Caching Mechanism Research Articles

Memoization in Model Checking for Safety Properties with Multi-Swarm Particle Swarm Optimization

In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checking, the task of finding specification errors is reduced to deterministic search techniques such as Depth-First Search. Recent research has shown that swarm intelligence offers a powerful search capability compared to traditional techniques. In particular, multi-swarm Particle Swarm Optimization is known to be efficient and can mitigate the state-space explosion problem, i.e., the exponential increase in the search space with a linear increase in the problem size. However, the state-space explosion problem is still significant when verifying very large systems. Further performance improvement is needed. To achieve this, we propose a novel memoization or cache mechanism for storing tentative solutions for reuse in the later stages of the search procedure. For each stage, a candidate solution computed by a swarm is summarized efficiently and heuristically to consolidate similar solutions into a single representative solution. We store the summary and its associated solutions in key-value maps. Instead of computing known solutions repeatedly, we retrieve the solution if the stored key matches the summary. We incorporated the proposed mechanism into a model-checking technique with multi-swarm Particle Swarm Optimization and evaluated the search performance. We show in this paper that the proposed mechanism improved time and space consumption while maintaining solution quality.

An on-demand collaborative edge caching strategy for edge–fog–cloud environment

In this paper, we tackle the critical challenges of content edge caching, such as limited storage capacity, content popularity prediction, dynamic user demand, and user privacy, issues that most existing studies only address partially. We present an innovative Genetic Algorithm-based On-demand Collaborative Edge Caching mechanism (GAOCEC), which introduces a multi-tiered caching architecture integrating cloud, fog, and edge computing. To enhance caching efficiency and minimize system cost, a novel on-demand caching quota mechanism is proposed that dynamically allocates cache resources to edge servers. To strengthen user privacy protection during content popularity prediction, a CNN-BiLSTM-based Federated Learning algorithm (CBFL) is presented that ensures high prediction accuracy without the need to upload local data to the cloud. We also refine the genetic algorithm for content placement by fine-tuning various parameter sets to identify the optimal balance between latency reduction and caching cost. Our experimental results validate the effectiveness of our approach, demonstrating increased cache hit rates, decreased content response times, and an overall improvement in system efficiency. This work provides a comprehensive, adaptive, and privacy-preserving solution for the edge–fog–cloud environment.

Enhancing smart city IoT communication: A two-layer NOMA-based network with caching mechanisms and optimized resource allocation

With advancements in next-generation communication systems, large-scale Internet of Things device (IoTDs) deployments in smart cities face challenges like limited bandwidth, high latency, and network congestion. To address this, we propose a two-layer network architecture utilizing non-orthogonal multiple access (NOMA) and caching to enhance IoT communications’ performance, efficiency, and reliability. Our primary objective is to optimize resource allocation and solve the association problem in a two-layer network. We formulated a joint optimization problem to maximize system utility through device association, power, and bandwidth allocation, considering constraints like channel quality and interference. We decoupled the non-linear, non-convex problem using block coordinate descent (BCD) and inner approximation techniques to maximize the aggregated data rate in high-density IoT scenarios. This approach reduced computational complexity while proving the scheme’s theoretical and numerical convergence. To evaluate the proposed scheme, we compared its performance with an ideal backhauling approach, an exhaustive search (upper bound), and a Genetic algorithm-based heuristic. Our scheme outperformed the others, achieving 98.04% of the ideal backhauling and 99.60% of the upper bound. Statistical analysis confirmed its robustness and consistent performance across various conditions. The two-layer NOMA-based network with caching and optimized resource allocation significantly enhances IoT communication efficiency and resilience, offering a solid framework for future smart city deployments.

