Content Caching Research Articles

An Intelligent Caching Strategy Considering Time-Space Characteristics in Vehicular Named Data Networks

In the Internet of Vehicles (IoV), the classic TCP/IP still plays an important role for data transmission, traffic control and address assignment. However, with increasing requirements on content retrieve efficiency in IoV, the drawbacks of traditional TCP/IP stacks, such as weak scalability in large networks, low efficiency in dense environment and unreliable addressing in high mobility circumstance, have incurred significant performance degradations in vehicular environments. Fortunately, the emerging Named Data Network (NDN) technology provides a good choice to address above issues in vehicular environment by proving content caching capability with introduced content store module, and boosts the research activity of Vehicular Named Data Network (VNDN) in the last few years. In this paper, to improve the service performance, e.g., reducing the delay of data acquisition, a data caching scheme is proposed by taking the spatial-temporal characteristics of data into account. At first, we divided the data in a VNDN into emergency safety message, traffic efficiency message and service message, according to the application requirements. Then, we analyze the spatial-temporal characteristics of these three message categories and design the caching strategy according to these characteristics. Experimental results from NDNSim platform show that our designed scheme has an approximately 50% performance enhancement compared with Leave Copy Everywhere (LCE), Pro(0.7), and Pro(0.2) data caching protocols in terms of average hit rate, average hop count and average cache replacement times, which verifies the reliability and effectiveness of our proposed data caching scheme.

A new GPU-based corrected explicit-implicit domain decomposition scheme for convection-dominated diffusion problems

In this study, a class of GPU-based corrected explicit-implicit domain decomposition schemes (GCEIDD) is proposed to accelerate the solution of convection-dominated diffusion problems on GPUs. All of these methods take advantage of the fractional steps and corrected explicit-implicit domain decomposition techniques. In each sweep, the domain is decomposed into many strip-divided subdomains. The aim is to reduce the size and increase the number of tri-diagonal systems to provide enough parallelism to keep the GPU occupied. In different steps of the algorithms, we use low-complexity schemes with a high degree of parallelism. The generic form of GCEIDD allows different schemes for the prediction, correction, and one-dimensional implicit solution. We have proposed two methods based on modified upwind and characteristics finite difference and a combined method that bypasses the limitations of these two. Also, important aspects of CUDA programming, which are critical to the implementation of the GCEIDD algorithm, are discussed in detail. For solving tri-diagonal systems, a three-stage strategy is proposed, which creates a balance between occupancy, cache hit rate, and shared memory usage and reduces cache contentions. Results show that GCEIDD has good accuracy and stability even when the number of subdomains is very large. The proposed method can accelerate the solution by a factor of up to 3 compared with the GPU implementation of the classic fractional step methods.

Improving Fairness for SSD Devices through DRAM Over-Provisioning Cache Management

Modern NVMe SSDs have been widely deployed in multi-tenant cloud computing environments or multi-programming systems. When multiple applications concurrently access one SSD hardware, unfairness within the shared SSD will slow down the application significantly and lead to a violation of service level objectives. However, traditional data cache management within SSDs mainly focuses on improving cache hit ratio, which causes data cache contention and sacrifices fairness among multiple applications. In this paper, we propose a DRAM-based Over-Provisioning (OP) cache management mechanism, named Justitia, to reduce data cache contention and improve fairness for modern SSDs. Justitia consists of two stages including <i>Static-OP</i> stage and <i>Dynamic-OP</i> stage. Through the novel OP mechanism in the two stages, Justitia reduces the max slowdown by <inline-formula><tex-math notation="LaTeX">$4.5\times$</tex-math></inline-formula> on average. At the same time, Justitia increases fairness by <inline-formula><tex-math notation="LaTeX">$20.6\times$</tex-math></inline-formula> and buffer hit ratio by <inline-formula><tex-math notation="LaTeX">$19.6\%$</tex-math></inline-formula> averagely, compared with the traditional shared mechanism.

Caching of popular content at F‐RAN for mmWave new radio

SummaryFog computing allows for local data processing at the edge of the network. This allows for reduced link delays between mobile users and access points. Here, fog nodes are collocated with radio remote heads (RRHs) to provide local processing capabilities, that is, creating fog radio access network (F‐RAN) that can support ultralow latency for cellular networks that operate on millimeter wave (mmWave) bands. Here, mobile stations (MS) demand various network functions (NFs) that correspond to different service requests. Hence, it is critical to study function popularity and allow content caching at the F‐RAN. Given the limited resources at the fog nodes, it is important to efficiently manage the resources to improve network operations and enhance the capacity at reduced delays and cost. Therefore, caching in mmWave F‐RAN requires node allocation that can accommodate the highest number of cached functions. Hence, this paper proposes a novel node placement scheme that leverages Nelder–Mead meta‐heuristic. Results show that the proposed scheme yields in reduced delay, cost, and power and energy consumption.

