Cache Storage Research Articles

Generalized hierarchical coded caching

Optimizing data traffic is a key concern in content distribution networks to reduce the bandwidth needed to serve the requested content to the final users. In this context, hierarchical coded caching has been proposed as an effective method for reducing traffic. The typical two-level scenario consists in a tree-like structure: on the first layer, a set of intermediate nodes or helpers with local caches store and serve content to a subset of users at the second layer, who also have their local caches. A central server partially stores its content between the first- and second-layer caches, in uncoded form, and subsequently transmits coded messages to satisfy the files demanded by the users. This topology has been thoroughly analyzed in the literature over the past years; however, little attention has been put in arbitrary topologies, where users might be connected to multiple helper nodes, which is especially interesting for scenarios such as wireless networks. Thus, in this paper we study a hierarchical two-level coded caching system with an arbitrary topology and propose a novel content distribution method for this generalized model that leverages the multiple connections between users and helpers to improve the performance of the distribution process. We give achievability results for this system and compare its performance with other classical single-layer and two-layer coded caching systems. Our results show that further gains are possible by using two levels of caching in different operating regimes.

To Cache or Not to Cache

Unlike conventional CPU caches, non-datapath caches, such as host-side flash caches which are extensively used as storage caches, have distinct requirements. While every cache miss results in a cache update in a conventional cache, non-datapath caches allow for the flexibility of selective caching, i.e., the option of not having to update the cache on each miss. We propose a new, generalized, bimodal caching algorithm, Fear Of Missing Out (FOMO), for managing non-datapath caches. Being generalized has the benefit of allowing any datapath cache replacement policy, such as LRU, ARC, or LIRS, to be augmented by FOMO to make these datapath caching algorithms better suited for non-datapath caches. Operating in two states, FOMO is selective—it selectively disables cache insertion and replacement depending on the learned behavior of the workload. FOMO is lightweight and tracks inexpensive metrics in order to identify these workload behaviors effectively. FOMO is evaluated using three different cache replacement policies against the current state-of-the-art non-datapath caching algorithms, using five different storage system workload repositories (totaling 176 workloads) for six different cache size configurations, each sized as a percentage of each workload’s footprint. Our extensive experimental analysis reveals that FOMO can improve upon other non-datapath caching algorithms across a range of production storage workloads, while also reducing the write rate.

The Implementation of PWA (Progressive Web App) Technology in Enhancing Website Performance & Mobile Accessibility

The implementation of PWA as a necessary feature aims to provide added value and enhance website performance. This is intended to address several common issues in websites, such as limitations in displaying pages offline and the cost of developing native applications across various operating system platforms, both for desktop and mobile devices. Data collection methods involve literature studies and direct measurements using various tools. Testing conducted includes installation testing, evaluation of PWA criteria, performance, size of transferred resources, and offline mode. Components used in PWA include the web app manifest, service worker, and cache storage. PWA implementation involves creating a web app manifest, service worker registration, service worker configuration, adding script tags, creating specific routes within the website using Express.js, and PWA testing. Test results indicate that the website can be installed and used effectively on various types of devices, both mobile and desktop, and can be accessed in offline mode or with unstable connections.

A survey of classification cache replacement techniques in the contentcentric networking domain

Content-Centric Networking (CCN) is an innovative approach that emphasizes content. A key strategy in CCN for spreading data across the network is in-network caching. Effective caching methods, including content placement and removal tactics, enhance the use of network resources. Cache replacement, also known as content eviction policies, is essential for maximizing CCN's efficiency. When cache storage is full, some content must be removed to make room for new items due to limited storage space. Recently, several advanced replacement strategies have been developed to determine the most suitable content for eviction. This study categorizes the latest cache replacement strategies into various groups such as static, space scarcity, content update, centralized, energy-efficient, weighted, adaptive, and based on dynamic popularity. These categories are based on the approaches suggested in previous research. Additionally, this paper provides a critical analysis of existing methods and suggests future research directions. To the best of our knowledge, this is the most up-to-date and comprehensive review available on this topic.