Improving Cloud Database Performance Using Storage Technologies

Cloud databases have become indispensable for modern applications due to their scalability, flexibility, and cost-effectiveness. However, optimizing performance in cloud environments can be challenging due to factors such as network latency, data distribution, and resource contention. This paper explores various storage technologies and techniques that can significantly enhance cloud database performance. We delve into the characteristics of different storage types, including solid-state drives (SSDs), hard disk drives (HDDs), and object storage, and discuss their suitability for various workloads. Additionally, we examine caching mechanisms, data compression, and deduplication strategies that can optimize data access and reduce storage costs. Furthermore, we explore the impact of database architecture choices, such as distributed databases and inmemory databases, on performance. We also discuss the importance of effective query optimization and indexing techniques. By understanding and implementing these storage technologies and techniques, organizations can achieve substantial improvements in cloud database performance, leading to better user experiences and increased application efficiency

Designing a Multi-Level Caching Forward Index for Recommendation System Services

This paper introduces a forward index based on microservices, which effectively addresses the issue of forward read amplification in recommendation systems through a multi-layer caching mechanism. This allows the forward service to handle a large number of concurrent requests with limited resources while ensuring service stability and final value rate. The paper provides a detailed description of the forward index's data structure design, traffic penetration calculation, and overall service architecture design, dividing the system into three parts: SDK, proxy service, and storage, corresponding to data query, data processing, and data storage respectively. In the SDK design, three components are employed: Cache Slot, synchronous queue, and timer, supporting synchronous, asynchronous, and flexible request forms. The proxy service design handles heterogeneous data and online computation, supporting batch and asynchronous designs, and provides a dynamic data protocol to adapt to the characteristic needs of different microservices. The storage layer design considers performance, cost, and maintainability, utilizing a Cache & SSD model to cope with high-concurrency scenarios.

Dual-stage semantic segmentation of endoscopic surgical instruments.

Endoscopic instrument segmentation is essential for ensuring the safety of robotic-assisted spinal endoscopic surgeries. However, due to the narrow operative region, intricate surrounding tissues, and limited visibility, achieving instrument segmentation within the endoscopic view remainschallenging. This work aims to devise a method to segment surgical instruments in endoscopic video. By designing an endoscopic image classification model, features of frames before and after the video are extracted to achieve continuous and precise segmentation of instruments in endoscopicvideos. Deep learning techniques serve as the algorithmic core for constructing the convolutional neural network proposed in this study. The method comprises dual stages: image classification and instrument segmentation. MobileViT is employed for image classification, enabling the extraction of key features of different instruments and generating classification results. DeepLabv3+ is utilized for instrument segmentation. By training on distinct instruments separately, corresponding model parameters are obtained. Lastly, a flag caching mechanism along with a blur detection module is designed to effectively utilize the image features in consecutive frames. By incorporating specific parameters into the segmentation model, better segmentation of surgical instruments can be achieved in endoscopicvideos. The classification and segmentation models are evaluated on an endoscopic image dataset. In the dataset used for instrument segmentation, the training set consists of 7456 images, the validation set consists of 829 images, and the test set consists of 921 images. In the dataset used for image classification, the training set consists of 2400 images and the validation set consists of 600 images. The image classification model achieves an accuracy of 70% on the validation set. For the segmentation model, experiments are conducted on two common surgical instruments, and the mean Intersection over Union (mIoU) exceeds 98%. Furthermore, the proposed video segmentation method is tested using videos collected during surgeries, validating the effectiveness of the flag caching mechanism and blur detection module. Experimental results on the dataset demonstrate that the dual-stage video processing method excels in performing instrument segmentation tasks under endoscopic conditions. This advancement is significant for enhancing the intelligence level of robotic-assisted spinal endoscopic surgeries.

An efficient algorithm for resource optimization in TWDM passive optical network using a C-RAN

The traditional base station in C-RAN is divided into three parts: a pool of centralized baseband units (BBUs), a fronthaul network that links the BBUs and remote radio units (RRUs), and RRUs. This paper proposes a novel cooperative algorithm for resource optimization in a time-wavelength division multiplexed (TWDM) passive optical network (PON) incorporating a cloud radio access network (C-RAN). First, a joint collaborative strategy is deployed to optimize cooperative caching and transmission in the wireless and optical domains. Then, the quality of experience (QoE) is improved by bandwidth configuration and caching. Simulation results show that the average throughput of the proposed QoE-aware video cooperative caching and transmission mechanism (QACCTM) algorithm is approximately 30% higher than that of other algorithms. Compared with the relative average residual clutter power (RARCP) and quality-aware wireless edge caching (QAWEC) algorithms, the proposed QACCTM algorithm reduces the access delay by approximately 27.1% and 15.9%, respectively.

