Power Information System Database Cache Model Based on Deep Machine Learning

At present,the database cache model of power information system has problems such as slow running speed and low database hit rate.To this end,this paper proposes a database cache model for power information systems based on deep machine learning.The caching model includes program caching,Structured Query Language(SQL)preprocessing,and core caching modules.Among them,the method to improve the efficiency of the statement is to adjust operations such as multi-table joins and replacement keywords in the SQL optimizer.Build predictive models using boosted regression trees in the core caching module.Generate a series of regression tree models using machine learning algorithms.Analyze the resource occupancy rate in the power information system to dynamically adjust the voting selection of the regression tree.At the same time,the voting threshold of the prediction model is dynamically adjusted.By analogy,the cache model is re-initialized.The experimental results show that the model has a good cache hit rate and cache efficiency,and can improve the data cache performance of the power information system.It has a high hit rate and short delay time,and always maintains a good hit rate even under different computer memory;at the same time,it only occupies less space and less CPU during actual operation,which is beneficial to power The information system operates efficiently and quickly.

Efficient cache replacement framework based on access hotness for spacecraft processors

A notable portion of cachelines in real-world workloads exhibits inner non-uniform access behaviors.However,modern cache management rarely considers this fine-grained feature,which impacts the effective cache capacity of contemporary high-performance spacecraft processors.To harness these non-uniform access behaviors,an efficient cache replacement framework featuring an auxiliary cache specifically designed to retain evicted hot data was proposed.This framework reconstructs the cache replacement policy,facilitating data migration between the main cache and the auxiliary cache.Unlike traditional cacheline-granularity policies,the approach excels at identifying and evicting infrequently used data,thereby optimizing cache utilization.The evaluation shows impressive performance improvement,especially on workloads with irregular access patterns.Benefiting from fine granularity,the proposal achieves superior storage efficiency compared with commonly used cache management schemes,providing a potential optimization opportunity for modern resource-constrained processors,such as spacecraft processors.Furthermore,the framework complements existing modern cache replacement policies and can be seamlessly integrated with minimal modifications,enhancing their overall efficacy.

一种带Cache加速的HyperRAM控制器设计与验证

针对目前可穿戴设备上对存储设备性能要求高、体积小、功耗低等问题,在FPGA上实现了一款可拓展的高性能HyperRAM控制器,并引入Cache缓存加速设计,以提高对频繁访问数据的命中率和优化存储器访问模式,实现更高速的数据传输和优化的系统性能。运用UVM验证方法学和FPGA进行验证,结果表明,带有Cache缓存的HyperRAM控制器相较于普通HyperRAM,在读写连续地址时性能提高61%,并具有较好的可靠性与有效性,可为嵌入式系统提供高效、灵活的存储器解决方案。

MemSC： A Scan-Resistant and Compact Cache Replacement Framework for Memory-Based Key-Value Cache Systems

Memory-based key-value cache systems, such as Memcached and Redis, have become indispensable components of data center infrastructures and have been used to cache performance-critical data to avoid expensive back-end database accesses. As the memory is usually not large enough to hold all the items, cache replacement must be performed to evict some cached items to make room for the newly coming items when there is no free space. Many real-world workloads target small items and have frequent bursts of scans （a scan is a sequence of one-time access requests）. The commonly used LRU policy does not work well under such workloads since LRU needs a large amount of metadata and tends to discard hot items with scans. Small decreases in hit ratio can result in large end-to-end losses in these systems. This paper presents MemSC, which is a scan-resistant and compact cache replacement framework for Memcached. MemSC assigns a multi-granularity reference flag for each item, which requires only a few bits （two bits are enough for general use） per item to support scanresistant cache replacement policies. To evaluate MemSC, we implement three representative cache replacement policies （MemSC-HM, MemSC-LH, and MemSC-LF） on MemSC and test them using various workloads. The experimental results show that MemSC outperforms prior techniques. Compared with the optimized LRU policy in Memcached, MemSC-LH reduces the cache miss ratio and the memory usage of the resulting system by up to 23% and 14% respectively.