Accelerating multi-tier storage cache simulations using knee detection

Storage cache hierarchies include diverse topologies, assorted parameters and policies, and devices with varied performance characteristics. Simulation enables efficient exploration of their configuration space while avoiding expensive physical experiments. Miss Ratio Curves (MRCs) efficiently characterize the performance of a cache over a range of cache sizes, revealing “key points” for cache simulation, such as knees in the curve that immediately follow sharp cliffs. Unfortunately, there are no automated techniques for efficiently finding key points in MRCs, and the cross-application of existing knee-detection algorithms yields inaccurate results.We present a multi-stage framework that identifies key points in any MRC, for both stack-based (e.g., LRU) and more sophisticated eviction algorithms (e.g., ARC). Our approach quickly locates candidates using efficient hash-based sampling, curve simplification, knee detection, and novel post-processing filters. We introduce Z-Method, a new multi-knee detection algorithm that employs statistical outlier detection to choose promising points robustly and efficiently.We evaluated our framework against seven other knee-detection algorithms, identifying key points in multi-tier MRCs with both ARC and LRU policies for 106 diverse real-world workloads. Compared to naïve approaches, our framework reduced the total number of points needed to accurately identify the best two-tier cache hierarchies by an average factor of approximately 5.5× for ARC and 7.7× for LRU.We also show how our framework can be used to seed the initial population for evolutionary algorithms. We ran 32,616 experiments requiring over three million cache simulations, on 151 samples, from three datasets, using a diverse set of population initialization techniques, evolutionary algorithms, knee-detection algorithms, cache replacement algorithms, and stopping criteria. Our results showed an overall acceleration rate of 34% across all configurations.

Cache Control of Edge Computing System for Tradeoff Between Delays and Cache Storage Costs

Cache Control of Edge Computing System for Tradeoff Between Delays and Cache Storage Costs

Research on Storage Structure Design and Performance Optimization Strategy of Embedded System for Low-Capacity Storage Units

Abstract Embedded systems have attracted great attention with their unique information processing ability and human-computer interaction ability, and how to improve the processing efficiency of embedded systems for low-capacity storage units has become a key topic of current research. Based on the data storage structure characteristics and hardware devices of embedded systems, we complete the task of storage structure design for embedded systems and describe the interaction mechanism of Cache storage mode and low-capacity storage mode. Using the POSE algorithm, we optimize the execution efficiency and module energy overhead in the storage structure of the embedded system and test and optimize the embedded system. The low-capacity storage mode has fewer physical block erasures than the Cache storage mode, and the response time is 15-50 ms less. Similarly, the PEOS algorithm has advantages in the optimization of the energy overhead of the embedded system, which ranges from 0 to 0.23 × 104. This study can effectively improve the execution efficiency and energy overhead of the storage, which is of great significance for its further development.

Joint Service Caching and Computing Resource Allocation for Edge Computing-Enabled Networks

In this paper, we consider the service caching and the computing resource allocation in edge computing (EC) enabled networks. We introduce a random service caching design considering multiple types of latency sensitive services and the base stations (BSs)’ service caching storage. We then derive a successful service probability (SSP). We also formulate a SSP maximization problem subject to the service caching distribution and the computing resource allocation. Then, we show that the optimization problem is nonconvex and develop a novel algorithm to obtain the stationary point of the SSP maximization problem by adopting the parallel successive convex approximation (SCA). Moreover, to further reduce the computational complexity, we also provide a low complex algorithm that can obtain the near-optimal solution of the SSP maximization problem in high computing capability region. Finally, from numerical simulations, we show that proposed solutions achieve higher SSP than baseline schemes. Moreover, we show that the near-optimal solution achieves reliable performance in the high computing capability region. We also explore the impacts of target delays, a BSs’ service cache size, and an EC servers’ computing capability on the SSP.

