Cache Hit Research Articles

Wireless Edge Caching and Content Popularity Prediction Using Machine Learning

The ever pervasive growth in information services and technology has resulted in the outbreak of demand for data in the wireless networks. This has made the network operators to ponder over the imminent difficulties such as computing capabilities and fronthaul-backhaul link capacities. Hence, to bridge the gap between the cloud capacity and the requirement of mobile services by the network edges, edge computing and caching techniques have been gaining more and more attention from researchers across the world. Further, motivated by the successful applications of ML in solving complex and dynamic problems, in this article, it has been used to advance edge caching capabilities. The proposed ML based algorithms have been evaluated and proved to have better performance compared with the existing conventional algorithms. The MSE, for the proposed DL algorithm, is 20-times less than the existing algorithms and while comparing with the simple neural network, the gain in MSE for the proposed DL algorithm is observed around 27%. Similarly for the FL based caching algorithm, average cache hit gain is of the order of 10^4, hence demonstrating the benefit of the proposed algorithms. Additionally, opportunities for a promising upcoming future of ML in edge computing prediction have been discussed.

Distance-based dynamic caching and replacement strategy in NDN-IoT networks

The amount of data on the Internet has risen noticeably due to the exponential increase in the number of Internet of Things (IoT) devices. Recently, Information-Centric Networking (ICN) is acknowledged as a suitable framework for creating IoT network protocols. One notable feature of ICN is in-network caching, which stores the data packets on intermediate routers/nodes that can be accessed by the subscribers when required. This work proposes a novel Distance-based dynamic caching and replacement strategy (DBDCRS) which involves a light weight calculation that utilizes the request aggregation to find the request weight of the requests stored in Pending interest table (PIT) and node weight of a node based on the distance from the subscriber and IoT publisher to accomplish the caching decision. To eliminate the data redundancy, the scheme caches a particular content on a single node along the specific interest-data path. Additionally, a custom cache replacement policy is introduced which retains the essential contents in cache. Simulation results show that our proposed DBDCRS scheme outperform other in-network cache placement schemes in terms of cache hit ratio, server hit reduction ratio, average response latency, and energy consumption.

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%.

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.

Caching Policy in Low Earth Orbit Satellite Mega-Constellation Information-Centric Networking for Internet of Things.

Information-Centric Networking (ICN) is the emerging next-generation internet paradigm. The Low Earth Orbit (LEO) satellite mega-constellation based on ICN can achieve seamless global coverage and provide excellent support for Internet of Things (IoT) services. Additionally, in-network caching, typically characteristic of ICN, plays a paramount role in network performance. Therefore, the in-network caching policy is one of the hotspot problems. Especially, compared to caching traditional internet content, in-networking caching IoT content is more challenging, since the IoT content lifetime is small and transient. In this paper, firstly, the framework of the LEO satellite mega-constellation Information-Centric Networking for IoT (LEO-SMC-ICN-IoT) is proposed. Then, introducing the concept of "viscosity", the proposed Caching Algorithm based on the Random Forest (CARF) policy of satellite nodes combines both content popularity prediction and satellite nodes location prediction, for achieving good cache matching between the satellite nodes and content. And using the matching rule, the Random Forest (RF) algorithm is adopted to predict the matching relationship among satellite nodes and content for guiding the deployment of caches. Especially, the content is cached in advance at the future satellite to maintain communication with the current ground segment at the time of satellite switchover. Additionally, the policy considers both the IoT content lifetime and the freshness. Finally, a simulation platform with LEO satellite mega-constellation based on ICN is developed in Network Simulator 3 (NS-3). The simulation results show that the proposed caching policy compared with the Leave Copy Everywhere (LCE), the opportunistic (OPP), the Leave Copy down (LCD), and the probabilistic algorithm which caches each content with probability 0.5 (prob 0.5) yield a significant performance improvement, such as the average number of hops, i.e., delay, cache hit rate, and throughput.

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.