基于SystemVerilog语言的像素cache验证平台的实现

以SystemVerilog为基础,对自主研发的GPU"萤火虫2号"中像素cache部分搭建可重用的验证平台。该平台可以自动完成整个验证过程,并将验证结果打印到Linux终端和文件当中,方便程序员检查验证结果。实验结果表明,该验证平台对像素cache的功能验证覆盖率可以达到100%,并且具有良好的可重用性,能够全面、正确地完成RTL级功能验证,有效地提高了验证的效率和质量。

Joint Optimization of Satisfaction Index and Spectrum Efficiency with Cache Restricted for Resource Allocation in Multi-Beam Satellite Systems

Dynamic resource allocation(DRA) is a key technology to improve system performances in GEO multi-beam satellite systems. And, since the cache resource on the satellite is very valuable and limited, DRA problem under restricted cache resources is also an important issue to be studied. This paper mainly investigates the DRA problem of carrier resources under certain cache constraints. What's more, with the aim to satisfy all users' traffic demands as more as possible, and to maximize the utilization of the bandwidth, we formulate a multi-objective optimization problem(MOP) where the satisfaction index and the spectrum efficiency are jointly optimized. A modified strategy SA-NSGAII which combines simulated annealing(SA) and non-dominated sorted genetic algorithm-II(NSGAII) is proposed to approximate the Pareto solution to this MOP problem. Simulation results show the effectiveness of the proposed algorithm in terms of satisfaction index, spectrum efficiency, occupied cache, and etc.

Cache Coherency Design in Pentium Ⅲ SMP System

This paper analyzes cache coherency mechanism from the view of system. It firstly discusses caehe-memory hierarchy of Pentium Ⅲ SMP system, including memory area distribution, cache attributes control and bus transaction. Secondly it analyzes hardware snoopy mechanism of P6 bus and MESI state transitions adopted by Pentium Ⅲ. Based on these, it focuses on how muhiprocessors and the P6 bus cooperate to ensure cache coherency of the whole system, and gives the key of cache coherency design.

A Novel Architecture of Metadata Management System Based on Intelligent Cache

This paper introduces a novel architecture of metadata management system based on intelligent cache called Metadata Intelligent Cache Controller （MICC）. By using an intelligent cache to control the metadata system, MICC can deal with different scenarios such as splitting and merging of queries into sub-queries for available metadata sets in local, in order to reduce access time of remote queries. Application can find results patially from local cache and the remaining portion of the metadata that can be fetched from remote locations. Using the existing metadata, it can not only enhance the fault tolerance and load balancing of system effectively, but also improve the efficiency of access while ensuring the access quality.

一种面向多核独享L2 Cache的缓存一致性设计实现

近年来,独享L2 Cache是实现高性能多核处理器的主流架构,但是该架构在维护Cache一致性上需要多次访存,增加了系统开销.为此,本文基于PowerPC指令架构实现了一种基于私有Cache状态机与片上总线监测机制相融合的多核缓存一致性设计,使处理器之间可以直接通过干涉接口交互数据.采用硬件描述语言Verilog HDL设计并实现了该多核缓存结构,仿真结果表明,在实现缓存一致性时,这种具有干涉路径的结构相比于传统访存方法最大能够节省87.06%的时间开销,有效地提升了多核处理器性能.最后经过实物芯片在板级上的测试,与仿真结果保持一致.