DEAD FORENSIC ANALYSIS OF QUTEBROWSER AND LIBREWOLF BROWSERS USING THE NIST 800-86 METHOD

A browser is software used to access web pages to obtain clear and readable information. Information resources are identified by a Uniform Resource Identifier (URI) and can be web pages, images, videos, or other content. When a browser user engages in online activities, they usually leave traces on the device such as history, cookies, cache files, and even emails and passwords. Such traces can usually help users access a website or input something, such as emails and passwords. The purpose of this research is to obtain digital evidence in the form of a cache on the hard disk in the Librewolf and Qutebrowser browsers. In this study, researchers used the National Institute of Standards and Technology (NIST) 800-86 method which consists of four stages, namely collection, examination, analysis, and reporting. which focuses on the Qutebrowser and LibreWolf browsers. The results obtained from this study were found to be 21 caches, 2 Sessions, 6 Cookies, 8 Network Persistent State, 9 QuotaManager, 11 IndexedDB, 24 LevelDB, 48 Cache Storage, 14 Favicons, 3 History, 6 Database, 3 StartupCache, 4 Alternate Services, 6 Content-Pref, Notification amounted to 1, Permission amounted to 7, Service Worker amounted to 6, SiteSecuristyServiceState amounted to 7, Webappstore amounted to 8, Sessionstore-Backups amounted to 5, Storage amounted to 47 NIST 800-86 method can be properly used in the acquisition of digital evidence and the most crucial data obtained in the Librewolf browser on the telegram and whatsapp sites.

Switching mode allocation in planning paths for vehicular network communication

Because of the increased mobility of vehicle users, it might be difficult to keep communication services in vehicle networks effective and dependable. Huge hurdles have been presented to vehicular networks as a result of the meteoric rise in the amount of data, which comes with the needs of high dependability and low latency. The deployment of access point servers at geographic locations that are closer to the vehicles in order to provide real-time service to applications that are based on the vehicles is one possible option. However, there is a limited amount of cache store space, and there is also a lack of a tractable access mode allocation method. As a result of these factors, it is very difficult to strike a compromise between the network transmission performance and fronthaul savings. Because the signal-to-interference-ratio (SIR) can be enhanced with switching mode in vehicular infrastructure, it may be possible to achieve higher levels of dependability. To serve all of the vehicles, the conventional allocation in vehicular network may not be sufficient on its own for two reasons: (1) the number of vehicles exceeds the number of paths, and (2) a vehicle may be located outside of the coverage path. Therefore, the implementation of switching mode allocation in vehicular communication is very necessary in order to increase the number of vehicles that can be supplied. In this paper, allocation using V2I, V2V, and V2X modes have been analyzed to provide dependable coverage for vehicles. These methods are used for communicating with other vehicles. In this paper, the numerical analysis has been performed such that SIR is optimized. In switching mode allocation, it has been shown that establishing a variable SIR threshold is helpful in achieving a path coverage that can be relied upon. It has been shown beyond a reasonable doubt that the coverage probability is likewise directly dependent on SIR thresholds. The theoretical analysis is verified, and it is confirmed that the suggested method is capable of achieving significant performance improvement in terms of coverage probability and data rate.

The comparative performance analysis of selected relational database systems

The objective of this study was to carry out a performance analysis of the following database systems: MySQL, PostgreSQL and Microsoft SQL Server. For this purpose scripts were used to measure execution times of selecting, updating and inserting data. Furthermore, three data sets were utilized consisting of 100, 1000 and 10000 rows. The experiment included nine cases depending on the query type and the data set. For each case, thirty five test trials were conducted while first five trials were ignored i.a. because of cache storage. The statistical test was performed for the results and the trials in which the DBMS achieved best times were counted. For each case best systems were acknowledged and the most efficient system of the experiment was determined along with systems for each operation type.

