Caching Scheme Research Articles

LAC: A Workload Intensity-Aware Caching Scheme for High-Performance SSDs

LAC: A Workload Intensity-Aware Caching Scheme for High-Performance SSDs

Construction of Coded Caching Scheme Based on Vector Space over Finite Field

Coded caching technology can better alleviate network traffic congestion. Since many of the centralized coded caching schemes now in use have high subpacketization, which makes scheme implementation more challenging, coded caching schemes with low subpacketization offer a wider range of practical applications. It has been demonstrated that the coded caching scheme can be achieved by creating a combinatorial structure named placement delivery array (PDA). In this work, we employ vector space over a finite field to obtain a class of PDA, calculate its parameters, and consequently achieve a coded caching scheme with low subpacketization. Subsequently, we acquire a new MN scheme and compare it with the new scheme developed in this study. The subpacketization \(F\) of the new scheme has significant advantages. Lastly, the number of users \(K\), cache fraction \(\frac{M}{N}\), and subpacketization \(F\) have advantages to some extent at the expense of partial transmission rate \(R\) when compared to the coded caching scheme in other articles.

Caching Method for Information-Centric Ad Hoc Networks Based on Content Popularity and Node Centrality

In recent years, most internet communications have focused on accessing content such as video, web services, and audio. Conversely, traditional Internet communications are inefficient because they are primarily designed for data transfer between hosts. In response, Information-Centric Networking (ICN) has emerged as a content-oriented networking model. The impact of ICN in reducing the location dependency of data and its high compatibility with ad hoc networks has led to research on realizing Information-Centric ad hoc Networks (ICANET). There has also been extensive research into caching content in the network, which is one of the features of ICN. In static networks, methods have been proposed to cache highly popular content in nodes that are more likely to be used for shortest paths. However, in dynamic networks, content with high popularity should be cached on nodes that are more likely to reach all nodes, as missing nodes need to be taken into account. In this study, we propose a cache control scheme for content caching in ICANET that utilizes both content popularity and the closeness centrality of nodes within the ad hoc network as indicators. To realise the proposed method, a new packet flow based on the Pending Interest Table (PIT) and Content Store (CS) was implemented in the forwarding strategy of ICN. The experiments used ndnSIM, a protocol implementation of NDN based on Network Simulator3, which is widely used in wireless network research. The experimental results showed that the cache hit rate could be increased by up to 4.5% in situations with low content bias. In the same situation, the response delay was also reduced by up to 28.3%.

Epidemic dynamics edge caching strategy for 6G networks

By caching popular content on edge servers closer to users to respond to users’ content requests in 6G networks, the transmission load of backhaul links can be reduced. However, the time-varying characteristics of content prevalence leads to the issue that the cache content may not match the user’s needs, resulting in a decrease in cache success ratio. To solve these issues, we proposed a cache distribution strategy based on epidemic dynamics (CDSED) for 6G edge network. First, a 6G edge caching content model (6G ECCM) is constructed to establish the process of cache content propagation among users as an infectious disease propagation process, analyze the distribution of users’ interest in cache content and obtain the cache content state probability prediction equation, and use the cache content state probability prediction equation to predict the cache content prevalence. Second, based on the predicted prevalence results, a prevalence predictive genetic-annealing cache content algorithm (PGAC) is proposed with the optimization objective of maximizing the cache success ratio. The algorithm designs the selection function of the traditional genetic algorithm as a simulated annealing selection function based on the cache content success ratio, which avoids the defect of the genetic algorithm that converges to the locally optimum cache strategy too early and enhances the cache success ratio. Finally, the optimum cache content decision is solved by iterative alternation. Simulation results demonstrate that CDSED strategy can enhance cache success ratio than the LRU strategy, the LFU strategy, and the MPC strategy.

Collaborative Video Caching in the Edge Network using Deep Reinforcement Learning

With the enormous growth in mobile data traffic over the 5G environment, Adaptive BitRate (ABR) video streaming has become a challenging problem. Recent advances in Mobile Edge Computing (MEC) technology make it feasible to use Base Stations (BSs) intelligently by network caching, popularity-based video streaming, and more. Additional computing resources on the edge node offer an opportunity to reduce network traffic on the backhaul links during peak traffic hours. More recently, it has been found in the literature that collaborative caching strategies between neighbouring BSs (i.e., MEC servers) make it more efficient to reduce backhaul traffic and network congestion and thus improve the viewer experience substantially. In this work, we propose a Reinforcement Learning (RL)–based collaborative caching mechanism in which the edge servers cooperate to serve the requested content from the end-users. Specifically, this research aims to improve the overall cache hit rate at the MEC, where the edge servers are clustered based on their geographic locations. This task is modelled as a multi-objective optimization problem and solved using an RL framework. In addition, a novel cache admission and eviction policy is defined by calculating the priority score of video segments in the clustered MEC mesh network.