An NDN Cache-Optimization Strategy Based on Dynamic Popularity and Replacement Value

Aiming at examining the problems of the low cache hit ratio and high-average routing hops in named data networking (NDN), this paper proposes a cache-optimization strategy based on dynamic popularity and replacement value. When the requested content arrives at the routing node, the latest popularity is calculated based on the number of requests in the current cycle and the popularity of the previous cycle. We adjust the node cache threshold according to the occupation of the node cache space and cache the content with a higher popularity than the threshold. When the cache is complete, the cache-optimization strategy considers the last request time, popularity, and transmission cost of cached content to calculate the replacement value of cached content. We move the content with the lowest replacement value out of the cache, and keep the content with a high replacement value. We deploy the proposed cache-optimization strategy by using a programmable language in a real network with programmable devices. The experimental results illustrate that the strategy proposed in this paper can effectively improve the cache hit ratio and reduce the average routing hops for user request responses compared with other traditional NDN caching strategies.

Edge Caching Enhancement for Industrial Internet: A Recommendation-Aided Approach

Edge caching enables low-delay and high-quality data services for the Industrial Internet. However, traditional popularity-based edge caching ignores the diversity and evolution of user interest, especially among user groups, and therefore has the limited quality of experience guarantees for users. In this regard, a recommendation-aided edge caching approach is proposed to leverage the time-varying user interest. Specifically, a dynamic interest capture model was proposed to mine the individual user interest, based on which, a group interest aggregation algorithm is then studied to determine the content caching strategies for edge nodes. Thereafter, an edge content recommendation is further proposed to optimize the cache hit ratio while ensuring a satisfying recommendation hit ratio based on the personalized user interest and given caching decision. The effectiveness of the proposed approach is finally validated by comparing it with other baseline approaches.

Multiagent Cooperative Caching Policy in Industrial Internet of Things

To handle the impact of the explosive growth of data traffic generated by smart industrial applications in the Industrial Internet of Things (IIoT) scenario, edge caching is commonly used in the IIoT to reduce the content access delay and to release the backhaul load. However, due to the mobility of IIoT devices and temporal dependence of popularity, traditional content caching policies, such as least frequently used (LFU) and least recently used (LRU), cannot cache the required contents accurately within the coverage of edge server. Therefore, by exploiting the moving trajectory of the IIoT devices and the temporal dependence of the content popularity, we propose a multiagent cooperative caching policy, in which each edge server acts as an agent to cooperatively learn the optimal caching decision, then each edge server caches the corresponding contents to reduce the content access delay. Especially, we first apply the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -order Markov chain to predict the moving trajectory of the IIoT devices to get the IIoT device set within the coverage of each edge server. Second, we use long short-term memory (LSTM) to get the prior knowledge of the content requests by using the moving trajectory prediction results. Finally, we obtain the optimal caching decision by deep reinforcement learning to improve the Quality of Service (QoS) of the IIoT applications. Experimental results demonstrate that the proposed caching policy can efficiently improve the cache-hit ratio and content access delay.

Research on Discrete Artificial Bee Colony Cache Strategy of UAV Edge Network

Unmanned aerial vehicle edge networks (UENs) can reduce the cache load of the core network and improve system performance to provide users with efficient content services. However, the time-varying characteristics of content popularity in UENs lead to a low accuracy of popularity prediction, and the capacity limitations of wireless channel conditions lead to a lower cache hit rate than the rates of traditional fiber-optic-based cache strategies. Therefore, this paper proposes the discrete artificial bee colony cache strategy of UENs (DABCCSU). First, the information–dynamics–dissemination model of UENs (IDDMU) is established to deduce the coupling relationship between the channel capacity and the service probability in IDDMU. The influence of the service probability change on the content dissemination process is discussed, and the content popularity in UENs is predicted by the state iteration matrix. Then, the discrete artificial bee colony cache (DABCC) optimization algorithm is proposed. The action function of the artificial bee colony is designed as a random action based on the historical cache strategy. The discrete cache strategy is used as an optimization variable, and the popularity prediction result obtained by IDDMU is used to maximize the cache hit rate. DABCC provides the optimal cache strategy for the UENs, and effectively improves the cache hit rate. The simulation result shows that the accuracy of DABCCSU in content popularity prediction is more than 90%, which achieves a good prediction effect. In terms of cache performance, the average cache hit rate of DABCCSU is 91.62%, which is better than the 51.09% of the Least Recently Used (LRU) strategy, 89.27% of the Greedy Algorithm (GA) and 54.26% of Binary Particle Swarm Optimization (BPSO). In addition, the cache hit rate of DABCCSU under different cache capacities is better than that of LRU, GA, and BPSO, showing a relatively stable performance. It shows that DABCCSU can achieve excellent content popularity prediction, and it can also maximize the cache hit rate under limited communication resources and cache resources to provide UENs with the optimal content cache strategy, and provides users with high-quality content services.