NCache: A Machine-Learning Cache Management Scheme for Computational SSDs

Inside a solid-state disk (SSD), cache stores frequently accessed data to shorten user-I/O response time and reduce the number of read/write operations in flash memory, thereby improving SSD performance and lifetime. Most existing cache schemes anchor in the spatiotemporal locality of I/O requests in workloads. In the face of a long-time workload, high performance and hit rate often get lost in these caching schemes. Flash memory-aware caching schemes trade hit ratio to prolong SSD lifetime. In this paper, we advocate for a machine learNing-based caching scheme named NCache to optimize both hit ratio and SSD performance. In NCache, we construct a machine learning (i.e., ML) model to predict whether data are re-accessed before being evicted from the cache. The cache replacement scheme preferentially evicts data that would not be accessed in the cache. The cache space is conserved for valid data that are likely to be repeatedly accessed. A pipelined scheme is implemented to accelerate the ML model, alleviating the time-cost of NCache. A double-linked list boosts the data addressing and cache replacement process. NCache is orthogonal to the existing caching schemes within the flash translation layer. The results validate NCache under a handful of real-world enterprise traces. Taking prn_0 as an example, NCache reduces the response time of LRU, CFLRU, GCaR_LRU, GCaR_CFLRU, and LCR by up to 15% with an average of 6.4%. The erase count is slashed by 16% at the maximum. Importantly, NCache is adroit at optimizing write amplification by up to 15.9%.

Multi-Rate Probabilistic Caching Optimized Video Offloading in Dense D2D Networks

In this paper, we propose a multi-rate caching (MRC) scheme for video offloading in dense device-to-device (D2D) networks. The MRC scheme utilises homogeneous Poisson Point Process model, D2D underlay transmission mode, and features of videos encoded into multiple rates (or versions), to derive the cache and spectrum sharing hit probabilities and the cache-based achievable rate. This way, an MRC optimisation problem is formulated for optimal caching probability and encoding rate selection of each video. The MRC optimisation problem further considers the playback resolution and storage resource constraints of the mobile devices to serve the mobile users more efficiently. As a result, the MRC solution gains the highest video offloading playback quality while conserving the bandwidth and caching storage resources of dense D2D networks.

Latency Minimization for Mobile Edge Computing Networks

The proliferation of data-intensive mobile applications is causing latency to become an issue in mobile edge computing (MEC) systems. In this work, we propose a novel methodology that optimizes communication, computation, and caching configurations in MEC to minimize the mean latency experienced by mobile devices. Transmission and computation processes are modeled using M/G/1 queues to account for service rates and warm-up times. Our caching scheme includes time variables for each file at each edge server in determining when to discard files from storage. We theoretically analyze the latency experienced by mobile devices due to communication, computation, and caching, showing how MEC system latency depends on the offloading decisions of mobile devices, bandwidth and CPU resources, and expiration times of files in the storage of edge servers. Our method for solving the latency minimization problem consists of two main components: iNner cOnVex Approximation (NOVA) to deal with non-convexity in the optimization, and an online algorithm for preventing cache storage violations as new tasks arrive and are serviced by the MEC system. Simulation results show that our algorithm outperforms several baselines in minimizing latency, and verify the benefit of including different resource allocation variables in our optimization.

Nutcracker optimizer: A novel nature-inspired metaheuristic algorithm for global optimization and engineering design problems

