Caching Technology Research Articles

Design of real-time & historical database for photovoltaic power plant

Abstract The real-time & historical database of photovoltaic power plant monitoring system is used to store and manage the real-time and historical data, including power plant parameters, environmental data, and photovoltaic unit data and so on. Analyzing of this data is helpful for monitoring the status of the plant equipment, performing maintenance, and enhancing the operational efficiency of the power plant. The data models and database function of the database is designed. The data models include tags, data processing, and data storage models. Database function include the modules of the collection, storage, compression, and index query. The data collection module utilizes dynamic link libraries to access database data and configures parameters through a comprehensive data collection framework. The storage strategies involve in a combination of in-memory databases and disk-based historical databases, incorporating secondary caching technology and cloud server storage techniques. The data compression module is achieved through secondary compression methods. The index query module encompass time index files and the secondary index files to improve query speed and real-time system performance. The designed elements collectively establish an efficient monitoring system database.

An FPGA-Based signal acquisition system for 3D holographic azimuthal electromagnetic resistivity logging instruments

Abstract A signal acquisition system of a 3D holographic azimuthal electromagnetic resistivity logging instrument while drilling is designed based on the Field Programmable Gate Array (FPGA). The azimuth electromagnetic wave resistivity logging instrument is mainly composed of transmitting coils, receiving coils, and control circuits. The signal acquisition system mainly includes the receiving coil and corresponding signal acquisition and processing circuit. First of all, the whole structure of the electromagnetic wave resistivity instrument system while drilling is briefly introduced, and then each function module of the acquisition board is elaborated in detail, and the hardware realization of the signal acquisition module and the corresponding FPGA program design flow chart are given. The acquisition system makes full use of the structured design of the module and First input First output (FIFO) cache technology and can complete the functions of signal acquisition, data transmission, and control on the acquisition board in real-time by using Serial Communication Interface (SCI) information transmission interface. The simulation results show that the system can realize the function of data acquisition and processing well.

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

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

Edge Caching Data Distribution Strategy with Minimum Energy Consumption.

In the context of the rapid development of the Internet of Vehicles, virtual reality, automatic driving and the industrial Internet, the terminal devices in the network show explosive growth. As a result, more and more information is generated from the edge of the network, which makes the data throughput increase dramatically in the mobile communication network. As the key technology of the fifth-generation mobile communication network, mobile edge caching technology which caches popular data to the edge server deployed at the edge of the network avoids the data transmission delay of the backhaul link and the occurrence of network congestion. With the growing scale of the network, distributing hot data from cloud servers to edge servers will generate huge energy consumption. To realize the green and sustainable development of the communication industry and reduce the energy consumption of distribution of data that needs to be cached in edge servers, we make the first attempt to propose and solve the problem of edge caching data distribution with minimum energy consumption (ECDDMEC) in this paper. First, we model and formulate the problem as a constrained optimization problem and then prove its NP-hardness. Subsequently, we design a greedy algorithm with computational complexity of O(n2) to solve the problem approximately. Experimental results show that compared with the distribution strategy of each edge server directly requesting data from the cloud server, the strategy obtained by the algorithm can significantly reduce the energy consumption of data distribution.

Physical Layer Security in IRS-Assisted Cache-Enabled Satellite Communication Networks

This paper presents a comprehensive analysis of the physical layer security performance of a cache-enabled satellite communication network that incorporates intelligent reflecting surfaces (IRS) in the presence of a passive eavesdropper. In the proposed system, content caches are deployed at both the ground station and the satellite, which can improve system performance by reducing latency and transmission overhead. Moreover, the use of IRS provides an additional layer of security by enabling the manipulation of the reflected signals to impede eavesdropping. Practical channel models are used to derive connection probability and secrecy probability for both the ground station-IRS-user and the satellite-IRS-user links. The obtained results are then used to evaluate the system’s secure transmission probability, which is maximized subject to the caching probabilities and transmission rate constraints. The paper presents numerical results to demonstrate the accuracy of the analysis and the effectiveness of deploying IRS and caching to support secure content delivery. The findings provide valuable insights into the potential benefits of utilizing IRS and caching technologies in satellite communication networks for improved physical layer security.

Computing Offloading Based on TD3 Algorithm in Cache-Assisted Vehicular NOMA-MEC Networks.

In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA-MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm.

