Caching Scheme Research Articles

A study on the impact of mobility on caching in non-standalone 5G vehicular networks

Responding to numerous requests of mobile users and maintaining the quality of service is one of the important challenges in designing and implementing future vehicular networks. One of the solutions to reduce network traffic is caching on the radio access network and close to users. In this paper, we investigate the impact of the mobility of users on caching performance and propose novel mobility-aware schemes to turn the mobility challenge into an opportunity to reduce latency. We study vehicular networks with Non-Standalone (NSA) 5G deployment in which a mobile user (MU) can access the 5G Next Generation NodeB (gNB) stations with caching capability in addition to accessing the 4G Evolved NodeB (eNB) stations. We provide an analytical framework and present a closed-form expression of the average end-to-end data transfer time in cache-enabled vehicular networks with NSA 5G deployment, considering users' mobility and the stochastic arrival of user requests. We study various caching schemes and propose novel mobility-aware caching schemes. Using the map-based mobility patterns of various vehicles including cars and buses and also pedestrians, it is shown that the proposed mobility-aware schemes improve the performance of mobile networks in terms of latency.

Collaborative Cache-Aided Relaying Networks: Performance Evaluation and System Optimization

This paper studies a multi-tier cache-aided relaying network, where the destination <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> is randomly located in the network and it requests files from the source <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> through the help of cache-aided base station (BS) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> relays. In this system, the multi-tier architecture imposes a significant impact on the system collaborative caching and file delivery, which brings a big challenge to the system performance evaluation and optimization. To address this problem, we first evaluate the system performance by deriving analytical outage probability expression, through fully taking into account the random location of the destination and different file delivery modes related to the file caching status. We then perform the asymptotic analysis on the system outage probability when the signal-to-noise ratio (SNR) is high, to enclose some important and meaningful insights on the network. We further optimize the caching strategies among the relays and BS, to improve the network outage probability. Simulations are performed to show the effectiveness of the derived analytical and asymptotic outage probability for the proposed caching strategy. In particular, the proposed caching is superior to the conventional caching strategies such as the most popular content (MPC) and equal probability caching (EPC) strategies.

Learning-Based Hierarchical Edge Caching for Cloud-Aided Heterogeneous Networks

Edge caching has emerged as a promising technique against latency caused by explosive growth of mobile data traffic through caching popular contents at the edge networks. However, the dynamically changing content popularity nature and limited caching capacity make it challenging to design an effective caching scheme to reduce latency. To solve this, a learning-based hierarchical edge caching (LHEC) scheme is proposed in this work. We first propose a novel deep learning architecture, namely Stacked Autoencoder-Long Short Term Memory Network (SAE-LSTMNet) to capture both the correlation of the request patterns among different content and the periodicity in time domain to improve the prediction accuracy of the content popularity. Then, to predict the popularity of these newly-added contents, a dynamic content catalog is introduced and a similarity-based content popularity prediction (SCPP) approach is proposed. Based on the content popularity prediction, a hierarchical edge caching optimization problem is formulated to minimize the average content downloading latency. Since the formulated problem is NP-hard and difficult to be solved, a low-complexity algorithm is proposed to obtain the near-optimal solutions. Simulation results show that the proposed content popularity prediction approach outperforms up to 6.36% in terms of the mean absolute error compared with the state-of-the-art methods and the proposed LHEC scheme reduces the average downloading latency at about 5.3%~7.9% compared with those existing caching schemes.

Deep Reinforcement Learning for Reactive Content Caching With Predicted Content Popularity in Three-Tier Wireless Networks

With the explosive growth of micro-video applications, the mobile traffic generated by retrieving a few user-generated micro-videos has brought a massive burden to backhaul links and backbone networks. Unlike other types of videos, user-generated micro-videos are typically requested by a large number of users within an extremely short period after their release. Therefore, it is crucial to predict the content popularity and make caching decision for the newly requested content timely. To predict content popularity in different locations, the request probabilities of the content in different locations are translated into rating scores. Then, a recommendation system-based prediction model is designed to predict rating scores of the newly requested content in different locations. To increase caching diversity and realize vertically collaboration in a three-tier wireless network, a deep reinforcement learning-based reactive content caching strategy is proposed to make caching decision for the newly requested content. The main goal is to obtain a higher caching gain at a lower caching space cost. To evaluate the caching performance, a new metric, cache benefit rate, is defined as the download latency reduction brought by each bit of cache. Compared with matrix factorization, the performance of prediction model can be increased by 23.98% and 12.44% in terms of mean absolute error and root mean square error, respectively. Extensive simulations demonstrate that the proposed reactive caching strategy outperforms other caching strategies under different system parameters in terms of the cache hit rate, average download time, and cache benefit rate.