This work presents a novel nature-inspired metaheuristic called Nutcracker Optimization Algorithm (NOA) inspired by Clark’s nutcrackers. The nutcrackers exhibit two distinct behaviors that occur at separate periods. The first behavior, which occurs during the summer and fall seasons, represents the nutcracker’s search for seeds and subsequent storage in an appropriate cache. During the winter and spring seasons, another behavior based on the spatial memory strategy is regarded to search for the hidden caches marked at different angles using various objects or markers as reference points. If the nutcrackers cannot find the stored seeds, they will randomly explore the search space to find their food. NOA is herein proposed to mimic these various behaviors to present a new, robust metaheuristic algorithm with different local and global search operators, allowing it to solve various optimization problems with better outcomes. NOA is evaluated on twenty-three standard test functions, test suites of CEC-2014, CEC-2017, and CEC-2020 and five real-world engineering design problems. NOA is compared with three classes of existing optimization algorithms: (1) SMA, GBO, EO, RUN, AVOA, RFO, and GTO as recently-published algorithms, (2) SSA, WOA, and GWO as highly-cited algorithms, and (3) AL-SHADE, L-SHADE, LSHADE-cnEpSin, and LSHADE-SPACMA as highly-performing optimizers and winners of CEC competition. NOA was ranked first among all methods and demonstrated superior results when compared to LSHADE-cnEpSin and LSHADE-SPACMA as the best-performing optimizers and the winners of CEC-2017, and AL-SHADE and L-SHADE as the winners of CEC-2014.

A Compensation Strategy for Mitigating Intermittencies Within a PV Powered Microgrid Using a Hybrid Multilevel Energy Storage System

Solar PV intermittencies due to passing clouds with high ramp rates occurring for a short time produce a significant challenge to grid voltage and frequency regulations of a PV-rich microgrid. To compensate for these high ramp rates, battery and/or supercapacitors (SCs) are commonly employed using the control strategy based on a moving average (MA) or low pass filter (LPF). However, the traditional MA and LPF schemes suffer from memory and time lag effects, resulting in the need for increased storage size. To address these issues, this paper describes the design and implementation of a generic ramp-rate-based compensation strategy for smoothing the solar power output and meeting rapid load demands in grid-interactive microgrids using a hybrid multilevel storage system, consisting of high-capacity buffer storage such as battery system and hydrogen storage comprising a proton exchange membrane electrolyzer and a fuel cell, and short-term cache storage such as supercapacitors, integrated in the DC link of the power converter. This hybrid multilevel modular storage system is required to rapidly control fluctuations in solar PV outputs and load demands over a wide range of time scales and regulate the voltage at the point of common coupling (PCC). The proposed compensation strategy is modeled and validated through simulations using a measured 24-h solar irradiance profile applied to a 100-kW grid-interactive PV-dominated microgrid system.

Distributed Resource Management in Downlink Cache-Enabled Multi-Cloud Radio Access Networks

In light of the premises of beyond fifth generation (B5G) networks, the need for better exploiting the capabilities of cloud-enabled networks arises, so as to cope with the large-scale interference resulting from the massive increase of data-hungry systems. A compound of several clouds, jointly managing inter-cloud and intra-cloud interference, constitutes a practical solution to account for the requirements of B5G networks. This paper considers a multi-cloud radio access network model (MC-RAN), where each cloud is connected to a distinct set of base stations (BSs) via limited capacity fronthaul links. The BSs are equipped with local cache storage and baseband processing capabilities, as a means to alleviate the fronthaul congestion problem. The paper then investigates the problem of jointly assigning users to clouds and determining their beamforming vectors so as to maximize the network-wide energy efficiency (EE) subject to fronthaul capacity and transmit power constraints. This paper solves such a mixed discrete-continuous, non-convex optimization problem using fractional programming (FP) and successive inner-convex approximation (SICA) techniques to deal with the non-convexity of the continuous part of the problem, and $l_0$-norm approximation to account for the binary association part. A highlight of the proposed algorithm is its capability of being implemented in a distributed fashion across the network's multiple clouds through a reasonable amount of information exchange. The numerical simulations illustrate the pronounced role the proposed algorithm plays in alleviating the interference of large-scale MC-RANs, especially in dense networks.