Cache content placement in the presence of fictitious requests in mmWave 5G IAB networks

Caching popular content at the edge of wireless networks leads to backhaul congestion mitigation. To come up with an effective caching policy, content popularity distribution should be taken into account, which is not accurately known in most practical scenarios. Moreover, the mobile users’ (MU) request pattern may not always follow a well-defined distribution since some malicious MUs may deliberately issue their requests incompatible with the content popularity statistics. In this paper, we consider the problem of cache content placement in a 5G mmWave small cell network that relies on integrated access and backhaul (IAB) technology for pushing contents to MUs. We assume that the IAB node is equipped with a cache and has no prior knowledge about the content popularity profiles; instead, it only relies on the observation of the instantaneous demands to shape its caching policy. Also, malicious MUs may exist whose goals are to increase cache miss by issuing fictitious requests. The IAB node decides on which contents to cache and for how long, given that frequently replacing contents incurs administrative costs. We model the content placement problem as an ”adversarial combinatorial multi-armed bandit process with switching costs (ACMAB-SC)” and present an online learning algorithm for shaping the caching policy. We conduct extensive simulation experiments to evaluate the convergence property and assess the performance of our algorithm in terms of backhaul congestion, delay, and cache hit ratio. We also compare against two baseline online learning schemes, including a CMAB-based approach and a generic caching policy based on the ”Follow the Perturbed Leader (FPL)” algorithm.

Decoding Efficiency: A Technical Exploration of Apache, Nginx and Varnish Cache Server through Comprehensive Performance Metrics

Web servers play a crucial role in delivering web content efficiently to users. When a web server receives a request, it processes the request for the requested resources. One method to optimize this process is through the use of cache servers. Cache servers store frequently accessed data in memory, reducing the need to retrieve data from the original source each time a request is made. By leveraging cache servers, web servers can significantly enhance their performance by reducing response times. When a cache server successfully serves a request with a cache hit, it eliminates the need for the web server to process the request and retrieve the data, thereby speeding up the response time. This efficiency is crucial for improving user experience, as faster response times lead to quicker loading of web pages and reduced latency. Through this technical exploration of Apache, Nginx, and Varnish cache servers, we aim to analyze and compare their performance metrics to determine the most effective solution for reducing response times and optimizing web server efficiency. Understanding how cache servers impact performance in terms of cache hit ratio, cache miss ratio, client connections, CPU usage, memory usage, error rates, requests per second, and bandwidth will provide valuable insights into selecting the best cache server solution for improved web server performance. Keywords— Cache Server, Cache, Response time, Apache, Nginx, Varnish.

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.

A Fast GPU Schedule For À-Trous Wavelet-Based Denoisers

Given limitations of contemporary graphics hardware, real-time ray-traced global illumination is only estimated using a few samples per pixel. This consequently causes stochastic noise in the resulting frame sequences which requires wide filter support during denoising for temporally stable estimates. The edge avoiding à-trous wavelet transform amortizes runtime cost by hierarchical filtering using a constant number of increasingly dilated taps in each iteration. While the number of taps stays constant, the runtime of each iteration increases in these usually memory-throughput bound shaders with increasing dilation, because the increasing non-locality negatively impacts cache hit rates. We present a scheduling approach that optimizes usage of the memory subsystem by permutating global invocation indices in such a way that each wavelet filter iteration is applied through undilated taps. In contrast to prior approaches, our method has identical performance characteristics in each iteration, effectively decreasing maintenance cost and improving performance predictability. Furthermore, we are able to leverage on-chip memory and hardware texture interpolation. Our permutation strategy is trivial to integrate into existing wavelet filters as a permutation before and after each level of the wavelet filter. We achieve speedups between 1.3 and 3.8 for usual wavelet configurations in Monte Carlo denoising and computational photography.

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.

Cooperative Distributed Uplink Cache over B5G small cell networks.