Joint Optimization of Caching Placement and Power Allocation in Virtualized Satellite-Terrestrial Network

With the rapid development of mobile services and applications, the transmitting of massive data makes low-cost communication a challenge. Edge-based wireless communication technology is developed to be a promising approach to satisfy the communication requirements. Edge caching technology is one of effective methods to reduce the overhead of communication system and the pressure of backhauls. In this paper, the joint optimization problem of caching placement and power allocation in virtualized low earth orbit (LEO) satellite-terrestrial networks is proposed, which is based on cooperative caching, by considering cache size limits and power constraints. The optimization problem is solved using an algorithm inspired by the courtship movements and random flights of mayflies. Simulation results show the effectiveness of the proposed scheme in improving system performance and reducing power consumption.

Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing

With the convergence of the Internet of Things, 5G, and artificial intelligence, limited network bandwidth and bursts of incoming service requests seem to be the most important factors affecting user experience. Therefore, caching technology was introduced. In this paper, a caching placement optimization strategy based on comprehensive utility (CPOSCU) in edge computing is proposed. Firstly, the strategy involves quantifying the placement factors of data blocks, which include the popularity of data blocks, the remaining validity ratio of data blocks, and the substitution rate of servers. By analyzing the characteristics of cache objects and servers, these placement factors are modeled to determine the cache value of data blocks. Then, the optimization problem for cache placement is quantified comprehensively based on the cache value of data blocks, data block retrieval costs, data block placement costs, and replacement costs. Finally, to break out of the partial optimal solution for cache placement, a penalty strategy is introduced, and an improved tabu search algorithm is used to find the best edge server placement for cached objects. Experimental results demonstrate that the proposed caching strategy enhances the cache service rate, reduces user request latency and system overhead, and enhances the user experience.

Re-Cache: Mitigating cache contention by exploiting locality characteristics with reconfigurable memory hierarchy for GPGPUs

Modern GPGPUs have employed multi-threading to hide the long off-chip memory access latency caused by frequent cache misses. However, the limited cache capacity shared by thousands of concurrently running warps will introduce serious cache contention problem, which results in high cache miss rates to hurt the overall GPGPU performance. To address this problem, this paper proposes a novel cache technology (Re-Cache) by exploiting locality characteristics and reconfiguring the memory hierarchy including the unused shared memory space and the idle registers of pending warps without extra storage overhead for GPGPUs. To further improve the utilization efficiency of the Re-Cache space, this paper proposes a dynamic and reconfigurable cache organization for Re-Cache that explores the spatial and temporal locality differences and changes among the running applications. Re-Cache dynamically reconfigures the cache organization as the adaptive prefetch region and victim region according to the differences and changes of the spatial and temporal locality by catching the locality characteristics for each load instruction. For the high-locality load instructions, the prefetch region stores the subsequent data blocks to exploit the spatial locality of the running threads among all streaming multiprocessors and warps, while the victim region collects the evicted data blocks from the upper memory hierarchy to exploit the temporal locality. Experimental results demonstrate that the proposed Re-Cache reduces up to 34.82% and 31.62% cache misses, while improving about 47.17% and 14.96% IPC performance compared with the state-of-the-art designs for cache-sensitive kernels and benchmarks, respectively.

Evaluation model of cultural heritage tourist attractions based on network virtual resource sharing and real-time information processing

With the continuous development of modern society, people’s demand for the network is also rising. Because of its powerful function, wide coverage, and other advantages, people in modern society have higher and higher utilization rate of the network, and many things are realized with the help of the network. At the same time, some new business based on the network is emerging. The rapid development of network technology makes many people rush for it. This paper mainly studies how to improve the cache technology, reasonable, and effective allocation of resources, so that people can get a better experience. This paper studies the suitability evaluation of tourism development of regional cultural heritage resources by using Delphi method and AHP analytic hierarchy process and infiltrates the evaluation items into various levels, such as natural environment conditions, social and economic conditions, resource combination levels, and so on. Through the analysis and comparison of different geographic information, we can clearly understand whether the region is suitable for tourism and the spatial differences of some regions. To a certain extent, it lays the foundation for the future development of cultural heritage tourist attractions, and the spatial analysis based on GIS, which is consistent with the tourism suitability evaluation index in the field of regional cultural heritage. When comparing the data with different measurement levels, we can directly determine the characteristics of the spatial differences of different cultural heritage resources, which depends on the value of resources and location conditions of a specific region.