RMBCC: A Replica Migration-Based Cooperative Caching Scheme for Information-Centric Networks

How to maximize the advantages of in-network caching under limited cache space has always been a key issue in information-centric networking (ICN). Replica placement strategies aim to fully utilize cache resources by optimizing the location and quantity distribution of replicas in the network, thereby improving the performance of the cache system. However, existing research primarily focuses on optimizing the placement of replicas along the content delivery path, which cannot avoid the inherent drawback of not being able to leverage off-path cache resources. The proposals for off-path caching cannot effectively solve this problem as they introduce excessive complexity and cooperation costs. In this paper, we address the trade-off between cache resource utilization and cooperation costs by introducing a mechanism complementary to replica placement. Instead of redesigning a new caching strategy from scratch, we propose a proactive cooperative caching mechanism (called RMBCC) that involves an independent replica migration process, through which we proactively relocate replicas evicted from the local cache to neighboring nodes with sufficient cache resources. The cooperation costs are effectively controlled through migration replica filtering, migration distance limitation, as well as hop-by-hop migration request propagation. Extensive simulation experiments show that RMBCC can be efficiently integrated with different on-path caching strategies. Compared with representative caching schemes, RMBCC achieves significant improvements in evaluation metrics such as cache hit ratio and content retrieval time, while only introducing negligible cooperation overhead.

Research on the Architectural Design and Performance Optimization of Large Scale Real-Time Chat Systems

With the rapid development of internet technology, large-scale real-time chat systems have become an essential tool for connecting users worldwide. These systems not only support the core functions of social communication but also serve as key platforms for business, education, and remote collaboration. However, the surge in user numbers and the explosive growth in message volume pose unprecedented technical challenges to real-time chat systems, including high concurrency processing, low latency communication, data consistency assurance, and system scalability. This study aims to explore and implement an efficient, stable, and scalable architecture for large-scale real-time chat systems and propose corresponding performance optimization strategies. The research employs literature review, system analysis, prototype design, and performance evaluation methods. It first analyzes the design limitations and performance bottlenecks of existing real-time chat systems. Based on this analysis, a microservices-based system design scheme is proposed, which enhances system performance and reliability through componentized services, message queues, load balancing, and caching mechanisms. In terms of performance optimization, this paper focuses on key technologies such as load balancing algorithms, asynchronous processing mechanisms of message queues, caching strategies, and data storage optimization. Experimental evaluations have confirmed the significant effects of the proposed scheme in handling high-concurrency requests, reducing system latency, and improving data throughput.

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.

OEM: An operation-aware event matching algorithm for content-based Pub/Sub systems

Due to the continuous expansion of the user base and the heightened demands for quality of service (QoS), real-time data distribution has encountered significant challenges in event matching within large-scale content-based publish/subscribe (CPS) systems. Many efficient algorithms have been proposed to enhance the matching performance. However, most existing algorithms scarcely consider operation characteristics, which often leads to performance degradation due to a multitude of repetitive operations, such as comparisons, additions, and assignments. In this paper, we propose an Operation-aware Event Matching algorithm called OEM, which takes into account the efficiency of CPU operations, SIMD instructions, and multi-thread architecture. First, we introduce a bitset-based subscription cache mechanism (SCM), which enables the efficient execution of bitwise OR operations during the matching process. Furthermore, we propose two optimizations, namely an optimal skewness-aware cell grouping (OSCG) strategy and an attribute-based clustering mechanism (ACM), to enhance the efficiency of OEM. These optimizations effectively address the challenges posed by data skewness, low cache efficiency, and high dimensionality. In addition, we establish a performance analysis model that characterizes the trade-off between memory consumption and performance improvement. We conducted extensive experiments to evaluate the performance of OEM. Compared to six state-of-the-art algorithms, namely REIN, Ada-REIN, TAMA, OpIndex, PEM and fgPEM, OEM achieves an average reduction in matching time by 40.4×, 38.5×, 12.0×, 33.1×, 27.8×, and 25.5×, respectively.