The emergence of content-centric network has resulted in a substantial increase in data transmission in both uplink and downlink directions. To tackle the ensuing challenges of network congestion and bottlenecks in backhaul links within Beyond Fifth Generation (B5G) networks, data caching has emerged as a popular solution. However, caching for uplink transmission in a distributed B5G scenario poses several challenges, including duplicate content matching and users' obliviousness about cached contents. Furthermore, it is important to maximize available space by caching the most popular contents in a distributed manner. In this paper, we propose two schemes for uplink transmission in distributed B5G SCNs. The first scheme focuses on content matching to eliminate duplicate contents among distributed caches, while the second scheme redistributes un-duplicated cached contents among distributed caches based on their available space and content's size. These approaches aim to enhance energy and spectral efficiency by reducing unnecessary uploads and optimizing distributed content caching, in addition to improve the content delivery. The analysis shows that the proposed schemes outperform the existing schemes by improving the cache hit ratio, cache hit probability, overall distributed cache efficiency, and diversity by 29.17%, 74.89%, 24.17%, and, 80%, respectively. Furthermore, the average throughput, Spectrum Efficiency (SE), and Energy Efficiency (EE) of the access network is improved by 17.78%, 18%, and 78%, respectively. Besides that, the EE and SE of both the sidehaul and backhaul links of the SBSs are also improved.

Privacy-protecting predictive cache method based on blockchain and machine learning in Internet of vehicles

To solve the privacy leakage problem faced by Internet of Vehicles (IoV) users when enjoying location-based services (LBS), a privacy-protecting predictive cache method based on blockchain and machine learning (BML-PPPCM) is proposed. First, a Bi-directional Long-Short Term Memory (Bi-LSTM) model is used to predict query requests over a future period based on historical request information. The predicted results are recommended to neighbors and broadcast to requestors. Then, deep Q-learning (DQN) is utilized to determine the optimal cache decision. Finally, a trust mechanism is introduced to calculate trust values, and blockchain is used to store transaction data and trust data, preventing malicious tampering by attackers. The simulation results show that BML-PPPCM has a higher cache hit ratio than other similar schemes and performs well in privacy protection and suppression of malicious and incentive denial of service providers.

Blockchain Based Decentralized and Proactive Caching Strategy in Mobile Edge Computing Environment.

In the mobile edge computing (MEC) environment, the edge caching can provide the timely data response service for the intelligent scenarios. However, due to the limited storage capacity of edge nodes and the malicious node behavior, the question of how to select the cached contents and realize the decentralized security data caching faces challenges. In this paper, a blockchain-based decentralized and proactive caching strategy is proposed in an MEC environment to address this problem. The novelty is that the blockchain was adopted in an MEC environment with a proactive caching strategy based on node utility, and the corresponding optimization problem was built. The blockchain was adopted to build a secure and reliable service environment. The employed methodology is that the optimal caching strategy was achieved based on the linear relaxation technology and the interior point method. Additionally, in a content caching system, there is a trade-off between cache space and node utility, and the caching strategy was proposed to solve this problem. There was also a trade-off between the consensus process delay of blockchain and the caching latency of content. An offline consensus authentication method was adopted to reduce the influence of the consensus process delay on the content caching. The key finding was that the proposed algorithm can reduce latency and can ensure the security data caching in an IoT environment. Finally, the simulation experiment showed that the proposed algorithm can achieve up to 49.32%, 43.11%, and 34.85% improvements on the cache hit rate, the average content response latency, and the average system utility, respectively, compared to the random content caching algorithm, and it achieved up to 9.67%, 8.11%, and 5.95% increases, successively, compared to the greedy content caching algorithm.