Accelerating Protocol Oblivious Forwarding Programmable Data Plane With Flow Cache

The advances in software-defined networking (SDN) have recently promoted the development of Programmable Data Planes (PDP). PDP is famous in various network environments that need to support new protocols due to its flexibility and protocol independence. Protocol-oblivious forwarding (POF) is an implementation of PDP that uses <offset, length> to describe the match fields and mixes the protocol parsing into the pipeline, so there is no need for a pre-defined packet header parser. Current cache schemes for the data plane, such as Microflow Cache/Megaflow Cache, are primarily implemented based on the pre-defined packet header parser. In this paper, we first analyzed the two difficulties of generating cache tables on POF PDP. Then we analyzed the advantages and disadvantages of the single flow table cache system and multiple flow tables cache system from the perspective of time overhead. Finally, we propose a pipeline-oriented cache system to accelerate POF PDP. Aiming at the pipeline with multiple branches and multiple matching paths, we design algorithms to obtain all cache tables. Then our design leveraged the incremental calculation feature of the hash function to reduce the time overhead of cache lookup. In addition, the sorting mechanism and sleep mechanism for multiple cache tables were proposed according to the locality of traffic. Our design is implemented in a real POF PDP environment. The experimental results show that our pipeline-oriented cache scheme can effectively accelerate the programmable data plane, reducing packet forwarding latency and improving the throughput.

An Unequal Caching Strategy for Shared-Memory Graph Analytics

Recent advances in computer architecture significantly enhance the computational capacity of multicore systems. It allows large-scale graphs to be processed inside a single machine. Nevertheless, the irregular processing pattern of graph-structured data constrains the hardware resources from being productively utilized. In this paper, we investigate the constraints in two aspects: workload imbalance and parallel inefficiency. When a graph analytics algorithm is multithreaded, the thread time is highly diversified, indicating an uneven work distribution. Also, the intensive thread contention lowers the computing capacity of CPU cores, thereby hindering the effective utilization of CPU resources. To address these challenges, we present a proactive graph caching strategy that unequally segments graph components into cache-able subsets of varying sizes, namely Syze. First, the computational loads of cache-sized subgraphs are estimated. Then, the demanding subgraphs are further subdivided until certain threshold is met. Moreover, during the propagation of updates, a fraction of vertex ID (i.e., several bits) are encoded to facilitate the communication between subgraphs. As a result, Syze is able to balance the workloads amongst the logical cores by shortening the longest thread execution time. Meanwhile, it alleviates thread contention and thus elevates the parallel efficiency of multicores. Compared with well-optimized Ligra, Gemini and GPOP, Syze achieves accelerations by up to <inline-formula><tex-math notation="LaTeX">$17.76\times$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$11.67\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$2.81\times$</tex-math></inline-formula> respectively. Additionally, the side effects of Syze are evaluated, including raised cache misses and memory accesses. They play a trivial role in deciding the overall performance, as their costs are far outweighed by the gains from the even distribution of workloads and the improved utilization of multicores. <!--?query id="Q2"--> Author: Please confirm or add details for any funding or financial support for the research of this article. ?>

Analysis and optimization on partition-based caching and delivery in satellite-terrestrial edge computing networks

As a viable component of 6G wireless communication architecture, satellite-terrestrial networks support efficient file delivery by leveraging the innate broadcast ability of satellite and the enhanced powerful file transmission approaches of multi-tier terrestrial networks. In the paper, we introduce edge computing technology into the satellite-terrestrial network and propose a partition-based cache and delivery strategy to make full use of the integrated resources and reducing the backhaul load. Focusing on the interference effect from varied nodes in different geographical distances, we derive the file successful transmission probability of the typical user and by utilizing the tool of stochastic geometry. Considering the constraint of nodes cache space and file sets parameters, we propose a near-optimal partition-based cache and delivery strategy by optimizing the asymptotic successful transmission probability of the typical user. The complex nonlinear programming problem is settled by jointly utilizing standard particle-based swarm optimization (PSO) method and greedy based multiple knapsack choice problem (MKCP) optimization method. Numerical results show that compared with the terrestrial only cache strategy, Ground Popular Strategy, Satellite Popular Strategy, and Independent and identically distributed popularity strategy, the performance of the proposed scheme improve by 30.5%, 9.3%, 12.5% and 13.7%.