MEC-Based Cooperative Multimedia Caching Mechanism for the Internet of Vehicles

Multimedia applications are expected to widely deploy over vehicular networks. In order to meet the low-latency and high-speed transmission requirements of multimedia applications, edge caching is introduced to reduce the network traffic and the transmission delay. Due to the limited storage of the edge cache server, an efficient approach for the content management plays a decisive role for the edge cache performance. This paper proposes a vehicle-to-infrastructure-based cooperative caching mechanism for Internet of Vehicles to improve the edge cache utilization. The system model is established with the goal of maximizing the cooperative caching hit rate. To jointly consider the collaborations between macrobase stations (MBS) and multiple roadside units (RSU), we propose a reinforcement learning algorithm to adaptively control the cache management. According to the content popularity and the network status, the proposed algorithm can dynamically adjust cached content across relevant MBSs and RSUs. The simulation results show that the proposed cooperative caching mechanism significantly improve the cache utilization and the quality of services.

Hybrid deep learning model based on Intelligent Microbat Routing (IMR) and Popularity Content Caching (PCC) for an effective caching and routing in vehicular edge networks

Vehicular networks are considered as one of the paramount components of intelligent transportation systems. With the help of new advanced technologies like Internet of things (IoT), and many wireless applications such as 5 G/6 G, these vehicular networks has changed its face of applications in terms of computations, communications and privacy. The arising vehicular applications such as crash awareness and emergency brake requires better communication, storage resources, and computation with severe execution prerequisites on response time and network bandwidth. But still, efficient data computation with an intelligent routing mechanism still remains to be darker side of the research. To achieve the improvisation in above parameters, this paper proposes the hybrid combination of the Intelligent Microbat Routing (IMR) and Deep learning based Popularity Content Caching (PCC) techniques which has been targeted for IoT based vehicular networks. This paper introduces the Reinforced Gated Recurrent Units (R-GRU) with High Speed Extreme Learning Machines for better prediction of contents’ popularity that aids for better caching. The proposed IMR-PCC architecture consists of four different tasks such as popularity prediction, cache placement, data retrieval and effective routing. The extensive experimentation is carried out to evaluate the IMR-PCC by implementing the dynamic network topology with multi factors, which directly impacts on the performance of the complete system. Nearly 300,000 contents were collected randomly and the complete algorithm was implemented in the OMNET++ interfaced with Python 3.8. Finally from the experimental results, the proposed IMR-PCC architecture proved its excellence in terms of accuracy (98.6%), precision (98.6%), recall (98.6%), F1-Score (98.8%), cache hit ratios (0.25), latency (0.3), Packet Delivery Ratio (PDR) (99%) than other existing methodologies such as Q-learning, BLSTME, MPCF, ICN. So the proposed IMR-PCC achieved the better Quality of Service (QoS) as well as executes cache performance which is highly suitable for the vehicular IoT networks.

Cooperative Edge Caching Based on Temporal Convolutional Networks

With the rapid growth of networked multimedia services in the Internet, wireless network traffic has increased dramatically. However, the current mainstream content caching schemes do not take into account the cooperation of different edge servers, resulting in deteriorated system performance. In this paper, we propose a learning-based edge caching scheme to enable mutual cooperation among different edge servers with limited caching resources, thus effectively reducing the content delivery latency. Specifically, we formulate the cooperative content caching problem as an optimization problem, which is proven to be NP-hard. To solve this problem, we design a new learning-based cooperative caching strategy (LECS) that encompasses three key components. Firstly, a temporal convolutional network driven content popularity prediction model is developed to estimate the content popularity with high accuracy. Secondly, with the predicted content popularity, the concept of content caching value (CCV) is introduced to weigh the value of a content cached on a given edge server. Thirdly, an novel dynamic programming algorithm is developed to maximize the overall CCV. Extensive simulation results have demonstrated the superiority of our approach. Compared with the state-of-the-art caching schemes, LECS can improve the cache hit rate by 8.3%-10.1%, and reduce the average content delivery delay by 9.1%-15.1%.