A Genetic Algorithm-based Proactive Caching Policy in Edge Networks

The incredible growth in mobile traffic lays down a challenge to network design. Therefore, proactive caching technology has been proposed as an effective method to reduce network congestion and improve the quality of service. In this paper, we focus on the caching position problem in edge networks and use a mixed integer linear programming (MILP) model to provide the optimal selection. Due to the curse of dimensionality, a genetic algorithm-based caching policy is given to make a balance between performance and time complexity. Numerical investigations reveal that the proposed approach can reduce the total cost by around 20% compared with other widely-used caching methods.

AoI-Aware Optimization of Service Caching-Assisted Offloading and Resource Allocation in Edge Cellular Networks

The rapid development of the Internet of Things (IoT) has led to computational offloading at the edge; this is a promising paradigm for achieving intelligence everywhere. As offloading can lead to more traffic in cellular networks, cache technology is used to alleviate the channel burden. For example, a deep neural network (DNN)-based inference task requires a computation service that involves running libraries and parameters. Thus, caching the service package is necessary for repeatedly running DNN-based inference tasks. On the other hand, as the DNN parameters are usually trained in distribution, IoT devices need to fetch up-to-date parameters for inference task execution. In this work, we consider the joint optimization of computation offloading, service caching, and the AoI metric. We formulate a problem to minimize the weighted sum of the average completion delay, energy consumption, and allocated bandwidth. Then, we propose the AoI-aware service caching-assisted offloading framework (ASCO) to solve it, which consists of the method of Lagrange multipliers with the KKT condition-based offloading module (LMKO), the Lyapunov optimization-based learning and update control module (LLUC), and the Kuhn-Munkres (KM) algorithm-based channel-division fetching module (KCDF). The simulation results demonstrate that our ASCO framework achieves superior performance in regard to time overhead, energy consumption, and allocated bandwidth. It is verified that our ASCO framework not only benefits the individual task but also the global bandwidth allocation.

Caching popularity-unknown contents for massive communication channels via deep Q-learning

In order to reduce the backhaul link pressure of wireless networks, edge caching technology has been regarded as a promising solution. However, with massive and dynamical communication connections, it is challenging to provide analytical caching solution to achieve the best performance, particularly when the requested contents are changing and their popularities are unknown. In this paper, we propose a deep Q-learning (DQN) method to address the issue of caching placement. Considering a content caching network containing multiple cooperating SBSs with unknown content popularity, we need to determine which content to cache and where to cache. Therefore, the learning network has to be designed for dual aims, one of which is to estimate the content popularities while the other is to assign contents to the proper channels. An elaborate DQN is proposed to make decisions to cache contents with limited storage space of base-station by considering channel conditions. Specifically, the content requests of users are first collected as one of the training samples of the learning network. Second, the channel state information for the massive links are estimated as the other training samples. Then, we train the network based on the proposed method thereby improving spectral efficiency of the entire system and reducing bit-error rate. Our major contribution is to contrive a caching strategy for enhanced performance in massive connection communications without knowing the content popularity. Numerical studies are performed to show that the proposed method acquires apparent performance gain over random caching in terms of average spectral efficiency and bit-error rate of the network.

Optimisation of Automation Devices Based on IOT Big Data Algorithms

With the continuous development of communication, sensor and caching technologies, IoT technology has gained rapid opportunities for growth and a huge digital revolution has taken place at all levels of society. Blockchain technology has emerged rapidly in recent years and can be seen as a distributed, time-based ledger of distributed data. It utilises technologies such as consensus protocols, modern cryptography, P2P and smart contracts, which can provide a secure, stable, transparent, auditable and low-consumption system architecture that has a traceable, stable and efficient security management capability, and can provide a new solution to identity security for the Internet of Things. This paper absorbs the existing blockchain-based access management approach and improves it, proposing a new private chain-based security management approach that solves the problems of access dynamics, low intelligence and high overhead in the traditional access management approach. This paper designs a new control management architecture, the Novel-Capability-Based Access Control (NCBAC), which draws on the microkernel and microservice ideas of operating systems. Firstly, this paper abstracts the concept of management node to solve the problem of weak computing power and low storage performance of IoT devices that cannot meet the difficulty of direct communication between IoT devices and blockchain, and at the same time can reduce the network operation overhead; secondly, it constructs a multi-level smart contract system and designs three kinds of smart contracts, AC, ACC and AMC, to build a trusted and reliable access control entity model; finally, it adopts radial basis based (RBF) neural network and combines with access policy to dynamically generate the credit degree threshold of access nodes to build an intelligent access authority management model for IoT mass sensors. The model proposed in this paper designs a token mechanism based on the fact that IoT systems have multiple requests within a short period of time in a real production environment, which, according to experimental results, improves the performance of the system to a certain extent.

