Caching Scheme Research Articles

Design Scheme of High Concurrent and High Usable Cache based on TB Level Data Capacity

With the continuous development of the Internet in recent years, the Internet business has also changed greatly. In the past, when accessing the database, it usually takes the way of direct access, in order to resist the reading and writing traffic, often use a master and data fragmentation methods. However, the current traffic is more and more, and the amount of data is also accumulating. The data capacity of many e-commerce websites is getting bigger and bigger, even exceeding the TB level. If we still use the previous way, all the traffic using the database to bear, its stability will be greatly reduced, and it will also bring higher cost and lower efficiency, is extremely undesirable. Therefore, there is an urgent need to find technologies and architectures related to high concurrency and high availability. For the above reasons, this topic first understands and analyzes the previous application platform cache and Redis database cluster cache, and then builds a multi-level cache strategy based on Njinx local cache, which will be described from requirements analysis and architecture design.

On the design of cost minimization for D2D‐enabled wireless caching networks: A joint recommendation, caching, and routing perspective

AbstractCache‐enabled device‐to‐device (D2D) network has been deemed as an effective technique to offload the data traffic. However, the gain of the caching schemes is closely related to the homogeneity among users' preference distribution. To tackle this issue, recommendation is a promising proactive approach. It increases the request probability of recommended contents, reshaping users' contents demand patterns, and improving caching performance. Moreover, considering the heterogeneous network settings, i.e. content retrieval costs vary, the routing design becomes a non‐negligible factor on caching performance optimization. On these grounds, the average system cost of D2D‐enabled wireless caching networks with multiple BSs is first described. Then the routing strategies are designed together with caching and recommendation policies by minimizing the average cost of these networks. The optimization problem is proven as NP‐hard. To facilitate the analysis, the original problem is decoupled into two sub‐problems and solve them respectively. Afterwards, all the variables are optimized in an alternating manner until the convergence is achieved. The proposed algorithm's convergence performance and benefits over benchmark strategies in terms of total cost and cache hit ratio are supported by Monte‐Carlo simulation results.

A Unified Federated Deep Q Learning Caching Scheme for Scalable Collaborative Edge Networks

A Unified Federated Deep Q Learning Caching Scheme for Scalable Collaborative Edge Networks

How to Cache Important Contents for Multi-Modal Service in Dynamic Networks: A DRL-Based Caching Scheme

How to Cache Important Contents for Multi-Modal Service in Dynamic Networks: A DRL-Based Caching Scheme

The satellite network cache placement strategy based on content popularity and node collaboration.

Proposed is a Satellite network cache placement strategy (PNCCP) based on popularity and node cooperation to address the issue of significant delays in end-to-end connectivity due to instability among satellites. Initially, the strategy employs spectral clustering algorithm to partition the satellite network's topology, limiting the retrieval scope of content and reducing unnecessary propagation delays. Within each partition, a cache collaboration open mechanism among satellites is devised to share cache resources, utilizing the proximity of neighboring nodes to share popular content and cache space. Furthermore, the data naming network (NDN) cache model is enhanced and integrated with the open mechanism, with an update mechanism designed to address the invalidation caused by the dynamic nature of satellite networks. Finally, aiming to minimize users' average retrieval delay, the artificial bee colony algorithm is employed to solve the optimal cache placement problem. Simulation results demonstrate that compared to three contrasting cache strategies, the proposed strategy reduces user content retrieval delays, improves cache hit rates, and holds an advantage in reducing request hop counts.

MsDD: A novel NDN producer mobility support scheme based on multi-satellite data depot.