一种面向二维三维卷积的GPGPU cache旁路系统

通用图形处理器作为卷积神经网络的核心加速平台,其处理二维、三维卷积的性能,决定着神经网络在实时目标识别检测领域的有效应用。然而,受其固有cache系统功能的限制,当前通用图形处理器架构无法实现二维、三维卷积的高效加速。针对此问题,首先提出一种L1Dcache动态旁路设计方案。该方案定义了一组能够动态反映指令访问cache特征的数据结构,并基于此数据结构定义访存特征记录表,以记录不同访存指令在请求cache时的执行状态。其次,采用优先线程块的warp调度策略来加速访存状态的采样。最后根据访存状态得出不同PC值下访存请求对L1Dcache的旁路的判定,并动态完成部分低局域性数据请求对L1Dcache的旁路。由此将L1Dcache空间保留给高局域性的数据并降低二维、三维卷积执行时的访存阻塞周期,进而提升了二维、三维卷积在通用图形处理器上执行时的访存效率。实验结果表明,相比原架构,在面向二维、三维卷积时分别带来了约2.16%与19.79%的性能提升,体现了设计方案的有效性与实用性。

基于SystemC的Cache一致性协议描述与验证

FLSAH SCI协议作为Cache一致性协议的一种,具有灵活性、高效性和适应性强等特点,是目前最具发展潜力的Cache一致性协议之一。该协议在继承标准SCI协议低存储开销、维护精确共享信息等优点的同时对标准SCI协议进行优化,得到更为高效、灵活的FLASH SCI协议。本文用SystemC高层次语言对FLASH SCI协议进行建模与描述,并验证其正确性。模拟结果证明了FLASH SCI协议的正确性以及其在存储开销和执行效率上的优越性。

GFCache: A Greedy Failure Cache Considering Failure Recency and Failure Frequency for an Erasure-Coded Storage System

In the big data era,data unavailability,either temporary or permanent,becomes a normal occurrence on a daily basis.Unlike the permanent data failure,which is fixed through a background job,temporarily unavailable data is recovered on-the-fly to serve the ongoing read request.However,those newly revived data is discarded after serving the request,due to the assumption that data experiencing temporary failures could come back alive later.Such disposal of failure data prevents the sharing of failure information among clients,and leads to many unnecessary data recovery processes,(e.g.caused by either recurring unavailability of a data or multiple data failures in one stripe),thereby straining system performance.To this end,this paper proposes GFCache to cache corrupted data for the dual purposes of failure information sharing and eliminating unnecessary data recovery processes.GFCache employs a greedy caching approach of opportunism to promote not only the failed data,but also sequential failure-likely data in the same stripe.Additionally,GFCache includes a FARC(Failure ARC)catch replacement algorithm,which features a balanced consideration of failure recency,frequency to accommodate data corruption with good hit ratio.The stored data in GFCache is able to support fast read of the normal data access.Furthermore,since GFCache is a generic failure cache,it can be used anywhere erasure coding is deployed with any specific coding schemes and parameters.Evaluations show that GFCache achieves good hit ratio with our sophisticated caching algorithm and manages to significantly boost system performance by reducing unnecessary data recoveries with vulnerable data in the cache.

A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

Named Data Networking(NDN)is gaining a significant attention in Vehicular Ad-hoc Networks(VANET)due to its in-network content caching,name-based routing,and mobility-supporting characteristics.Nevertheless,existing NDN faces three significant challenges,including security,privacy,and routing.In particular,security attacks,such as Content Poisoning Attacks(CPA),can jeopardize legitimate vehicles with malicious content.For instance,attacker host vehicles can serve consumers with invalid information,which has dire consequences,including road accidents.In such a situation,trust in the content-providing vehicles brings a new challenge.On the other hand,ensuring privacy and preventing unauthorized access in vehicular(VNDN)is another challenge.Moreover,NDN’s pull-based content retrieval mechanism is inefficient for delivering emergency messages in VNDN.In this connection,our contribution is threefold.Unlike existing rule-based reputation evaluation,we propose a Machine Learning(ML)-based reputation evaluation mechanism that identifies CPA attackers and legitimate nodes.Based on ML evaluation results,vehicles accept or discard served content.Secondly,we exploit a decentralized blockchain system to ensure vehicles’privacy by maintaining their information in a secure digital ledger.Finally,we improve the default routing mechanism of VNDN from pull to a push-based content dissemination using Publish-Subscribe(Pub-Sub)approach.We implemented and evaluated our ML-based classification model on a publicly accessible BurST-Asutralian dataset for Misbehavior Detection(BurST-ADMA).We used five(05)hybrid ML classifiers,including Logistic Regression,Decision Tree,K-Nearest Neighbors,Random Forest,and Gaussian Naive Bayes.The qualitative results indicate that Random Forest has achieved the highest average accuracy rate of 100%.Our proposed research offers the most accurate solution to detect CPA in VNDN for safe,secure,and reliable vehicle communication.