QWLCPM: A Method for QoS-Aware Forwarding and Caching Using Simple Weighted Linear Combination and Proximity for Named Data Vehicular Sensor Network

The named data vehicular sensor network (NDVSN) has become an increasingly important area of research because of the increasing demand for data transmission in vehicular networks. In such networks, ensuring the quality of service (QoS) of data transmission is essential. The NDVSN is a mobile ad hoc network that uses vehicles equipped with sensors to collect and disseminate data. QoS is critical in vehicular networks, as the data transmission must be reliable, efficient, and timely to support various applications. This paper proposes a QoS-aware forwarding and caching algorithm for NDVSNs, called QWLCPM (QoS-aware Forwarding and Caching using Weighted Linear Combination and Proximity Method). QWLCPM utilizes a weighted linear combination and proximity method to determine stable nodes and the best next-hop forwarding path based on various metrics, including priority, signal strength, vehicle speed, global positioning system data, and vehicle ID. Additionally, it incorporates a weighted linear combination method for the caching mechanism to store frequently accessed data based on zone ID, stability, and priority. The performance of QWLCPM is evaluated through simulations and compared with other forwarding and caching algorithms. QWLCPM’s efficacy stems from its holistic decision-making process, incorporating spatial and temporal elements for efficient cache management. Zone-based caching, showcased in different scenarios, enhances content delivery by utilizing stable nodes. QWLCPM’s proximity considerations significantly improve cache hits, reduce delay, and optimize hop count, especially in scenarios with sparse traffic. Additionally, its priority-based caching mechanism enhances hit ratios and content diversity, emphasizing QWLCPM’s substantial network-improvement potential in vehicular environments. These findings suggest that QWLCPM has the potential to greatly enhance QoS in NDVSNs and serve as a promising solution for future vehicular sensor networks. Future research could focus on refining the details of its implementation, scalability in larger networks, and conducting real-world trials to validate its performance under dynamic conditions.

Intelligent Caching for Vehicular Dew Computing in Poor Network Connectivity Environments

In vehicular networks, some edge servers may not function properly due to the time-varying load condition and the uneven computing resource distribution, resulting in a low quality of caching services. To overcome this challenge, we develop a Vehicular dew computing (VDC) architecture for the first time by combining dew computing with vehicular networks, which can achieve wireless communication between vehicles in a resource-constrained environment. Consequently, it is crucial to develop an adaptive caching scheme that empowers vehicles to form efficient cooperation in VDC. In this paper, we propose an intelligent caching scheme based on VDC architecture, which includes two parts. First, to meet the dynamic nature of VDC, a spatiotemporal vehicle clustering algorithm is proposed to establish adaptive cooperation to assist content caching for vehicles. Second, the multi-armed bandit algorithm is employed to select suitable content for caching in vehicles based on real-time file popularity, and a model is established to dynamically update each vehicle’s request preferences. Extensive experiments are conducted to demonstrate that the proposed scheme has excellent performance in terms of cluster head stability and cache hit rate.

Optimizing CNN Hardware Acceleration with Configurable Vector Units and Feature Layout Strategies

Convolutional neural network (CNN) hardware acceleration is critical to improve the performance and facilitate the deployment of CNNs in edge applications. Due to its efficiency and simplicity, channel group parallelism has become a popular method for CNN hardware acceleration. However, when processing data involving small channels, there will be a mismatch between feature data and computing units, resulting in a low utilization of the computing units. When processing the middle layer of the convolutional neural network, the mismatch between the feature-usage order and the feature-loading order leads to a low input feature cache hit rate. To address these challenges, this paper proposes an innovative method inspired by data reordering technology, aiming to achieve CNN hardware acceleration that reuses the same multiplier resources. This method focuses on transforming the hardware acceleration process into feature organization, feature block scheduling and allocation, and feature calculation subtasks to ensure the efficient mapping of continuous loading and the calculation of feature data. Specifically, this paper introduces a convolutional algorithm mapping strategy and a configurable vector operation unit to enhance multiplier utilization for different feature map sizes and channel numbers. In addition, an off-chip address mapping and on-chip cache management mechanism is proposed to effectively improve the feature access efficiency and on-chip feature cache hit rate. Furthermore, a configurable feature block scheduling policy is proposed to strike a balance between weight reuse and feature writeback pressure. Experimental results demonstrate the effectiveness of this method. When using 512 multipliers and accelerating VGG16 at 100 MHz, the actual computing performance reaches 102.3 giga operations per second (GOPS). Compared with other CNN hardware acceleration methods, the average computing array utilization is as high as 99.88% and the computing density is higher.