The Named Data Networking (NDN) is currently an important future network framework, and the mobility issue of producers within NDN is a primary challenge. However, in environments characterized by frequent producers mobility, traditional producer mobility support schemes still encounter issues such as excessive consumer delays and interest packet loss. With the development of The 6th generation communication technology (6G), integrating ground networks with satellites has emerged as a potential solution to address the aforementioned problems. In this paper, we propose an NDN producer mobility support scheme based on multi-satellite data depot, named MsDD. The proposed scheme proactively caches producer data into a data depot built on a low-earth orbit satellite constellation to minimize the impact of NDN producer mobility on network performance. We design data depot construction strategy, in-network caching strategy, and routing strategy based on forwarding hint to facilitate effective communication in satellite networks. Experimental results using ndnSIM demonstrate that compared with other existing schemes, MsDD can effectively shield the impact of producer mobility on consumer delay, delivery ratio, and signaling overhead, and MsDD has a clear advantage in terms of consumer delay and delivery ratio.

Improved AFSA-based energy-aware content caching strategy for UAV-assisted VEC

Improved AFSA-based energy-aware content caching strategy for UAV-assisted VEC

BERT4Cache: a bidirectional encoder representations for data prefetching in cache.

Cache plays a crucial role in improving system response time, alleviating server pressure, and achieving load balancing in various aspects of modern information systems. The data prefetch and cache replacement algorithms are significant factors influencing caching performance. Due to the inability to learn user interests and preferences accurately, existing rule-based and data mining caching algorithms fail to capture the unique features of the user access behavior sequence, resulting in low cache hit rates. In this article, we introduce BERT4Cache, an end-to-end bidirectional Transformer model with attention for data prefetch in cache. BERT4Cache enhances cache hit rates and ultimately improves cache performance by predicting the user's imminent future requested objects and prefetching them into the cache. In our thorough experiments, we show that BERT4Cache achieves superior results in hit rates and other metrics compared to generic reactive and advanced proactive caching strategies.

Comparative Analysis of Popularity Aware Caching Strategies in Wireless-Based NDN Based IoT Environment

Comparative Analysis of Popularity Aware Caching Strategies in Wireless-Based NDN Based IoT Environment

Federated Learning-Based Service Caching in Multi-Access Edge Computing System

Multi-access edge computing (MEC) brings computations closer to mobile users, thereby decreasing service latency and providing location-aware services. Nevertheless, given the constrained resources of the MEC server, it is crucial to provide a limited number of services that properly fulfill the demands of users. Several static service caching approaches have been proposed. However, the effectiveness of these strategies is constrained by the dynamic nature of the system states and user demand patterns. To mitigate this problem, several investigations have been conducted on dynamic service caching techniques that can be categorized as centralized and distributed. However, centralized approaches typically require gathering comprehensive data from the entire system. This increases the burden on resources and raises concerns regarding data security and privacy. By contrast, distributed strategies require the formulation of complicated optimization problems without leveraging the inherent characteristics of the data. This paper proposes a distributed service caching strategy based on federated learning (SCFL) that works efficiently in a distributed system with user mobility. An autoencoder model is utilized to extract features regarding the service request distribution of individual MEC servers. The global model is then generated using federated learning, which is utilized to make service-caching decisions. Extensive experiments are conducted to demonstrate that the performance of the proposed method is superior to that of other methods.

Learning-based Cooperative Cache Strategy Integrating Content Recommendation in IoV

Learning-based Cooperative Cache Strategy Integrating Content Recommendation in IoV

Enabling In-Network Caching in Traditional IP Networks: Selective Router Upgrades and Cooperative Cache Strategies

Enabling In-Network Caching in Traditional IP Networks: Selective Router Upgrades and Cooperative Cache Strategies

An efficient content replacement policy to retain essential content in information-centric networking based internet of things network

In the current scenario, TCP/IP is widely used in the Internet of Things (IoT) for disseminating data, addressing, and controlling traffic. However, the exponential growth of IoT devices has incurred the TCP/IP-based network from mismanagement due to heavy traffic, address space scarcity, and low efficiency. In the last few years, Information-centric networking (ICN) has emerged as an excellent choice to address the above issues. One of the important characteristics of ICN is In-network Caching, in which contents are cached at the intermediate nodes. Most in-network caching strategies use traditional replacement policies such as First in first out, least recently used, and least frequently used, ignoring the essential content properties, eventually evicting the vital contents. This work introduces a novel content replacement scheme that proactively records the number of requests and access time of the content in the Pending interest table (PIT) and considers essential content attributes, e.g., number of requests, remaining lifetime, and upcoming request waiting time, before replacement to retain vital contents in the cache. The simulations are performed on the ndnSIM framework of NS-3 to evaluate the performance of our proposed replacement scheme with other widely used benchmark replacement schemes and tested under six different caching strategies. The experimental finding indicates that, on average, the proposed replacement scheme shows 28.94% and 27.49% increase in cache hit ratio and server hit reduction ratio while 22.30% and 30.64% reduction in response delay and energy consumption compared to NDN-FIFO, NDN-LRU, and NDN-LRFU.

