Pilfering of western juniper seed caches by scatter-hoarding rodents varies by microsite and canopy type

Scatter-hoarding rodents store seeds throughout their home ranges in superficially buried caches which,unlike seeds larder-hoarded in burrows,are difficult to defend.Cached seeds are often pilfered by other scatter-hoarders and either re-cached,eaten or larder-hoarded.Such seed movements can influence seedling recruitment,because only seeds remaining in caches are likely to germinate.Although the importance of scatter-hoarding rodents in the dispersal of western juniper seeds has recently been revealed,the level of pilfering that occurs after initial burial is unknown.Seed traits,soil moisture,and substrate can influence pilfering processes,but less is known about how pilfering varies among caches placed in open versus canopy microsites,or how cache discovery and removal varies among different canopy-types,tree versus shrub.We compared the removal of artificial caches between open and canopy microsites and between tree and shrub canopies at two sites in northeastern California during late spring and fall.We also used trail cameras at one site to monitor artificial cache removal,identify potential pilferers,and illuminate microsite use by scatter-hoarders.Removal of artificial caches was faster in open microsites at both sites during both seasons,and more caches were removed from shrub than tree canopies.California kangaroo rats were the species observed most on cameras,foraging most often in open microsites,which could explain the observed pilfering patterns.This is the first study to document pilfering of western juniper seeds,providing further evidence of the importance of scatter-hoarding rodent foraging behavior in understanding seedling recruitment processes in juniper woodlands.

Managing Data-Objects in Dynamically Reconfigurable Caches

The widening gap between processor and memory speeds makes cache an important issue in the computer system design. Compared with work set of programs, cache resource is often rare. Therefore, it is very important for a computer system to use cache efficiently. Toward a dynamically reconfigurable cache proposed recently, DOOC （Data- Object Oriented Cache）, this paper proposes a quantitative framework for analyzing the cache requirement of data-objects, which includes cache capacity, block size, associativity and coherence protocol. And a kind of graph coloring algorithm dealing with the competition between data-objects in the DOOC is proposed as well. Finally, we apply our approaches to the compiler management of DOOC. We test our approaches on both a single-core platform and a four-core platform. Compared with the traditional caches, the DOOC in both platforms achieves an average reduction of 44.98% and 49.69% in miss rate respectively. And its performance is very close to the ideal optimal cache.

Hierarchical caches in content-centric networks： modeling and analysis

Content-centric network （CCN） is a new Inter- net architecture in which content is treated as the primitive of communication. In CCN, routers are equipped with con- tent stores at the content level, which act as caches for fre- quently requested content. Based on this design, the Internet is available to provide content distribution services without any application-layer support. In addition, as caches are inte- grated into routers, the overall performance of CCN will be deeply affected by the caching efficiency. In this paper, our aim is to gain some insights on how caches should be designed to maintain a high performance in a cost-efficient way. We try to model the two-layer cache hi- erarchy composed of CCN touters using a two-dimensional discrete-time Markov chain, and develop an efficient algo- rithm to calculate the hit ratios of these caches. Simulations validate the accuracy of our modeling method, and convey some meaningful information which can help us better un- derstand the caching mechanism of CCN.

Protecting User Privacy in a Multi-Path Information-Centric Network Using Multiple Random-Caches

In-network caching is a fundamental mechanism advocated by information-centric networks （ICNs） for efficient content delivery. However, this new mechanism also brings serious privacy risks due to cache snooping attacks. One effective solution to this problem is random-cache, where the cache in a router randomly mimics a cache hit or a cache miss for each content request/probe. In this paper, we investigate the effectiveness of using multiple random-caches to protect cache privacy in a multi-path ICN. We propose models for characterizing the privacy of multi-path ICNs with random-caches, and analyze two different attack scenarios： 1） prefix-based attacks and 2） suffix-based attacks. Both homogeneous and heterogeneous caches are considered. Our analysis shows that in a multi-path ICN an adversary can potentially gain more privacy information by adopting prefix-based attacks. Furthermore, heterogeneous caches provide much better privacy protection than homogeneous ones under both attacks. The effect of different parameters on the privacy of multi-path random-caches is further investigated, and the comparison with its single-path counterpart is carried out based on numerical evaluations. The analysis and results in this paper provide insights in designing and evaluating multi-path ICNs when we take privacy into consideration.