A Network-Embedding-Based Approach for Scalable Network Navigability in Content-Centric Social IoT

Social Internet of Things (SIoT) boosts the Internet of Things (IoT) by integrating the concept of social networking to advance the discovery of content and service. Another common and promising solution to IoT’s improvement is adopting the emerging multiaccess edge computing (MEC) paradigm by bringing computational and storage capacity at the edge. Concerning the MEC-enabled SIoT, this article aims to propose a network-embedding-based solution for scalable network navigation via leveraging the social similarity of SIoT. Different from the traditional SIoT, we characterize the social relationship from the contents point of view since MEC enables the edge with cache storage. Then, a novel heuristic embedding algorithm termed HeurEmb is proposed to embed the SIoT into the hyperbolic space to facilitate coordinate-based navigability. HeurEmb is a decentralized approach that leverages the social similarity to compute coordinates, bypassing the heavy computation of numerically maximizing the likelihood. Moreover, HeurEmb infers the virtual coordinates on multiple-level hyperbolic disks, which enabled the efficiently vertical and horizontal search for the destination. We analyze the complexity of HeurEmb and then evaluate its performance via the simulation study. The simulation results show that HeurEmb is efficient, achieving a decrease of up to two orders of magnitude of running time compared with the latest work, and effective, improving the success ratio and navigation path length. Finally, we apply HeurEmb in a content retrieval scenario of SIoT using a real-world trace. We make only a minor modification to HeurEmb-based forwarding, and the resulting strategy can achieve similar performance as the best benchmark.

Cooperative caching for scalable video coding using value-decomposed dimensional networks

Scalable video coding (SVC) has been widely used in video-on-demand (VOD) service, to efficiently satisfy users' different video quality requirements and dynamically adjust video stream to time-variant wireless channels. Under the 5G network structure, we consider a cooperative caching scheme inside each cluster with SVC to economically utilize the limited caching storage. A novel multi-agent deep reinforcement learning (MADRL) framework is proposed to jointly optimize the video access delay and users' satisfaction, where an aggregation node is introduced helping individual agents to achieve global observations and overall system rewards. Moreover, to cope with the large action space caused by the large number of videos and users, a dimension decomposition method is embedded into the neural network in each agent, which greatly reduce the computational complexity and memory cost of the reinforcement learning. Experimental results show that: 1) the proposed value-decomposed dimensional network (VDDN) algorithm achieves an obvious performance gain versus the traditional MADRL; 2) the proposed VDDN algorithm can handle an extremely large action space and quickly converge with a low computational complexity.

Improving biomedical named entity recognition by dynamic caching inter-sentence information.

Biomedical Named Entity Recognition (BioNER) aims to identify biomedical domain-specific entities (e.g. gene, chemical and disease) from unstructured texts. Despite deep learning-based methods for BioNER achieving satisfactory results, there is still much room for improvement. Firstly, most existing methods use independent sentences as training units and ignore inter-sentence context, which usually leads to the labeling inconsistency problem. Secondly, previous document-level BioNER works have approved that the inter-sentence information is essential, but what information should be regarded as context remains ambiguous. Moreover, there are still few pre-training-based BioNER models that have introduced inter-sentence information. Hence, we propose a cache-based inter-sentence model called BioNER-Cache to alleviate the aforementioned problems. We propose a simple but effective dynamic caching module to capture inter-sentence information for BioNER. Specifically, the cache stores recent hidden representations constrained by predefined caching rules. And the model uses a query-and-read mechanism to retrieve similar historical records from the cache as the local context. Then, an attention-based gated network is adopted to generate context-related features with BioBERT. To dynamically update the cache, we design a scoring function and implement a multi-task approach to jointly train our model. We build a comprehensive benchmark on four biomedical datasets to evaluate the model performance fairly. Finally, extensive experiments clearly validate the superiority of our proposed BioNER-Cache compared with various state-of-the-art intra-sentence and inter-sentence baselines. Code will be available at https://github.com/zgzjdx/BioNER-Cache. Supplementary data are available at Bioinformatics online.