Feedback Delay-Tolerant Proactive Caching Scheme Based on Federated Learning at the Wireless Edge

Edge caching has emerged as a promising approach to meet explosive mobile data on 6G networks. One critical issue in edge caching is file popularity prediction. The federated learning (FL) based distributed algorithm is used to predict file popularity, which solves user privacy issues in centralized algorithms. However, due to the heterogeneity of user devices, the feedback time (i.e., the update and upload time of model parameters) is various for each user, which causes the feedback delay in the framework of FL-based edge caching. In this letter, we devise a feedback delay-tolerant proactive caching scheme (FLASH) based on FL in which each user utilizes hybrid filtering on stacked autoencoders to train the prediction model locally. Experimental results indicate that FLASH can effectively handle feedback delay and outperform counterpart algorithms in cache efficiency.

A Federated Learning-Based Edge Caching Approach for Mobile Edge Computing-Enabled Intelligent Connected Vehicles

Massive map data transmission and the strict demand for the privacy of high-precision maps have brought significant challenges to the cache of high-precision maps in intelligent connected vehicles (ICV). Federal learning (FL) was introduced to reduce the pressure on the edge network and protect privacy. But the high dynamics of cars and limited resources lead to low accuracy and high training delay. We propose a joint optimization scheme of participant selection and resource allocation for federated learning. In each time slice, vehicles are determined whether to participate in training, which minimizes long-term training delay with limited energy consumption. To meet the delay and privacy requirements of high-precision map caching, we present an edge cooperative caching scheme based on federated deep reinforcement learning (F-DRL), which aims to achieve dynamic adaptive edge caching while protecting user privacy. The collaborative caching model is formulated as a Markov decision process (MDP). Dueling Deep Q Network (Dueling-DQN) is used to solve the optimal strategy, and federal learning is used for training. Enough comparative experiments to evaluate the performance of the proposed schemes. The aspects of reliability, cache hit rate, and training accuracy prove that the method effectively improves the training parameters of federated learning while meeting a high-precision map cache’s delay and reliability requirements.

A Novel Nested Coded Delivery Strategy for Coded Caching Under Nonuniform File Popularity

This letter proposes an efficient nested coded caching scheme (NCCS) under nonuniform file popularity to reduce the average delivery rate. The NCCS uses a nested coded delivery strategy to ensure the subfiles of the files being coded into the same message are of the same size, thereby eliminating zero-padding that is commonly used in coded messages for delivery and, at the same time, maximally exploiting multicasting gain. We further optimize the NCCS by jointly optimizing the cache placement and nested coded delivery strategy to minimize the average delivery rate. Numerical results show that the optimized NCCS outperforms other state-of-the-art coded caching schemes and achieves near-optimal performance.

A GNN-based proactive caching strategy in NDN networks

As people spend more time watching and sharing videos online, it is critical to provide users with a satisfactory quality of experience (QoE). Leveraging the in-network caching and named-based routing features in Named Data Networks (NDNs), our paper aims to improve user experience through caching. We propose a graph neural network-gain maximization (GNN-GM) cache placement algorithm. First, we use a GNN model to predict users’ ratings of unviewed videos. Second, we consider the total predicted rating of a video as the gain of the cached video. Third, we propose a cache placement algorithm to maximize the caching gain and actively cache videos. Cache replacement is implemented based on the cache gain ranking of videos, with higher cache gain videos replacing lower cache gain videos. We compare GNN-GM with two state-of-the-art caching strategies, namely the NMF-based caching strategy and GNN-CPP. GNN-GM is also compared with two traditional caching strategies, LCE and LRU, LCE and FIFO. We evaluate the five caching strategies using real-world datasets in a tree network topology, a real-world network topology GEANT, and various random topologies. The experimental results show that our caching policy significantly improves cache hit ratio, latency and server load. Notably, GNN-GM achieves a 25% higher cache hit rate, 5% lower latency and 7% lower server load than GNN-CPP in GEANT.

Files Cooperative Caching Strategy Based on Physical Layer Security for Air-to-Ground Integrated IoV

Mobile edge cache (MEC)-enabled air-to-ground integrated Internet of Vehicles (IoV) technology can solve wireless network backhaul congestion and high latency, but security problems such as eavesdropping are often ignored when designing cache strategies. In this paper, we propose a joint design of cache strategy and physical layer transmission to improve the security offloading ratio of MEC-enabled air-to-ground IoV. By using the random geometry theory and Laplace transform, we derive the closed-form expression of the network security offloading ratio, which is defined as the probability that the request vehicle (RV) successfully finds the required file around it and obtains the file with a data rate larger than a given threshold. During the file acquisition process, we collectively consider the impact of the successful connection and secure transmission in the vehicle wireless communication. Then, we establish an optimization problem for maximizing the network security offloading ratio, in which the cache strategy and the secure transmission rate are jointly optimized. Furthermore, we propose an alternating optimization algorithm to solve the joint optimization problem. Simulation experiments verify the correctness of our theoretical derivation, and prove that the proposed cache strategy is superior to other existing cache strategies.