Probabilistic Caching Strategy and TinyML-Based Trajectory Planning in UAV-Assisted Cellular IoT System

Probabilistic Caching Strategy and TinyML-Based Trajectory Planning in UAV-Assisted Cellular IoT System

Evolutionary lability of food caching behaviour in mammals.

Food hoarding provides animals access to resources during periods of scarcity. Studies on mammalian caching indicate associations with brain size, seasonality and diet but are biased to a subset of rodents. Whether the behaviour is generalizable at other taxonomic scales and/or is influenced by other ecological factors is less understood. Population density may influence food caching due to food competition or pilferage, but this remains untested in a comparative framework. Using phylogenetic analyses, we assessed the role of morphology (body and brain size), climate, diet breadth and population density on food caching behaviour evolution at multiple taxonomic scales. We also used a long-term dataset on caching behaviour of red squirrels (Tamiasciurus fremonti) to test key factors (climate and population density) on hoarding intensity. Consistent with previous smaller scale studies, we found the mammalian ancestral state for food caching was larderhoarding, and scatterhoarding was derived. Caching strategy was strongly associated with brain size, population density and climate. Mammals with larger brains and hippocampal volumes were more likely to scatterhoard, and species living at higher population densities and in colder climates were more likely to larderhoard. Finer-scale analyses within families, sub-families and tribes indicated that the behaviour is evolutionary labile. Brain size in family Sciuridae and tribe Marmotini was larger in scatterhoarders, but not in other tribes. Scatterhoarding in tribe Marmotini was more likely in species with lower population densities while scatterhoarding in tribe Sciurini was associated with warmer climates. Red squirrel larderhoarding intensity was positively related to population density but not climate, implicating food competition or pilferage as an important mechanism mediating caching behaviour. Our results are consistent with previous smaller-scale studies on food caching and indicate the evolutionary patterns of mammalian food caching are broadly generalizable. Given the lability of caching behaviour as evidenced by the variability of our results at finer phylogenetic scales, comparative analyses must consider taxonomic scale. Applying our results to conservation could prove useful as changes in population density or climate may select for different food caching strategies and thus can inform management of threatened and endangered species and their habitats.

Mobility-aware proactive video caching based on asynchronous federated learning in mobile edge computing systems

With the rapid advance of the Internet of Things (IoT) and the surging user-centric smart devices, video services such as short videos, real-time video streaming, and Virtual Reality (VR) gaming are emerging. Traditional cloud-centric caching is no longer able to satisfy the low-latency needs of mobile user groups. Predicting the most popular content in advance and caching it on edge devices such as small base stations (SBS) is a promising solution to alleviate network congestion and supply better Quality of Service (QoS) for mobile users. However, frequently changing popular content, limited edge caching resources, privacy of user data, and user mobility pose significant challenges to edge video caching. To tackle these challenges, we propose an efficient asynchronous federated learning (AFL)-based mobility-aware proactive video caching scheme (AF-MPVC), which significantly improves the overall network performance while preventing the risk of user data leakage. First, we model the proactive video caching problem in mobile edge computing systems and consider the impact of user mobility on AFL, representing the problem as an autoencoder (AE) model loss minimization problem. Then, a proactive video caching algorithm is proposed based on a filtering model, which uses the hidden features obtained from the trained AE model to compute the user similarity to find the popular videos that are most likely to be accessed by users and combine them with the most frequently requested videos. Meanwhile, we take into account the short duration of high mobility users staying on the current SBS and cache some videos to neighboring edge servers to have more redundant space to store more less popular videos. Finally, experiments on three real-world datasets demonstrate that the presented caching scheme outperforms other baseline caching schemes with respect to cache hit rate and latency reduction.

A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks.