QoE-Aware Efficient Content Distribution Scheme For Satellite-Terrestrial Networks

The satellite-terrestrial networks (STN) utilize the spacious coverage and low transmission latency of the Low Earth Orbit (LEO) constellation to transfer requested content for subscribers especially in remote areas. With the development of storage and computing capacity of satellite onboard equipment, it is considered promising to leverage in-network caching technology on STN to improve content distribution efficiency. However, traditional caching and distribution schemes are not suitable in STN, considering dynamic satellite propagation links and time-varying topology. More specifically, the unevenness of user distribution heightens difficulties for assurance of user quality of experience. To address these problems, we first propose a density-based network division algorithm. The STN is divided into a series of blocks with different sizes to amortize the data delivery costs. To deploy the caching satellites, we analyze the link connectivity and propose an approximate minimum coverage vertex set algorithm. Then, a novel cache node selection algorithm is designed for optimal subscriber matching. On the basis of time-varying network model, the STN cache content updating mechanism is derived to enable a stable and sustainable quality of user experience. The simulation results demonstrate that the proposed user-oriented STN content distribution scheme can obviously reduce the average propagation delay and network load under different network conditions and has better stability and self-adaptability under continuous time variation.

Content Collaborative Caching Strategy in the Edge Maintenance of Communication Network: A Joint Download Delay and Energy Consumption Method

With the development of Big Data technology and Internet, the surge of data in the network will cause network congestion and untimely task processing. Additionally, caching content in the core network may cause redundant access of content and backhaul bottlenecks. Due to the increasing requirements of users for task processing efficiency, the centralized maintenance system based on traditional cloud computing cannot meet the current computing requirements. In view of these problems, we propose a content collaborative caching mechanism based on joint decision of download delay and energy consumption. By integrating network coding and content caching technology, the work content maintained in the communication network is deployed near the edge of the network in the form of coding to reduce the redundant transmission of content and acquisition time of content. This article establishes a user QoE satisfaction model, which consists of two indexes that measure time delay and energy consumption. This article proposes a <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> -hybrid Q-learning algorithm to optimize the placement of cache files, and made the cache action selection based on the combination of improved heuristic greedy algorithm and simulated annealing algorithm. The experimental results show that the proposed cache strategy can reduce the delay of users downloading content and the energy consumption of content cache, so as to improve the quality of field maintenance work in communication network.

Multisignal Cooperative Processing Method for Internet of Vehicles Based on Data-Driven Edge Computing Method

With the rapid development of Internet of Vehicles applications, more and more data are generated. How to effectively distribute content in the Internet of Vehicles to meet the service quality requirements of users has become one of the industry pain points in the field of smart cars and autonomous driving. In order to solve the shortage of local computing resources of vehicles, a vehicle edge network is proposed, which uses data-driven edge computing to offload vehicle tasks to a mobile edge computing server to reduce overall network energy consumption and meet task latency requirements. In addition, in order to reduce the end-to-end delay, caching technology is adopted at the network edge, which can reduce the content transmission delay. This paper focuses on the problem of computing offloading in the data-driven edge computing method in the context of the Internet of Vehicles. Simulation experiments have proved its superiority compared with the traditional offloading method, and the delay and energy consumption are better than the traditional method. First, the basic concepts of the Internet of Vehicles and MEC are introduced; second, the TOAI algorithm flow chart and the computing tasks of the offloading work of the Internet of Vehicles are introduced; then, based on MEC and partial offloading, the task offloading problem is modeled and solved; the problem of unloading collaborative content under networking is solved, and the simulation results are analyzed and verified. The simulation experiment not only shows that the proposed algorithm optimizes the efficiency of the task under the average unit but also shows the effectiveness of this method, which lays a foundation for the engineering implementation of the algorithm. The experimental results show that the average task completion rate is increased by 0.58%, and the average unit task energy consumption is increased by 0.32%, which improves the practicability of the system.