Low-power secure caching strategy for Internet of vehicles

Low-power secure caching strategy for Internet of vehicles

Caching popularity-unknown contents for massive communication channels via deep Q-learning

In order to reduce the backhaul link pressure of wireless networks, edge caching technology has been regarded as a promising solution. However, with massive and dynamical communication connections, it is challenging to provide analytical caching solution to achieve the best performance, particularly when the requested contents are changing and their popularities are unknown. In this paper, we propose a deep Q-learning (DQN) method to address the issue of caching placement. Considering a content caching network containing multiple cooperating SBSs with unknown content popularity, we need to determine which content to cache and where to cache. Therefore, the learning network has to be designed for dual aims, one of which is to estimate the content popularities while the other is to assign contents to the proper channels. An elaborate DQN is proposed to make decisions to cache contents with limited storage space of base-station by considering channel conditions. Specifically, the content requests of users are first collected as one of the training samples of the learning network. Second, the channel state information for the massive links are estimated as the other training samples. Then, we train the network based on the proposed method thereby improving spectral efficiency of the entire system and reducing bit-error rate. Our major contribution is to contrive a caching strategy for enhanced performance in massive connection communications without knowing the content popularity. Numerical studies are performed to show that the proposed method acquires apparent performance gain over random caching in terms of average spectral efficiency and bit-error rate of the network.

Deep Reinforcement Learning for Edge Caching with Mobility Prediction in Vehicular Networks.

As vehicles are connected to the Internet, various services can be provided to users. However, if the requests of vehicle users are concentrated on the remote server, the transmission delay increases, and there is a high possibility that the delay constraint cannot be satisfied. To solve this problem, caching can be performed at a closer proximity to the user which in turn would reduce the latency by distributing requests. The road side unit (RSU) and vehicle can serve as caching nodes by providing storage space closer to users through a mobile edge computing (MEC) server and an on-board unit (OBU), respectively. In this paper, we propose a caching strategy for both RSUs and vehicles with the goal of maximizing the caching node throughput. The vehicles move at a greater speed; thus, if positions of the vehicles are predictable in advance, this helps to determine the location and type of content that has to be cached. By using the temporal and spatial characteristics of vehicles, we adopted a long short-term memory (LSTM) to predict the locations of the vehicles. To respond to time-varying content popularity, a deep deterministic policy gradient (DDPG) was used to determine the size of each piece of content to be stored in the caching nodes. Experiments in various environments have proven that the proposed algorithm performs better when compared to other caching methods in terms of the throughput of caching nodes, delay constraint satisfaction, and update cost.

Performance comparison of cache replacement algorithms onvarious internet traffic

Internet users tend to skip and look for alternative websites if they have slow response times. For cloud network managers, implementing a caching strategy on the edge network can help lighten the workload of databases and application servers. The caching strategy is carried out by storing frequently accessed data objects in cache memory. Through this strategy, the speed of access to the same data becomes faster. Cache replacement is the main mechanism of the caching strategy. There are seven cache replacement algorithms with good performance that can be used, namely LRU, LFU, LFUDA, GDS, GDSF, SIZE, and FIFO. The algorithm is developed uniquely according to the internet traffic patterns encountered. Therefore, a particular cache replacement algorithm cannot be superior to other algorithms. This paper presents a performance comparison simulation of the seven cache replacement algorithms on various internet traffic extracted from the public IRcache dataset. The results of this study indicate that the hit ratio performance is strongly influenced by cache size, cacheable and unique requests. The smaller the unique request that occurs, the greater the hit ratio performance obtained. The LRU algorithm shows an excellent hit ratio performance to perform cache replacement work under normal internet conditions. However, when the access impulse phenomenon occurs, the GDSF algorithm is superior in obtaining hit ratios with limited cache memory capacity. The simulation results show that GDSF reaches a 50.75% hit ratio while LRU is only 49.17% when access anomalies occur.