QAVA: QoE-Aware Adaptive Video Bitrate Aggregation for HTTP Live Streaming Based on Smart Edge Computing

Currently video streaming in heterogeneous network environments is affected by limited network bandwidth availability and consequent low and variable user Quality of Experience (QoE) levels. In particular, for the case of live video streaming, a very high number of end-clients request content at the same time, generating huge concurrent traffic, and putting pressure on the existing network infrastructure. An approach which helps address this issue is deployment of emerging edge computing technologies to smooth the live streaming traffic and improve QoE by adapting client bitrates and caching content at the edge server. In this context, this paper proposes a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</b> oE-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> daptive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> ideo bitrate <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ggregation scheme for HTTP live streaming based on smart edge computing (QAVA). As an intelligent proxy server, a “smart edge” which deploys QAVA aggregates all the traffic requested by clients for the same live streaming service and adapts their bitrates based on network conditions, client states and video characteristics. The adaptation is performed based on a Deep Reinforcement Learning (DRL)-based algorithm, which is also proposed. The QAVA DRL algorithm is trained and modeled based on a real client experience dataset. The experimental evaluation results presented in this paper show how QAVA outperforms other state-of-the-art adaptive bitrate algorithms in terms of average QoE and QoE fairness.

Energy-Efficient Proactive Content Caching For On-Demand Video Streaming Under Uncertainty

Proactive content caching using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge service providers</i> (ESPs) present in wireless heterogeneous networks is an emerging research topic to deal with stringent client requirements. The limited computing resources of an ESP (e.g., small-cell base stations) can be utilized for transcoding the high resolution contents and thereby supporting the bit rate requirements of the clients. Unlike existing works, this paper deals with the case where the storage and computing capacities of the ESPs are random and time varying (e.g., due to internal workloads). This aspect is important because a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">content provider</i> (CP) cannot be aware of the exact resource availability at an ESP when determining proactive content placement. Different from existing works, we also ensure that the bit rate requirements of the clients are maintained throughout their video streaming sessions. Further, uncertainties in the demand (i.e., number of content requests) and link capacities are also considered. Given these unique challenges, we propose a two-stage stochastic problem which jointly determines the content placement decisions and demand allocation decisions for the CP under various uncertainties. The performance of the proposed model is rigorously analyzed in terms of energy consumption and percentage demand allocation, and compared to some benchmark models through extensive simulations.

A Privacy-Protected Popularity Prediction Scheme for Content Caching Based on Federated Learning

With the explosive growth of mobile data in wireless communication, popular contents need to be brought closer to the edge for rapid data access. Recent studies have proposed methods of content caching based on machine learning but neglected the leakage of private data. To solve this problem, we propose a Private Federated Learning-based Caching (PFLC) scheme. Benefiting from federated learning framework and the pseudo rating matrix (PRM), the scheme can collect the statistical characteristics of a user group to make caching decisions by predicting the popularity of contents without revealing the privacy of users, which means that it can protect the privacy of individual users from being accessed by the server and other users. The performance evaluation demonstrates that the proposed scheme effectively protects individual users’ privacy without sacrificing the cache hit ratio.

Joint MDS Codes and Weighted Graph-Based Coded Caching in Fog Radio Access Networks

In this paper, we investigate maximum-distance separable (MDS) codes and weighted graph based coded caching in fog radio access networks (F-RANs). In the placement phase, the redundant MDS based coded placement scheme is used to provide redundant coded packets and homogeneous cached contents. The redundant coded packets can be used to construct multicast opportunities for similar requests. In the delivery phase, the weighted graph based coded delivery scheme is conducted based on homogeneous cached contents, which can induce considerable multicast opportunities. By integrating the above two schemes, a joint MDS codes and weighted graph based coded caching policy is proposed to minimize the fronthaul load. Finally, we theoretically analyze the performance of the proposed policy by deriving the lower and upper bounds of the fronthaul load. Simulation results show that our proposed policy can provide 44% savings in the fronthaul load compared to the MDS-based uncoded delivery policy.