Multi-Level Cache System of Small Spatio-Temporal Data Files Based on Cloud Storage in Smart City

In this paper, we present a distributed multi-level cache system based on cloud storage, which is aimed at the low access efficiency of small spatio-temporal data files in information service system of Smart City. Taking classification attribute of small spatio-temporal data files in Smart City as the basis of cache content selection, the cache system adopts different cache pool management strategies in different levels of cache. The results of experiment in prototype system indicate that multi-level cache in this paper effectively increases the access bandwidth of small spatio-temporal files in Smart City and greatly improves service quality of multiple concurrent access in system.

Shared Cache Based on Content Addressable Memory in a Multi-Core Architecture

Modern shared-memory multi-core processors typically have shared Level 2(L2)or Level 3(L3)caches.Cache bottlenecks and replacement strategies are the main problems of such architectures,where multiple cores try to access the shared cache simultaneously.The main problem in improving memory performance is the shared cache architecture and cache replacement.This paper documents the implementation of a Dual-Port Content Addressable Memory(DPCAM)and a modified Near-Far Access Replacement Algorithm(NFRA),which was previously proposed as a shared L2 cache layer in a multi-core processor.Standard Performance Evaluation Corporation(SPEC)Central Processing Unit(CPU)2006 benchmark workloads are used to evaluate the benefit of the shared L2 cache layer.Results show improved performance of the multicore processor’s DPCAM and NFRA algorithms,corresponding to a higher number of concurrent accesses to shared memory.The new architecture significantly increases system throughput and records performance improvements of up to 8.7%on various types of SPEC 2006 benchmarks.The miss rate is also improved by about 13%,with some exceptions in the sphinx3 and bzip2 benchmarks.These results could open a new window for solving the long-standing problems with shared cache in multi-core processors.

Request pattern change-based cache pollution attack detection and defense in edge computing

Through caching popular contents at the network edge,wireless edge caching can greatly reduce both the content request latency at mobile devices and the traffic burden at the core network.However,popularity-based caching strategies are vulnerable to Cache Pollution Attacks(CPAs)due to the weak security protection at both edge nodes and mobile devices.In CPAs,through initiating a large number of requests for unpopular contents,malicious users can pollute the edge caching space and degrade the caching efficiency.This paper firstly integrates the dynamic nature of content request and mobile devices into the edge caching framework,and introduces an eavesdroppingbased CPA strategy.Then,an edge caching mechanism,which contains a Request Pattern Change-based Cache Pollution Detection(RPC2PD)algorithm and an Attack-aware Cache Defense(ACD)algorithm,is proposed to defend against CPAs.Simulation results show that the proposed mechanism could effectively suppress the effects of CPAs on the caching performance and improve the cache hit ratio.

Cache in fog computing design,concepts,contributions,and security issues in machine learning prospective

The massive growth of diversified smart devices and continuous data generation poses a challenge to communication architectures.To deal with this problem,communication networks consider fog computing as one of promising technologies that can improve overall communication performance.It brings on-demand services proximate to the end devices and delivers the requested data in a short time.Fog computing faces several issues such as latency,bandwidth,and link utilization due to limited resources and the high processing demands of end devices.To this end,fog caching plays an imperative role in addressing data dissemination issues.This study provides a comprehensive discussion of fog computing,Internet of Things(IoTs)and the critical issues related to data security and dissemination in fog computing.Moreover,we determine the fog-based caching schemes and contribute to deal with the existing issues of fog computing.Besides,this paper presents a number of caching schemes with their contributions,benefits,and challenges to overcome the problems and limitations of fog computing.We also identify machine learning-based approaches for cache security and management in fog computing,as well as several prospective future research directions in caching,fog computing,and machine learning.