FASA-DRAM: Reducing DRAM Latency with Destructive Activation and Delayed Restoration

DRAM memory is a performance bottleneck for many applications, due to its high access latency. Previous work has mainly focused on data locality, introducing small but fast regions to cache frequently accessed data, thereby reducing the average latency. However, these locality-based designs have three challenges in modern multi-core systems: (1) inter-application interference leads to random memory access traffic, (2) fairness issues prevent the memory controller from over-prioritizing data locality, and (3) write-intensive applications have much lower locality and evict substantial dirty entries. With frequent data movement between the fast in-DRAM cache and slow regular arrays, the overhead induced by moving data may even offset the performance and energy benefits of in-DRAM caching. In this article, we decouple the data movement process into two distinct phases. The first phase is Load-Reduced Destructive Activation (LRDA), which destructively promotes data into the in-DRAM cache. The second phase is Delayed Cycle-Stealing Restoration (DCSR), which restores the original data when the DRAM bank is idle. LRDA decouples the most time-consuming restoration phase from activation, and DCSR hides the restoration latency through prevalent bank-level parallelism. We propose FASA-DRAM, incorporating destructive activation and delayed restoration techniques to enable both in-DRAM caching and proactive latency-hiding mechanisms. Our evaluation shows that FASA-DRAM improves the average performance by 19.9% and reduces average DRAM energy consumption by 18.1% over DDR4 DRAM for four-core workloads, with less than 3.4% extra area overhead. Furthermore, FASA-DRAM outperforms state-of-the-art designs in both performance and energy efficiency.

Hybrid deduplication system with content-based cache for cloud environment

Primary storage deduplication systems are performance sensitive. Their performance depends upon two factors — metadata access for duplicate detection and strategy for elimination of duplicate data. Various approaches for duplicate detection through suitable caching mechanisms have been proposed in the literature. Most of the approaches assumed that the primary workloads exhibit strong temporal locality. Whereas, this cannot be assumed in the context of Cloud as the workloads locality does not exist with interferences among different workloads on the same system. Duplicate content among the data blocks with different addresses lead to an inefficient utilization of the data cache. In this context, applying deduplication causes sharing of the data blocks among the clients with different access patterns and frequencies. In this situation, LRU cache, which considers only the recency of the references, is not appropriate. In this paper, Hybrid Deduplication System (HDS) containing the content-based cache with a new replacement policy — Modified Adaptive Replacement Cache (ARC), is proposed. The proposed system is simulated in the Linux environment using three different types of FIU traces. Effectiveness of the system is compared with a full deduplication system. Experimental results show that the system has performed consistently better than the full deduplication system in reducing the metadata overhead for all of the three data sets.

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%.

An adaptive differential evolution algorithm based on archive reuse

In recent years, differential evolution algorithms based on archives have achieved significant success because archives can increase population diversity and balance the exploration and exploitation of algorithms. However, insufficient utilisation of archives has led to an imbalance between exploration and exploitation. Herein, a new archive-reuse-based adaptive differential evolution (AR-aDE) algorithm framework is proposed that can be applied to (L)SHADE and its variants. It comprises three main strategies. First, a new external archive update method based on a cache mechanism is proposed, in which the archive size is the same as the population size, eliminating the need to adjust its size. Second, influenced by knowledge transfer in multitasking optimisation, we designed a new method of reusing the archive to better utilise the information in it. Finally, the classic parameter adaptation method was improved. The experimental results for the CEC2020 and CEC2021 competition problem sets show that the KR-aDE has a strong competitive advantage.

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.

Optimized Two-Tier Caching with Hybrid Millimeter-Wave and Microwave Communications for 6G Networks