Impact of Segmentation and Popularity-based Cache Replacement Policies on Named Data Networking

The data distribution mechanism of internet protocol (IP) technology is inefficient because it necessitates the user to await a response from the server. Named data networking (NDN) is a cutting-edge technology being assessed for enhancing IP networks, primarily because it incorporates a data packet caching technique on every router. However, the effectiveness of this approach is highly dependent on the router's content capacity, thus requiring the use data replacement mechanism when the router capacity is full. The least recently used (LRU) method is employed for cache replacement policy; yet, it is considered ineffective as it neglects the content's popularity. The LRU algorithm replaces the infrequently requested data, leading to inefficient caching of popular data when multiple users constantly request it. To address this problem, we propose a segmented LRU (SLRU) replacement strategy that considers content popularity. The SLRU will evaluate both popular content and content that has previously been popular in two segment categories, namely the probationary and protected segments. Icarus simulator was used to evaluate multiple comprehensive scenarios. Our experimental results show that the SLRU obtains a better cache hit ratio (CHR) and able to minimize latency and link load compared to existing cache replacement policies such as First In, First Out (FIFO), LRU, and Climb.

Shrinking VOD Traffic via Rényi-Entropic Optimal Transport

In response to the exponential surge in Internet Video on Demand (VOD) traffic, numerous research endeavors have concentrated on optimizing and enhancing infrastructure efficiency. In contrast, this paper explores whether users' demand patterns can be shaped to reduce the pressure on infrastructure. Our main idea is to design a mechanism that alters the distribution of user requests to another distribution which is much more cache-efficient, but still remains 'close enough' (in the sense of cost) to fulfil each individual user's preference. To quantify the cache footprint of VOD traffic, we propose a novel application of Rényi entropy as its proxy, capturing the 'richness' (the number of distinct videos or cache size) and the 'evenness' (the relative popularity of video accesses) of the on-demand video distribution. We then demonstrate how to decrease this metric by formulating a problem drawing on the mathematical theory of optimal transport (OT). Additionally, we establish a key equivalence theorem: minimizing Rényi entropy corresponds to maximizing soft cache hit ratio (SCHR) --- a variant of cache hit ratio allowing similarity-based video substitutions. Evaluation on a real-world, city-scale video viewing dataset reveals a remarkable 83% reduction in cache size (associated with VOD caching traffic). Crucially, in alignment with the above-mentioned equivalence theorem, our approach yields a significant uplift to SCHR, achieving close to 100%.

CTJIF-ICN: A Coadjuvant Trust Joint Interest Forwarding Mechanism in Information Centric Networks

The Information centric networks (ICN) transforms the focal point of current Internet paradigm to data centric approach from host centric approach by allowing content driven forwarding and in-network caching mechanisms. Though NDN (Named data networking) paradigm of ICN assures a secure content communication, it is vulnerable to different attacks by the malicious nodes. To minimize the hazards from compromised nodes and to improve the network security, the remaining nodes should transparently receive information about such nodes. This will restrict the forwarding strategy to exploit these malicious nodes for forwarding interest and content as well. Our protocol introduces a dynamic model for prediction of trust in order to evaluate the node trust. Proposed approach observes the historical behaviors of node and uses extended fuzzy logic rules for the prediction of future behaviors to evaluate the node’s trust value. This prediction model is incorporated within the trust based forwarding mechanism that aims to forward interest through secure and shortest path. The extensive simulation study has been carried out to analyze the protocol performance in ns-3 driven ndnSIM-2.0 simulator for performance metrics such as data discovery latency, packet delivery ratio, network overhead, detection ratio and cache hit ratio. When we integrate our trust joint forwarding strategy to state-of-the-art protocols, their performance is significantly improved up to approximately 10-35% against stated performance measures for realistic network topology.