Named Multipath Depth-First Search: An SDN-based Routing Strategy for Efficient Failure Handling and Content Delivery in NDN

Information-centric networking (ICN) architectures, such as named data networking (NDN), have emerged as potential solutions for efficiently retrieving and delivering content. However, challenges remain regarding routing scalability, resilience, and caching efficiency. Software-defined networking (SDN) offers opportunities to optimize NDN implementations through centralized control and programmability. In this paper, we propose Named Multipath DFS, an SDN-based routing and caching scheme for NDN networks. NMDFS leverages a centralized controller to pre-compute multipath routes and implement coordinated caching. We evaluate NMDFS on an emulated topology testbed against default NDN and Named-data link state routing. The results demonstrate significant improvements with NMDFS, reducing overhead signalling costs by 94% and 78%, respectively, compared with other schemes. Round-trip latencies for content retrieval were reduced by up to 98%. The SDN controller’s global network view and control are leveraged to optimize content caching through packet loss-driven adaptation and eliminate redundant messaging, leading to substantial performance gains.

Joint AP‐user association and caching strategy for delivery delay minimization in cell‐free massive MIMO systems

AbstractEdge caching at access points (APs) is a promising approach to alleviate the fronthaul burden and reduce user‐perceived delay. However, the edge caching placement is still challenging considering the coupling between caching and AP‐user association, limited fronthaul capacity, and multi‐AP deployment in the cell‐free (CF) massive MIMO systems. To this end, the authors establish a framework for the joint problem of AP‐user association and caching to minimize the content delivery delay which considers both cooperation delivery delay and radio access delay. It is an integer nonlinear programming problem and NP‐hard. The optimization problem is first decomposed into an AP‐user association sub‐problem and a caching placement sub‐problem to address this problem. A two‐stage matching algorithm is further proposed to achieve AP‐user association and a modified genetic algorithm to determine caching placement. A computationally efficient iterative algorithm is developed to solve the joint optimization problem. Finally, the global convergence and computational complexity of the proposed strategy are analyzed theoretically. Simulation results reveal that the proposed strategy can achieve better delivery delay performance than benchmark schemes.

User preference-aware content caching strategy for video delivery in cache-enabled IoT networks

The escalating growth of content-dependent services and applications within the Internet of Things (IoT) platform has led to a surge in traffic, necessitating real-time data processing. Content caching has emerged as an effective solution to counteract this traffic upswing. Caching not only improves network efficiency but also enhances user service quality. Critical to the development of an optimal caching algorithm is the accurate prediction of future content popularity. This prediction hinges on the ability to anticipate users' content preferences, which is a pivotal method for assessing content popularity. In this study, we introduce a novel caching strategy termed User Preference-aware content Caching Strategy (UPCS) tailored for an IoT platform, where users access multimedia services offered by remote Content Providers (CPs). The UPCS encompasses three key algorithms: a content popularity prediction algorithm that utilizes Variational Autoencoders (VAE) to forecast users' future content preferences based on their prior requests, an online algorithm for dynamic cached content replacement, and a cooperative caching algorithm to augment caching efficiency. The proposed content caching strategy outperforms alternative methods, exhibiting superior cache hit rates and reduced Content Retrieval Delays (CRD).