Data caching is a promising technique to alleviate the data traffic burden from the backhaul and minimize data access delay. However, the cache capacity constraint poses a significant challenge to obtaining content through the cache resource that degrades the caching performance. In this paper, we propose a novel two-tier caching mechanism for data caching on mobile user equipment (UE) and the small base station (SBS) level in ultra-dense 6G heterogeneous networks for reducing data access failure via cache resources. The two-tier caching enables users to retrieve their desired content from cache resources through device-to-device (D2D) communications from neighboring users or the serving SBS. The cache-enabled UE exploits millimeter-wave (mmWave)-based D2D communications, utilizing line-of-sight (LoS) links for high-speed data transmission to content-demanding mobile UE within a limited connection time. In the event of D2D communication failures, a dual-mode hybrid system, combining mmWave and microwave μWave technologies, is utilized to ensure effective data transmission between the SBS and UE to fulfill users’ data demands. In the proposed framework. the data transmission speed is optimized through mmWave signals in line-of-sight (LoS) conditions. In non-LoS scenarios, the system switches to μWave mode for obstacle-penetrating signal transmission. Subsequently, we propose a reinforcement learning (RL) approach to optimize cache decisions through the approximation of the Q action-value function. The proposed technique undergoes iterative learning, adapting to dynamic network conditions to enhance the content placement policy and minimize delay. Extensive simulations demonstrate the efficiency of our proposed approach in significantly reducing network delay compared with benchmark schemes.

Improving web-oriented information systems efficiency using Redis caching mechanisms

The responsiveness of a web application with minimum latency time and maximum web pages loading speed is determined by its overall performance. When dealing with a large number of users and amount of data, the performance of web applications is significantly affected by ways of data processing, storage and access. This paper identifies the significance of data caching process to speed up access to relational database. The study examines approaches to improve the performance of web applications through the joint use of MySQL relational database management system (DBMS) and Redis NoSQL DBMS. The practical part of the study presents a description of a web application built based on Java and Spring Boot framework. The paper proposes the implementation of the caching strategies that take into account the principles of aspect-oriented programming. Made experiments on performance testing of the developed web application with and without caching are presented. The presented results of the study allowed us to conclude that it is possible to improve the performance of web applications by the optimal use of caching strategies when performing database queries.

Decision Diagram-Based Branch-and-Bound with Caching for Dominance and Suboptimality Detection

The branch-and-bound algorithm based on decision diagrams is a framework for solving discrete optimization problems with a dynamic programming formulation. It works by compiling a series of bounded-width decision diagrams that can provide lower and upper bounds for any given subproblem. Eventually, every part of the search space will be either explored or pruned by the algorithm, thus proving optimality. This paper presents new ingredients to speed up the search by exploiting the structure of dynamic programming models. The key idea is to prevent the repeated expansion of nodes corresponding to the same dynamic programming states by querying expansion thresholds cached throughout the search. These thresholds are based on dominance relations between partial solutions previously found and on pruning inequalities given by rough upper bounds and local bounds — two additional filtering techniques recently introduced. Computational experiments show that the pruning brought by this caching mechanism allows for significantly reducing the number of nodes expanded by the algorithm. This results in more benchmark instances of difficult optimization problems being solved in less time while using narrower decision diagrams. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms–Discrete. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0340 ), as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0340 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

FASA-DRAM: Reducing DRAM Latency with Destructive Activation and Delayed Restoration

DRAM memory is a performance bottleneck for many applications, due to its high access latency. Previous work has mainly focused on data locality, introducing small-but-fast regions to cache frequently accessed data, thereby reducing the average latency. However, these locality-based designs have three challenges in modern multi-core systems: 1) Inter-application interference leads to random memory access traffic. 2) Fairness issues prevent the memory controller from over-prioritizing data locality. 3) Write-intensive applications have much lower locality and evict substantial dirty entries. With frequent data movement between the fast in-DRAM cache and slow regular arrays, the overhead induced by moving data may even offset the performance and energy benefits of in-DRAM caching. In this paper, we decouple the data movement process into two distinct phases. The first phase is Load-Reduced Destructive Activation (LRDA), which destructively promotes data into the in-DRAM cache. The second phase is Delayed Cycle-Stealing Restoration (DCSR), which restores the original data when DRAM bank is idle. LRDA decouples the most time-consuming restoration phase from activation, and DCSR hides the restoration latency through prevalent bank-level parallelism. We propose FASA-DRAM incorporating destructive activation and delayed restoration techniques to enable both in-DRAM caching and proactive latency-hiding mechanisms. Our evaluation shows that FASA-DRAM improves the average performance by 19.9% and reduces average DRAM energy consumption by 18.1% over DDR4 DRAM for four-core workloads, with less than 3.4% extra area overhead. Furthermore, FASA-DRAM outperforms state-of-the-art designs in both performance and energy efficiency.