A hybrid caching strategy for information-centric satellite networks based on node classification and popular content awareness

Low Earth Orbit (LEO) satellite networks are inexpensive to deploy and have wide coverage, and are widely available for differentiated content distribution services. With the development of on-board storage and computing capabilities, in-network caching technology in information centric networking (ICN) has proven to be an effective way to increase the throughput and content distribution efficiency of satellite networks. However, traditional caching and distribution schemes, which do not take into account the high-speed motion of satellite nodes and dynamic changes in topology, are not applicable to satellite networks. To address these issues, a hybrid caching strategy for satellite networks based on node classification and popular content awareness (NCPCA) is proposed. Firstly, the time slot partitioning method based on interlayer similarity is proposed to transform the dynamically changing process into a set of time slots with stable topology. Next, the satellite nodes are dynamically divided into two categories by fully considering the changes in connection relationships and interaction order during the spatial–temporal evolution of the satellite nodes. Nodes with satellite topological and functional characteristics are screened out as core nodes with TOPSIS algorithm, and the remaining nodes are regarded as edge nodes. Finally, a probabilistic caching scheme based on content popularity is adopted with core nodes as caching nodes to ensure caching performance and promote the diversity of cached content. Simulation results show that this strategy can effectively improve cache hit rate, reduce user request delay and content fetching hops, and promote the stable operation of satellite networks compared with other caching strategies.

Throughput Maximization for Backscatter- and Cache-Assisted Wireless Powered UAV Technology

This paper investigates a wireless powered unmanned aerial vehicle (UAV) communication network with backscatter and caching technologies. Specifically, we assume a self-energized UAV with a cache memory is deployed as a flying backscatter device (BD), term the UAV-enabled BD (UB), to relay the source’s signals to the destination. Whereas the source S can act as a wireless charging station or a base station to supply power or transmit information to the UB using the dynamic time splitting (DTS) method. The UAV utilizes its harvested energy for backscattering (i.e., passive communication) and transmit information (i.e., active communication) to the destination. In this context, we aim to maximize the total throughput by jointly optimizing the DTS ratio and the UB’s trajectory with caching capability at the UB. The formulation is troublesome to solve since it is a non-convex problem. To find solutions, we decompose the original problem into two sub-problems, whereas we first optimize the DTS ratio for a given UB’s trajectory and the UB’s trajectory optimization for a given DTS ratio. By using the KKT conditions, a closed-form expression for the optimal value of the DTS ratio is obtained, greatly reducing the computation time. Moreover, the solution of the second sub-problem can be acquired by adopting the successive convex approximation (SCA) technique. Consequently, an efficient alternating algorithm is proposed by leveraging the block coordinate descent (BCD) method. To show the advantages of the proposed BCD-based algorithm, we also provide the solution of the original problem applying the inner approximation (IA) method. Finally, the intensive numerical results demonstrate that our proposed schemes achieve significant throughput gain in comparison to the benchmark schemes.

A Study of an IT-Assisted Higher Education Model Based on Distributed Hardware-Assisted Tracking Intervention.

This paper presents an in-depth study and analysis of the model of higher education using distributed hardware tracking intervention of information technology. The MEC-based dynamic adaptive video stream caching technology model is proposed. The model dynamically adjusts the bit rate by referring to the broadband estimation and cache occupancy data to ensure users have a smooth experience effect. Simulation results show that the model has fewer transcoding times and generates lower latency than the traditional model, which is suitable for dual-teacher classroom scenarios and further improves the quality of the user's video viewing experience. The model uses an edge cloud collaborative architecture to migrate the rendering technology to an edge server closer to the user side, enabling real-time interaction, computation, and rendering, reducing the time of data transmission as well as computation time. According to the blended learning-based adaptive intervention model, three rounds of teaching practice are conducted to validate the effectiveness of the intervention model in terms of both student process performance and outcome performance, thereby improving learning adaptability and improving learning effect. Teachers' teaching has a significant impact on learning motivation (β = 0.311, p < 0.01), which in turn affects learning adaptability. Teachers use scientific teaching methods to stimulate students' learning motivation, mobilize enthusiasm, and improve learning adaptability. Under the communication topology of the system as a directed graph, a multi-intelligent system dynamic model with grouping is established; i.e., the intragroup intelligence has the same dynamics but is different between groups, and all system dynamics are unknown. The proposed novel policy iterative algorithm is used to learn the optimal control protocol and achieve optimal consistency control. The effectiveness of the algorithm is demonstrated by the simulation experimental results. The simulation results show that the model has lower latency and energy consumption compared to the cloud rendering model, which is suitable for the safety education classroom scenario and solves the outstanding problems of network connection rate and cloud service latency.