Satellite fog computing (SFC) achieves computation, caching, and other functionalities through collaboration among fog nodes. Satellites can provide real-time and reliable satellite-to-ground fusion services by pre-caching content that users may request in advance. However, due to the high-speed mobility of satellites, the complexity of user-access conditions poses a new challenge in selecting optimal caching locations and improving caching efficiency. Motivated by this, in this paper, we propose a real-time caching scheme based on a Double Deep Q-Network (Double DQN). The overarching objective is to enhance the cache hit rate. The simulation results demonstrate that the algorithm proposed in this paper improves the data hit rate by approximately 13% compared to methods without reinforcement learning assistance.

Low-complexity collaborative caching strategy based on spatio-temporal graph convolutional model

To meet the low delay requirements for data content access in industrial IoT, efficient content caching strategies need to be designed in the Mobile Edge Computing (MEC). Most existing caching policies reduce access delay by predicting content popularity and caching popular content earlier during off-peak traffic, but these strategies only focus on the temporal order of content popularity without fully considering the graph topology of MEC servers and ignore the spatial correlation of content popularity. This paper proposes a low-complexity collaborative caching strategy based on spatio-temporal graph convolutional model (STCC). We integrate graph convolutional neural network and gated recurrent unit to construct spatio-temporal graph convolutional model, which mines the spatio-temporal correlation features of content popularity and make effective predictions on the MEC server graph topology constructed by proximity and semantic relations. Furthermore, we use hierarchical clustering to classify MEC servers into collaborative domains and design a low-complexity heuristic collaborative caching content placement algorithm to minimize the average access delay. Compared with the existing MEC server caching strategy, simulation experiments show that STCC has a better prediction effect of content popularity, and achieves better performance in the two indicators of the cache hit ratio and average access delay.

Retraction Note: Placement delivery array design for the coded caching scheme in medical data sharing

Retraction Note: Placement delivery array design for the coded caching scheme in medical data sharing

BGS: Accelerate GNN training on multiple GPUs

Emerging Graph Neural Networks (GNNs) have made significant progress in processing graph-structured data, yet existing GNN frameworks face scalability issues when training large-scale graph data using multiple GPUs. Frequent feature data transfers between CPUs and GPUs are a major bottleneck, and current caching schemes have not fully considered the characteristics of multi-GPU environments, leading to inefficient feature extraction. To address these challenges, we propose BGS, an auxiliary framework designed to accelerate GNN training from a data perspective in multi-GPU environments. Firstly, we introduce a novel training set partition algorithm, assigning independent training subsets to each GPU to enhance the spatial locality of node access, thus optimizing the efficiency of the feature caching strategy. Secondly, considering that GPUs can communicate at high speeds via NVLink connections, we designed a feature caching placement strategy suitable for multi-GPU environments. This strategy aims to improve the overall hit rate by setting reasonable redundant caches on each GPU. Evaluations on two representative GNN models, GCN and GraphSAGE, show that BGS significantly improves the hit rate of feature caching strategies in multi-GPU environments and substantially reduces the time overhead of data loading, achieving a performance improvement of 1.5 to 6.2 times compared to the baseline.

A High-Performance Accelerator for Real-Time Super-Resolution on Edge FPGAs

In the digital era, the prevalence of low-quality images contrasts with the widespread use of high-definition displays, primarily due to low-resolution cameras and compression technologies. Image super-resolution (SR) techniques, particularly those leveraging deep learning, aim to enhance these images for high-definition presentation. However, real-time execution of deep neural network (DNN)-based SR methods at the edge poses challenges due to their high computational and storage requirements. To address this, field-programmable gate arrays (FPGAs) have emerged as a promising platform, offering flexibility, programmability, and adaptability to evolving models. Previous FPGA-based SR solutions have focused on reducing computational and memory costs through aggressive simplification techniques, often sacrificing the quality of the reconstructed images. This paper introduces a novel SR network specifically designed for edge applications, which maintains reconstruction performance while managing computation costs effectively. Additionally, we propose an architectural design that enables the real-time and end-to-end inference of the proposed SR network on embedded FPGAs. Our key contributions include a tailored SR algorithm optimized for embedded FPGAs, a DSP-enhanced design that achieves a significant four-fold speedup, a novel scalable cache strategy for handling large feature maps, optimization of DSP cascade consumption, and a constraint optimization approach for resource allocation. Experimental results demonstrate that our FPGA-specific accelerator surpasses existing solutions, delivering superior throughput, energy efficiency, and image quality.