Caché数据库中数据的存储及其查询优化

Caché数据库的多维数据模型可以存储丰富的数据,在处理复杂的医疗数据时减少了表连接等处理过程,从而使多维数组能更快地存取数据。与主流的Oracle和SQL server等关系型数库相比,Caché主要在其存储结构上有很大的不同,Caché主要是以Global的形式存储数据,依据M语言开发应用程序。首先,介绍了Caché数据库中数据的存储形式;然后,展示了在医院HIS系统应用过程中Caché数据库中数据查询的几种方式及应用场合;最后,总结Caché数据库中SQL优化的几种办法。结果表明,Caché数据库具有更高的灵活性,适用于多种应用场合,而且在采用优化的查询方案后查询效率提高了很多倍。

基于GPU的高分辨率卫星影像正射纠正处理

针对高分辨率卫星影像正射纠正处理效率低的问题,本文提出了一种基于GPU的高分辨率卫星影像正射纠正处理方法。受GPU全局内存容量限制,对DEM数据和原始影像作矩形分块分批处理,通过对GPU作执行配置优化和存储器优化,大幅度缩短了正射纠正处理时间。在执行配置优化中,对线程块内的线程数量优化选择;在存储器优化中,将RPC参数存储在常量内存中,GPU采用计算工作负载模式,片上缓存划分为32 KB共享内存和64 KB一级cache。试验结果表明,与CPU多线程相比,该方法时间加速比约为8.6倍,能够提高高分辨率卫星影像正射纠正处理效率,可以满足应急快速处理需求。

辣椒查尔酮合成酶基因CaCHS02的克隆及功能分析

查尔酮合成酶(Chalcone synthase,CHS)基因在植物黄酮类物质代谢过程中发挥重要作用。为研究查尔酮合成酶基因CaCHS02在辣椒(Capsicum annuum L.)中辣椒类黄酮代谢过程中的功能,本研究分析了CaCHS02基因的基因特征、蛋白特点和基因表达模式,并构建了CaCHS02过表达载体,通过农杆菌介导法获得瞬时过表达CaCHS02(35S:CaCHS02)的辣椒植株。结果发现,瞬时过表达CaCHS02的辣椒植株叶片中CaCHS02发生显著超量表达,其中编码类黄酮代谢途径中其他关键酶基因(CaCHS02、CaPAL、CaC4H、Ca4CL、CaCHI、CaFLS和CaF3H)的表达水平同步显著上调;超表达CaCHS02促进辣椒叶片中查尔酮合成酶酶活性、总黄酮含量和辣椒叶片的α-葡糖糖苷酶抑制活性的提升。研究表明,CaCHS02在辣椒类黄酮代谢过程中发挥正向调控功能,过表达CaCHS02提高了辣椒叶片的α-葡糖糖苷酶抑制活性。本研究为解析辣椒α-葡萄糖苷酶抑制剂生物合成机制奠定了基础,为选育高α-葡糖糖苷酶抑制活性的功能辣椒品种提供了理论支持。

多核处理器共享Cache的划分算法

针对多核处理器性能优化问题,文中深入研究多核处理器上共享Cache的管理策略,提出了基于缓存时间公平性与吞吐率的共享Cache划分算法MT-FTP(Memory Time based Fair and Throughput Partitioning)。以公平性和吞吐率两个评价性指标建立数学模型,并分析了算法的划分流程。仿真实验结果表明,MT-FTP算法在系统吞吐率方面表现较好,其平均IPC(Instructions Per Cycles)值比UCP(Use Case Point)算法高1.3%,比LRU(Least Recently Used)算法高11.6%。MT-FTP算法对应的系统平均公平性比LRU算法的系统平均公平性高17%,比UCP算法的平均公平性高16.5%。该算法实现了共享Cache划分公平性并兼顾了系统的吞吐率。