On the Fundamental Limits of Device-to-Device Private Caching Under Uncoded Cache Placement and User Collusion

In the coded caching problem, as originally formulated by Maddah-Ali and Niesen, a server communicates via a noiseless shared broadcast link to multiple users that have local storage capability. In order for a user to decode its demanded file from the coded multicast transmission, the demands of all the users must be globally known, which may violate the privacy of the users. To overcome this privacy problem, Wan and Caire recently proposed several schemes that attain coded multicasting gain while simultaneously guarantee information theoretic privacy of the users’ demands. In Device-to-Device (D2D) networks, the demand privacy problem is further exacerbated by the fact that each user is also a transmitter, which appears to be needing the knowledge of the files demanded by the remaining users in order to form its coded multicast transmission. This paper shows how to solve this seemingly infeasible problem. The main contribution of this paper is the development of new achievable and converse bounds for D2D coded caching that are to within a constant factor of one another when privacy of the users’ demands must be guaranteed even in the presence of colluding users (i.e., when some users share cached contents and demanded file indices). First, a D2D private caching scheme is proposed, whose key feature is the addition of virtual users in the system in order to “hide” the demands of the real users. By comparing the achievable D2D private load with an existing converse bound for the shared-link model without demand privacy constraint, the proposed scheme is shown to be order optimal, except for the very low memory size regime with more files than users. Second, in order to shed light into the open parameter regime, a new achievable scheme and a new converse bound under the constraint of uncoded cache placement (i.e., when each user stores directly a subset of the bits of the library) are developed for the case of two users, and shown to be to within a constant factor of one another for all system parameters. Finally, the two-user converse bound is extended to any number of users by a cut-set type argument. With this new converse bound, the virtual users scheme is shown to be order optimal in all parameter regimes under the constraint of uncoded cache placement and user collusion.

An Information-Centric Network Caching Method Based on Popularity Rating and Topology Weighting

Ubiquitous caching is a feature shared by all proposed information-centric network (ICN) architectures. Prioritising storage resources to popular content in the network is a proven way to guarantee hit rates, reduce the number of hops forwarded, and reduce user request latency. An ideal ICN caching mechanism should make the best use of relevant information such as content information, network state, and user requirements to achieve optimal selection and have the ability to adaptively adjust the decision cache content for dynamic scenarios. Since router nodes have limited cache space, it is then useless to accurately predict the popularity of the content with very low popularity, as this content has no chance of being cached. A more effective approach is to focus on content with high popularity that influences caching decisions. As for different nodes, they have different sets of popular content, and using this property, this paper designs a caching method based on the popularity hierarchy with topological weights. The method considers managing the cached content in nodes with a hierarchy of popularity and improving their distribution in terms of the importance of the nodes’ position in the network. Finally, the scheme is simulated by changing the parameter settings under different actual topologies on the simulation platform to confirm the feasibility of the scheme.

Popularity based content caching of YouTube data in cellular networks

Popularity based content caching of YouTube data in cellular networks

Energy- and Quality-Aware Task Offloading for WebVR Service in Terminal-Aided Mobile Edge Network

Web virtual reality (WebVR) is gaining increasing attention as interactive VR experiences become more prevalent. The high energy consumption and strict quality requirements are still significant challenges for its application in the mobile edge network. In this context, content caching and task offloading are promising solutions to save the energy of WebVR. This paper proposes a hybrid decentralized offloading architecture in which the MEC server and multiple terminals jointly participate in the caching and processing of WebVR tasks. In this framework, the graph convolutional network and unsupervised clustering algorithm are jointly applied to process the WebVR service feature graph to achieve FoV-level content caching. Moreover, WebVR users with idle computing resources assist neighboring users with performing tasks. On this basis, we propose a distributed low-bound-based alternative direction method of multiplier (LADMM) algorithm to optimize the offloading mode and the allocation of tasks and computing power resources to minimize system energy consumption. The proposed offloading mode can lower energy consumption while maintaining a good balance between delay performance and resource utilization.

Efficient Cache Consistency Management for Transient IoT Data in Content-Centric Networking

Since Internet of Things (IoT) communications can enjoy many advantages brought by content-centric networking (CCN) in nature, there is an increasing interest on their integration for better information retrieval and distribution. Nevertheless, different from the conventional multimedia traffic of which contents are hardly changed, IoT data are always transient and updated by their producers according to the actual situation. As a result, if without any effective countermeasures, outdated copies are inevitably stored by CCN routers and then distributed to the associated consumers, degrading both caching efficiency and user experience. In fact, most of related policies take little account of information freshness for cached contents, and how to tackle transient IoT data in CCN is still an ignored but crucial issue required for further explorations. Therefore, in this article, we propose an efficient popularity-based cache consistency management scheme, which aims to guarantee freshness of IoT data returned by on-path routers and avoid heavy signalling costs introduced at the same time. Extensive simulations were performed under both real-world scare-free and binary-tree topologies, and corresponding results have proved the efficiency of the proposed scheme in timely evictions of outdated IoT data stored by CCN in-network caching.