Joint User Association and Caching Placement for Cache-Enabling UAV Networks

Cache-enabling unmanned aerial vehicles(UAVs)are considered for storing popular contents and providing downlink data offloading in cellular networks.In this context,we formulate a joint optimization problem of user association,caching placement,and backhaul bandwidth allocation for minimizing content acquisition delay with consideration of UAVs’energy constraint.We decompose the formulated problem into two subproblems:i)user association and caching placement and ii)backhaul bandwidth allocation.We first obtain the optimal bandwidth allocation with given user association and caching placement by the Lagrangian multiplier approach.After that,embedding the backhaul bandwidth allocation algorithm,we solve the user association and caching placement problem by a threedimensional(3D)matching method.Then we decompose it into two two-dimensional(2D)matching problems and develop low-complexity algorithms.The proposed scheme converges and exhibits a low computational complexity.Simulation results demonstrate that the proposed cache-enabling UAV framework outperforms the conventional UAV-assisted cellular networks in terms of content acquisition delay and the proposed scheme achieves significantly lower content acquisition delay compared with other two benchmark schemes.

An Efficient Memory Management for Mobile Operating Systems Based on Prediction of Relaunch Distance

Recently,various mobile apps have included more features to improve user convenience.Mobile operating systems load as many apps into memory for faster app launching and execution.The least recently used(LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low.However,the LRUbased cached app termination does not distinguish between frequently or infrequently used apps.The app launch performance degrades if LRU terminates frequently used apps.Recent studies have suggested the potential of using users’app usage patterns to predict the next app launch and address the limitations of the current least recently used(LRU)approach.However,existing methods only focus on predicting the probability of the next launch and do not consider how soon the app will launch again.In this paper,we present a new approach for predicting future app launches by utilizing the relaunch distance.We define the relaunch distance as the interval between two consecutive launches of an app and propose a memory management based on app relaunch prediction(M2ARP).M2ARP utilizes past app usage patterns to predict the relaunch distance.It uses the predicted relaunch distance to determine which apps are least likely to be launched soon and terminate them to improve the efficiency of the main memory.

A Time Pattern-Based Intelligent Cache Optimization Policy on Korea Advanced Research Network

Data is growing quickly due to a significant increase in social media applications.Today,billions of people use an enormous amount of data to access the Internet.The backbone network experiences a substantial load as a result of an increase in users.Users in the same region or company frequently ask for similar material,especially on social media platforms.The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user.Applications that require relatively low latency can use Content Delivery Network(CDN)technology to meet their requirements.An edge and the data center con-stitute the CDN architecture.To fulfill requests from the edge and minimize the impact on the network,the requested content can be buffered closer to the user device.Which content should be kept on the edge is the primary concern.The cache policy has been optimized using various conventional and unconventional methods,but they have yet to include the timestamp beside a video request.The 24-h content request pattern was obtained from publicly available datasets.The popularity of a video is influenced by the time of day,as shown by a time-based video profile.We present a cache optimization method based on a time-based pat-tern of requests.The problem is described as a cache hit ratio maximization pro-blem emphasizing a relevance score and machine learning model accuracy.A model predicts the video to be cached in the next time stamp,and the relevance score identifies the video to be removed from the cache.Afterwards,we gather the logs and generate the content requests using an extracted video request pattern.These logs are pre-processed to create a dataset divided into three-time slots per day.A Long short-term memory(LSTM)model is trained on this dataset to forecast the video at the next time interval.The proposed optimized caching policy is evaluated on our CDN architecture deployed on the Korean Advanced Research Network(KOREN)infrastructure.Our findings demonstrate how add-ing time-based request patterns impacts the system by increasing the cache hit rate.To show the effectiveness of the proposed model,we compare the results with state-of-the-art techniques.