基于Cache优化的服务调用方法

集中式服务网关通常使用共享内存进行服务实例与治理参数的本地化生产与消费,实现业务处理与服务发现逻辑的解耦,增强系统的稳定性,但频繁的共享内存操作往往带来系统资源利用率和请求处理耗时上的低效。通过引入缓存机制,在服务网关的路由组件内部实现并利用针对服务调用优化的Cache,热点数据请求直接从Cache中读取结构化信息,避免了共享内存操作与存储块的编解码,有效地利用缓存空间,提高了数据访问速度,同时减少了共享内存操作中的资源竞争,提高了系统并发。

RISC-V标量处理器的应用与优化分析

阐述基于RISC-V指令集架构的特点,设计一款支持RV64IM指令子集的处理器核。首先,分析流水线对处理器性能的影响,使用五级流水线以提升处理器的吞吐率。其次,使用分支预测模块及Cache缓存模块对处理器性能进行优化。最后,使用FPGA验证处理器设计,在50MHz时钟下,CoreMark跑分为2.86/MHz。

Multilayer Satellite Network Collaborative Mobile Edge Caching:A GCN-Based Multi-Agent Approach

With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has ensued.The emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also significantly improve user experience.Integrating with the MEC and satellite networks,the network is empowered popular content ubiquitously and seamlessly.Addressing the research gap between multilayer satellite networks and MEC,we study the caching placement problem in this paper.Initially,we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale networks.Considering the constraint on satellite capacity and content propagation delay,the cache placement problem is then formulated and transformed into a markov decision process(MDP),where the content coded caching mechanism is utilized to promote the efficiency of content delivery.Furthermore,a new generic metric,content delivery cost,is proposed to elaborate the performance of caching decision in large-scale networks.Then,we introduce a graph convolutional network(GCN)-based multi-agent advantage actor-critic(A2C)algorithm to optimize the caching decision.Finally,extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.

R-DSP中二级Cache控制器的优化设计

针对二级Cache控制器(L2)对于提升R数字信号处理器(R-DSP)访存效率和整体性能的重要作用,结合L2中涉及的内存安全维护和多请求访存仲裁问题,在现有R-DSP中L2基础上实现优化。首先,采用多重分块的存储组织结构,提高访存效率;其次,并行处理一级Cache控制器请求与外存请求,减小请求处理周期;最后,增加带宽管理与存储保护功能,合理仲裁访存请求并维护存储安全。实验结果表明,相较于传统设计,新设计在保护二级存储安全的同时实现带宽管理式访存仲裁。与现有R-DSP中的L2相比,新设计的存储体单拍最大可响应访存请求数量提升了1倍,一级请求和外存请求的平均处理时钟周期数分别降低了25%和19.6%。

Digital twin driven and intelligence enabled content delivery in end-edge-cloud collaborative 5G networks

The rapid development of 5G/6G and AI enables an environment of Internet of Everything(IoE)which can support millions of connected mobile devices and applications to operate smoothly at high speed and low delay.However,these massive devices will lead to explosive traffic growth,which in turn cause great burden for the data transmission and content delivery.This challenge can be eased by sinking some critical content from cloud to edge.In this case,how to determine the critical content,where to sink and how to access the content correctly and efficiently become new challenges.This work focuses on establishing a highly efficient content delivery framework in the IoE environment.In particular,the IoE environment is re-constructed as an end-edge-cloud collaborative system,in which the concept of digital twin is applied to promote the collaboration.Based on the digital asset obtained by digital twin from end users,a content popularity prediction scheme is firstly proposed to decide the critical content by using the Temporal Pattern Attention(TPA)enabled Long Short-Term Memory(LSTM)model.Then,the prediction results are input for the proposed caching scheme to decide where to sink the critical content by using the Reinforce Learning(RL)technology.Finally,a collaborative routing scheme is proposed to determine the way to access the content with the objective of minimizing overhead.The experimental results indicate that the proposed schemes outperform the state-of-the-art benchmarks in terms of the caching hit rate,the average throughput,the successful content delivery rate and the average routing overhead.