Efficient Caching in Vehicular Edge Computing Based on Edge-Cloud Collaboration

In vehicular edge computing (VEC), the execution of offloading task needs not only the task data uploaded by the requesting vehicle, but also the additional data to support the task to be executed successfully, and how to efficiently cache and access these supporting data becomes the key issue for task offloading in VEC. In this paper, we study the efficient caching mechanism to minimize the acquisition delay of the supporting data. Firstly, with the software defined network (SDN) based VEC framework, we analyze the acquisition ways of the supporting data and the caching collaboration between VEC servers. Then, according to the density of the requesting vehicles, we divide the VEC coverage into dense and ordinary areas. With the consideration of the similarity of the requested data and the distance between edge servers, the edge servers are clustered into multiple groups based on K-mean++ algorithm. Finally, each server's storage space is divided into three partitions, and the most beneficial data for itself, its group and the whole system are respectively stored in these partitions. Based on service area dividing, server grouping and storage space partitioning, we propose an efficient edge-cloud collaborative caching strategy, which can reduce the delay of data migration while task execution. Simulation results show that, compared with other schemes, the proposed caching strategy has better performance in terms of average data migration delay and application QoS.

Hierarchical cache configuration based on hybrid SOT- and STT-MRAM

With the rapid growth of big data information and the continuous iteration progress of CPU architecture, the implementation of a new memory-based cache architecture is urgent and challenging. In the paper, a CPU cache architecture system based on MRAM is built. Firstly, the performance of SRAM, STT-MRAM and SOT-MRAM as caches from 8 kb to 32 Mb is evaluated. Secondly, by summarizing the performance of SRAM and MRAM in different cache levels, a new quad-core CPU cache architecture design scheme with SOT-MRAM as the first level of cache and STT-MRAM as the second level of cache is determined. Thirdly, the built cache system is simulated. A non-inclusive strategy is proposed to replace the inclusive strategy in order to solve the problem of high dynamic energy of STT-MRAM at the second level. The idea of having a quad-core CPU dynamically share the second-level cache is proposed in the paper. Finally, the caching system in the paper is compared with the other previous works, showing up to 60.78% energy consumption advantage and 33.22% leakage power advantage. The proposed MRAM-based CPU cache system and the corresponding cache strategy have potential application with the benefits of low power and less area.

Lower energy consumption in cache-aided cell-free massive MIMO systems

Cell-free (CF) massive MIMO systems provide uniform and fast service to users by the cooperation of multiple distributed access points (APs) at the expense of high energy consumption (EC) due to the heavy use of fronthaul and backhaul links. To tackle this issue, we propose a cache-aided cell-free (CCF) system where all APs are deployed with edge cache to reduce the load on links. In this paper, a scalable total EC model of CCF systems is established with two parts: EC of APs (ECAP) and EC for cache transmission (ECCT). An improved partial zero-forcing precoding scheme is proposed, which can provide high spectral efficiency and transmit few control data to reduce ECAP greatly. Then a minimum ECCT strategy is formulated. We transform ECCT minimization problem into a series of geometric programming problems by the successive convex approximation and an ingenious equivalent transformation. The obtained optimal cache placement makes the ECCT of CCF systems lower than that obtained by maximum hit ratio, equal probability and most popular caching strategies. Simulation results show that the proposed CCF systems are much more energy efficient than CF systems.

ICache: An Intelligent Caching Scheme for Dynamic Network Environments in ICN-Based IoT Networks

Advanced network technologies and ubiquitous connected devices are boosting the development of the Internet of Things (IoT) at an unprecedented pace. However, as most of the connected IoT devices are battery powered, the energy consumption issue has become the bottleneck of the IoT’s development. Caching is a promising approach to reducing the energy consumption of the battery-powered devices since the requested data packets can be retrieved from intermediate nodes in the network, e.g., routers, instead of from the remote battery-powered IoT devices, which allows the IoT devices to spend more time in the sleep mode. To realize in-network caching and overcome the IP-based networks’ inefficiency support for IoT, building IoT over information-centric networking (ICN) is a promising approach advocated by researchers. However, existing works in this area assume the network environments are static, which hinders the development of existing approaches in the real dynamic network environments. In this article, we leverage the deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -networks (DQNs) to propose an intelligent caching scheme (named as iCache) that can automatically adjust the caching nodes’ caching parameters to make caching decisions for the dynamic network environments. Extensive evaluations were conducted and the results show that the proposed iCache outperforms the existing approaches in terms of the total energy consumption (e.g., more than 29% reduction compared to the caching transient data (CTD) caching scheme) and the average number of hops (e.g., more than 20% reduction compared to the CTD caching scheme).