An Intelligent Caching Scheme Considering the Spatio-Temporal Characteristics of Data in Internet of Vehicles

An Intelligent Caching Scheme Considering the Spatio-Temporal Characteristics of Data in Internet of Vehicles

Design of Coded Caching Scheme for MAMISO Networks With Improved DoF

Design of Coded Caching Scheme for MAMISO Networks With Improved DoF

Multi-Agent Collaborative Caching Strategies in Dynamic Heterogeneous D2D Networks

Multi-Agent Collaborative Caching Strategies in Dynamic Heterogeneous D2D Networks

Joint cooperative caching and power control for UAV-assisted internet of vehicles

In view of the current problems of spectrum resource scarcity, return congestion, and insufficient energy utilization in the unmanned aerial vehicle (UAV)-assisted Internet of Vehicles (IoV), this paper investigates the cooperative caching and power control, and proposes a joint optimization method to improve the overall Energy Efficiency (EE) . In this method, we first propose a communication establishment threshold to control the V2V communication distance and serve as a joint optimization factor. Then we derive the closed form expressions of offloading ratio and EE of the UAV-assisted IoV, and formulate the optimization problem of maximizing EE. Due to the coupling relationship between caching strategy and transmission power, it is difficult for us to directly solve the optimization problem. Furthermore, we propose an alternating optimization algorithm for solving the optimization problem. Finally, the experimental simulation compare the propose joint optimization method with other existing optimization methods, and the simulation results prove the effectiveness and superiority of the propose joint optimization method.

Distributed cache strategy based on LT codes under spark platform

Distributed cache strategy based on LT codes under spark platform

Online task offloading algorithm based on multi-objective optimization caching strategy

Within the realm of Mobile Edge Computing (MEC), task offloading has consistently garnered significant attention. Within the context of intricate caching environments and multi-user scenarios, conventional solutions frequently encounter difficulties in simultaneously fulfilling the demands for latency reduction and energy consumption optimization. This paper presents a novel online task offloading algorithm that leverages a multi-objective optimization caching strategy. This algorithm addresses two challenges: the Online Task Offloading (OTO) problem and the Online Task File Caching (OTFC) problem. The OTO problem is conceptualized as a multi-user game, where Nash equilibrium is employed to effectively characterize and address it. This ensures the determination of the optimal offloading strategy in the presence of various caching scenarios. Meanwhile, the OTFC problem is transformed into a Markov decision process, and through the utilization of Deep Q-Networks, we can forecast the requirements of online tasks and subsequently determine the optimal caching vector. The incorporation of the Multi-Objective Cache Policy (MOCP) algorithm precedes the finalization of the caching vector. Rooted in multi-objective optimization, this algorithm adeptly balances various caching decisions, achieving a Pareto optimal outcome. The proposed offloading model that effectively caters to the requirements of task offloading while incorporating the demands of task file caching. Moreover, the MOCP algorithm ensures optimal caching decisions across a broad range of scenarios. Simulation tests reveal that this enhanced offloading algorithm, grounded in multi-objective optimization, outperforms traditional methods in energy conservation, boasting energy savings of up to 15%.

Energy Consumption Optimization based on Economic Benefit in WSN-based IoT via Global Hierarchical Caching Strategy

Recent advances in Wireless Sensor Network (WSN) have significantly contributed to the prevalence of Internet of Things (IoT) devices and their pervasive integration into everyday life and industrial operations. A WSN-based IoT comprises numerous small, scattered, battery-operated sensors that are designed to perform collaborative tasks. These sensor nodes are prone to energy drain because of their limited battery capacity when they needed to run efficiently over extended periods of time. Enhancing the large-scale network’s lifespan and controlling the economic costs become relevant since battery replacement or recharging is impractical in severe environments. In this paper, we present a novel energy optimization algorithm tailored for WSNs, which not only takes into account the reduction in replacement costs stemming from prolonged equipment lifespan but also incorporates the operational savings resulting from enhanced energy efficiency. It aims to enhance energy efficiency by leveraging a global hierarchical caching mechanism to simultaneously balance exploration and exploitation of energy resources in both the uplink and downlink of the networks. The simulation results demonstrate that our algorithm effectively minimizes energy consumption while maintaining optimal economic efficiency by decreasing the frequency of state transitions. It can consume 13% less energy than original system and extend the network lifetime by 10%.