETs that cache submitted queries in special nodes, called query directories, and uses them to locate the data (responses) that are stored in the nodes that first request them, called caching nodes. The system, which was named distributed cache invalidation method (DCIM), includes client‐based mechanisms for keeping the cached data consistent with the data source. In this work, we extend DCIM to handle cache replicas inside the MANET. For this purpose, we utilize a push‐based approach within the MANET to propagate the server updates to replicas inside the network. The result is a hybrid approach that utilizes the benefits of pull approaches for client server communication and those of push approaches inside the network between the replicas. The approach is analyzed analytically, and the appropriate number of replicas is obtained, where it was concluded that full replication of the indices of data items at the query directory and two‐partial replication of the data items themselves makes most sense. Simulation results based on ns2 demonstrate the ability of the added replication scheme to lower delays and improve hit ration at the cost of mild increases in overhead traffic. Copyright © 2013 John Wiley & Sons, Ltd.     

Technique for Estimating the Number of Active Flows in High‐Speed Networks

The online collection of coarse‐grained traffic information, such as the total number of flows, is gaining in importance due to a wide range of applications, such as congestion control and network security. In this paper, we focus on an active queue management scheme called SRED since it estimates the number of active flows and uses the quantity to indicate the level of congestion. However, SRED has several limitations, such as instability in estimating the number of active flows and underestimation of active flows in the presence of non‐responsive traffic. We present a Markov model to examine the capability of SRED in estimating the number of flows. We show how the SRED cache hit rate can be used to quantify the number of active flows. We then propose a modified SRED scheme, called hash‐based two‐level caching (HaTCh), which uses hashing and a two‐level caching mechanism to accurately estimate the number of active flows under various workloads. Simulation results indicate that the proposed scheme provides a more accurate estimation of the number of active flows than SRED, stabilizes the estimation with respect to workload fluctuations, and prevents performance degradation by efficiently isolating non‐responsive flows.     

Reducing wireless multi‐hop delay via RSU re‐routing in vehicular wireless networks

Vehicular wireless networks offer wireless multi‐hop communications between vehicles and roadside units (RSUs). To reduce deployment cost, the distance between two RSUs could be long; that is, the communications between an RSU and a vehicle may be carried out through multi‐hops among intermediate vehicles. When a vehicle is driven from one RSU to another, the wireless multi‐hop delay becomes more serious as the number of multi‐hop relays increases. The wireless multi‐hop delay is critical for some emergency service. For instance, in a traffic accident, when a patient was sent to the hospital by ambulance, the life information of the patient must be transmitted to the hospital on time through the multi‐hop wireless network. If the ambulance is moved from one RSU to another, the wireless multi‐hop delay becomes more and more serious as the ambulance is closing to another RSU. In this paper, we propose an RSU re‐routing strategy that dynamically alters multi‐hop communications until the best RSU with the shortest path using location information is found. Moreover, we compare the proposed strategy with the existing strategy in terms of broadcasting costs, re‐routing delay, and wireless multi‐hop delay of data transmission. Performance results show that the proposed strategy can reduce the wireless multi‐hop delay significantly. Copyright © 2014 John Wiley & Sons, Ltd.     

基于BP和RBF神经网络的C-V2X无线资源管理

为了提升蜂窝车联网（Cellular Vehicle-to-Everything,C-V2X）资源复用的有效性和降低终端间的干扰，提出通过神经网络对未来时刻车流量的预测辅助无线资源管理方案。依据车载单元（On Board Unit,OBU）与路侧单元（Road Side Unit,RSU）间的车联网消息，获取RSU覆盖区域内各时刻的车流情况，分别采用BP（Back Propagation）神经网络和RBF（Radial Basis Function）神经网络进行短时交通流预测。RSU根据预测结果进行自适应分簇，簇间复用相同资源，簇内进行资源池的划分，RSU覆盖内的OBU在划分的资源池中选择发送资源，从而减少终端间的干扰，并保证热点区域车辆拥有更多的资源。仿真结果表明，在道路交通拥塞的场景下，所提方案的数据包接收率较标准中的方案提升14%，较典型文献方案提升10%，保证了通信的可靠性。     

Cooperative caching game based on social trust for D2D communication networks

Content sharing via device‐to‐device (D2D) communications has become a promising method to increase system throughput and reduce traffic load. Due to the characteristic of spectrum sharing in D2D network, confidentiality is becoming a key issue in content transmission. Secure communication in D2D networks is generally guaranteed by a physical‐layer security mechanism. However, this method sacrifices the system transmission rate while ensuring security. Since mobile devices are carried by humans, we can leverage their trust relations to enhance the security of communications. As much, considering the psychology structure and social attributes of mobile users, we build a multidimensional trust evaluation mechanism to evaluate the trust relationship between users, and we pick out the trusted users based on the decision‐theoretic rough sets. By sharing content only between trust users, we can enhance the security of content transmissions without relying on physical‐layer security measures. Meanwhile, content caching is now widely used to improve accessing efficiency and reduce traffic load on cellular networks. However, caching content for other users incurs additional cost, which results in selfish and noncooperative behavior in users. Considering such selfishness, we introduce a cooperative caching game based on multidimensional trust relations to motivate users to cache contents for other devices. In this game, the trust relations and physical distance between two users are considered to formulate the cost function. Furthermore, we introduce an incentive caching algorithm based on social trust to minimize the total cost in the D2D network.