The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

An SDN-Based Algorithm for Caching,Routing,and Load Balancing in ICN

One of the challenges of Informationcentric Networking(ICN)is finding the optimal location for caching content and processing users’requests.In this paper,we address this challenge by leveraging Software-defined Networking(SDN)for efficient ICN management.To achieve this,we formulate the problem as a mixed-integer nonlinear programming(MINLP)model,incorporating caching,routing,and load balancing decisions.We explore two distinct scenarios to tackle the problem.Firstly,we solve the problem in an offline mode using the GAMS environment,assuming a stable network state to demonstrate the superior performance of the cacheenabled network compared to non-cache networks.Subsequently,we investigate the problem in an online mode where the network state dynamically changes over time.Given the computational complexity associated with MINLP,we propose the software-defined caching,routing,and load balancing(SDCRL)algorithm as an efficient and scalable solution.Our evaluation demonstrates that the SDCRL algorithm significantly reduces computational time while maintaining results that closely resemble those achieved by GAMS.

User Preference Aware Hierarchical Edge-User Cooperative Caching Strategy

The emergence of various new services has posed a huge challenge to the existing network architecture.To improve the network delay and backhaul pressure,caching popular contents at the edge of network has been considered as a feasible scheme.However,how to efficiently utilize the limited caching resources to cache diverse contents has been confirmed as a tough problem in the past decade.In this paper,considering the time-varying user requests and the heterogeneous content sizes,a user preference aware hierarchical cooperative caching strategy in edge-user caching architecture is proposed.We divide the caching strategy into three phases,that is,the content placement,the content delivery and the content update.In the content placement phase,a cooperative content placement algorithm for local content popularity is designed to cache contents proactively.In the content delivery phase,a cooperative delivery algorithm is proposed to deliver the cached contents.In the content update phase,a content update algorithm is proposed according to the popularity of the contents.Finally,the proposed caching strategy is validated using the MovieLens dataset,and the results reveal that the proposed strategy improves the delay performance by at least 35.3%compared with the other three benchmark strategies.

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.

Cost-Efficient Edge Caching for NOMA-Enabled IoT Services

Mobile edge computing(MEC)is a promising paradigm by deploying edge servers(nodes)with computation and storage capacity close to IoT devices.Content Providers can cache data in edge servers and provide services for IoT devices,which effectively reduces the delay for acquiring data.With the increasing number of IoT devices requesting for services,the spectrum resources are generally limited.In order to effectively meet the challenge of limited spectrum resources,the Non-Orthogonal Multiple Access(NOMA)is proposed to improve the transmission efficiency.In this paper,we consider the caching scenario in a NOMA-enabled MEC system.All the devices compete for the limited resources and tend to minimize their own cost.We formulate the caching problem,and the goal is to minimize the delay cost for each individual device subject to resource constraints.We reformulate the optimization as a non-cooperative game model.We prove the existence of Nash equilibrium(NE)solution in the game model.Then,we design the Game-based Cost-Efficient Edge Caching Algorithm(GCECA)to solve the problem.The effectiveness of our GCECA algorithm is validated by both parameter analysis and comparison experiments.

Deep Reinforcement Learning-Based Task Offloading and Service Migrating Policies in Service Caching-Assisted Mobile Edge Computing

Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.

Deep Reinforcement Learning for Energy-Efficient Edge Caching in Mobile Edge Networks

Edge caching has emerged as a promising application paradigm in 5G networks,and by building edge networks to cache content,it can alleviate the traffic load brought about by the rapid growth of Internet of Things(IoT)services and applications.Due to the limitations of Edge Servers(ESs)and a large number of user demands,how to make the decision and utilize the resources of ESs are significant.In this paper,we aim to minimize the total system energy consumption in a heterogeneous network and formulate the content caching optimization problem as a Mixed Integer Non-Linear Programming(MINLP).To address the optimization problem,a Deep Q-Network(DQN)-based method is proposed to improve the overall performance of the system and reduce the backhaul traffic load.In addition,the DQN-based method can effectively solve the limitation of traditional reinforcement learning(RL)in complex scenarios.Simulation results show that the proposed DQN-based method can greatly outperform other benchmark methods,and significantly improve the cache hit rate and reduce the total system energy consumption in different scenarios.