Resource Allocation in UAV-Enabled NOMA Networks for Enhanced Six-G Communications Systems

Enhancing energy efficiency, content distribution, latency, and transmission speeds are vital components of communication systems. Multiple access methods hold great promise for boosting these performance indicators. This manuscript evaluates the effectiveness of Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) systems within a single cell, where users are scattered randomly and rely on relays for dependability. This paper presents a model for improving energy efficiency, content distribution, latency, and transmission speeds in communication systems using NOMA and OMA systems within a single cell. Additionally, this paper also proposes a caching strategy using unmanned aerial vehicles (UAVs) as aerial base stations for ground users. These UAVs distribute cached content to minimize the overall latency of content demands from ground users while modifying their positions. We carried out simulations using various cache capacities and user counts linked to their respective UAVs. Furthermore, we evaluated OMA and NOMA in terms of the achievable rate and energy efficiency. The proposed model has achieved noteworthy enhancement across various scenarios including different sum rates, numbers of mobility users, diverse cache sizes, and amounts of power allocation.

Labeled graph partitioning scheme for distributed edge caching

Distributed edge caching could address latency and congestion problems in large-scale data access effectively, improving system throughput and performance. However, the lack of specialized edge caching solutions for graph data resulted in low cache hit rates and high data access latency. This limitation stemmed from existing graph partitioning schemes that overlooked the specific requirements of edge caching, such as communication overhead between edge servers, data query frequency, and query patterns. To overcome these challenges, we proposed LGPE, a labeled graph partitioning scheme for distributed edge caching based on frequent query patterns. LGPE generated frequent query patterns according to the user’s historical query subgraphs and then performed labeled graph partitioning based on the frequent pattern, ensuring that the labeled graph divided into edge servers had a high hit rate for frequent user queries while satisfying a minimum edge cut. Evaluation on real datasets from various application domains demonstrated that LGPE achieved approximately 40% higher cache hit rates compared to benchmarks like METIS and SCOTCH, and it also performed well in terms of edge cuts in special graph cases.

Exploratory Search Prompt Generation using n-Degree Connection in Knowledge Graph

Search engines play a vital role in retrieving in- formation, but users often struggle to express their precise information needs, resulting in less-than-optimal search results. Therefore, enhancing search query refinement is crucial to elevate the accuracy and relevance of search outcomes. One particular challenge that existing search engines face is presenting refined results for queries containing two or more unrelated entities.
 This paper presents a novel approach for efficient search prompt generation by leveraging connected nodes and attributes in the knowledge graph. We propose a comprehensive exploration technique that explores the n-degrees connections and their at- tributes to generate all possible imaginative prompts. We realized that n-degrees connection exploration is an expensive task, hence we conducted experiments to determine the effectiveness of 2- degree exploration prompts in covering all the user-asked queries within the provided dataset.
 In testing with approximately 2000 queries on a related knowl- edge graph dataset, we found out that our proposed methodol- ogy significantly improved query coverage. At n=1 depth, the coverage increased from 58% without the methodology to 84% with it. At n=2 depth, the coverage rose from 92% without the methodology to nearly 99% with it. Additionally, due to our question caching strategy at n=2, we observed faster response times for all questions.

Traffic-Aware Optimization of Task Offloading and Content Caching in the Internet of Vehicles

Emerging in-vehicle applications seek to improve travel experiences, but the rising number of vehicles results in more computational tasks and redundant content requests, leading to resource waste. Efficient compute offloading and content caching strategies are crucial for the Internet of Vehicles (IoV) to optimize performance in time latency and energy consumption. This paper proposes a joint task offloading and content caching optimization method based on forecasting traffic streams, called TOCC. First, temporal and spatial correlations are extracted from the preprocessed dataset using the Forecasting Open Source Tool (FOST) and integrated to predict the traffic stream to obtain the number of tasks in the region at the next moment. To obtain a suitable joint optimization strategy for task offloading and content caching, the multi-objective problem of minimizing delay and energy consumption is decomposed into multiple single-objective problems using an improved Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) via the Tchebycheff weight aggregation method, and a set of Pareto-optimal solutions is obtained. Finally, the experimental results verify the effectiveness of the TOCC strategy. Compared with other methods, its latency is up to 29% higher and its energy consumption is up to 83% higher.