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SFML: A personalized, efficient, and privacy-preserving collaborative traffic classification architecture based on split learning and mutual learning SFML:基于分裂学习和相互学习的个性化、高效和保护隐私的协作式流量分类架构
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-23 DOI: 10.1016/j.future.2024.107487
Jiaqi Xia, Meng Wu, Pengyong Li

Traffic classification is essential for network management and optimization, enhancing user experience, network performance, and security. However, evolving technologies and complex network environments pose challenges. Recently, researchers have turned to machine learning for traffic classification due to its ability to automatically extract and distinguish traffic features, outperforming traditional methods in handling complex patterns and environmental changes while maintaining high accuracy. Federated learning, a distributed learning approach, enables model training without revealing original data, making it appealing for traffic classification to safeguard user privacy and data security. However, applying it to this task poses two challenges. Firstly, common client devices like routers and switches have limited computing resources, which can hinder efficient training and increase time costs. Secondly, real-world applications often demand personalized models and tasks for clients, posing further complexities. To address these issues, we propose Split Federated Mutual Learning (SFML), an innovative federated learning architecture designed for traffic classification that combines split learning and mutual learning. In SFML, each client maintains two models: a privacy model for the local task and a public model for the global task. These two models learn from each other through knowledge distillation. Furthermore, by leveraging split learning, we offload most of the computational tasks to the server, significantly reducing the computational burden on the client. Experimental results demonstrate that SFML outperforms typical training architectures in terms of convergence speed, model performance, and privacy protection. Not only does SFML improve training efficiency, but it also satisfies the personalized needs of clients and reduces their computational workload and communication overhead, providing users with a superior network experience.

流量分类对于网络管理和优化、提升用户体验、网络性能和安全性至关重要。然而,不断发展的技术和复杂的网络环境带来了挑战。最近,研究人员将流量分类的研究转向了机器学习,因为机器学习能够自动提取和区分流量特征,在处理复杂模式和环境变化方面优于传统方法,同时还能保持较高的准确性。联邦学习是一种分布式学习方法,可在不泄露原始数据的情况下进行模型训练,因此在交通分类中很有吸引力,可保护用户隐私和数据安全。然而,将其应用于这项任务会面临两个挑战。首先,路由器和交换机等普通客户端设备的计算资源有限,这可能会阻碍高效训练并增加时间成本。其次,现实世界中的应用往往要求为客户提供个性化的模型和任务,这就带来了更多的复杂性。为了解决这些问题,我们提出了分离式联合相互学习(SFML),这是一种创新的联合学习架构,设计用于将分离式学习和相互学习相结合的流量分类。在 SFML 中,每个客户端维护两个模型:一个是本地任务的隐私模型,另一个是全局任务的公共模型。这两个模型通过知识提炼相互学习。此外,通过利用拆分学习,我们将大部分计算任务卸载到服务器上,大大减轻了客户端的计算负担。实验结果表明,SFML 在收敛速度、模型性能和隐私保护方面都优于典型的训练架构。SFML 不仅提高了训练效率,还满足了客户端的个性化需求,减少了客户端的计算工作量和通信开销,为用户提供了卓越的网络体验。
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引用次数: 0
LSTM-Oppurs: Opportunistic user recruitment strategy based on deep learning in mobile crowdsensing system LSTM-Oppurs:移动人群感知系统中基于深度学习的机会性用户招募策略
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-22 DOI: 10.1016/j.future.2024.107490
Jing Zhang , Ding He , Xueqi Chen , Xiangxuan Zhong , Peiwei Tsai

As the scale of Mobile CrowdSensing (MCS) system expands, effective mobile user allocation and recruitment system design becomes crucial. Mobile users can be divided into opportunistic users and participatory ones. Most of the existing recruitment strategy have neglected some aspects, such as without considering the low-paying opportunistic users, without comprehensively considering users’ attributes and without considering their future location, etc. In this paper, the recruitment scheme is investigated by considering the opportunistic users. Firstly, the User Recruitment problem based on User Comprehensive Capabilities (urUCC) is proposed with the objective of maximizing the total revenue. In addition, this problem is proved to be NP-Hard. Secondly, the Opportunistic Users recruitment strategy based on Deep Learning (OUDL) is designed, which consists of three parts, the user location prediction algorithm based on the Long Short-Term Memory (LSTM), the user evaluation algorithm based on the topsis comprehensive evaluation method and the dynamic user recruitment algorithm. Finally, a large number of simulation experiments are conducted by using real datasets. It is proved that the strategy OUDL can recruit high-quality opportunistic users to participate in the sensing task while guaranteeing the task completion rate. Compared with other strategies, the task coverage of the strategy OUDL increased by more than 5% while the comprehensive quality of users increased by about 10%. Thus, the quality of data can be guaranteed while reducing the cost.

随着移动群感(MCS)系统规模的扩大,有效的移动用户分配和招募系统设计变得至关重要。移动用户可分为机会型用户和参与型用户。现有的招募策略大多忽略了一些方面,如未考虑低薪机会型用户、未全面考虑用户属性、未考虑用户未来位置等。本文研究了考虑机会主义用户的招募方案。首先,以总收入最大化为目标,提出了基于用户综合能力的用户招募问题(urUCC)。此外,该问题被证明为 NP-Hard。其次,设计了基于深度学习的机会主义用户招募策略(OUDL),该策略由三部分组成,即基于长短期记忆(LSTM)的用户位置预测算法、基于拓扑综合评价法的用户评价算法和动态用户招募算法。最后,利用真实数据集进行了大量仿真实验。实验证明,OUDL 策略可以招募高质量的机会主义用户参与感知任务,同时保证任务完成率。与其他策略相比,OUDL 策略的任务覆盖率提高了 5%以上,而用户的综合质量提高了约 10%。因此,在降低成本的同时还能保证数据质量。
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引用次数: 0
Concurrent vertical and horizontal federated learning with fuzzy cognitive maps 利用模糊认知地图同时进行纵向和横向联合学习
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-22 DOI: 10.1016/j.future.2024.107482
Jose L. Salmeron , Irina Arévalo

Data privacy is a major concern in industries such as healthcare or finance. The requirement to safeguard privacy is essential to prevent data breaches and misuse, which can have severe consequences for individuals and organisations. Federated learning is a distributed machine learning approach where multiple participants collaboratively train a model without compromising the privacy of their data. However, a significant challenge arises from the differences in feature spaces among participants, known as non-IID data. This research introduces a novel federated learning framework employing fuzzy cognitive maps, designed to comprehensively address the challenges posed by diverse data distributions and non-identically distributed features in federated settings. The proposal is tested through several experiments using four distinct federation strategies: constant-based, accuracy-based, AUC-based, and precision-based weights. The results demonstrate the effectiveness of the approach in achieving the desired learning outcomes while maintaining privacy and confidentiality standards.

数据隐私是医疗保健或金融等行业关注的一个主要问题。保护隐私的要求对于防止数据泄露和滥用至关重要,这可能会给个人和组织带来严重后果。联合学习是一种分布式机器学习方法,多个参与者在不损害其数据隐私的情况下合作训练一个模型。然而,参与者之间的特征空间差异(即非 IID 数据)带来了巨大挑战。本研究介绍了一种采用模糊认知图的新型联合学习框架,旨在全面解决联合环境中多样化数据分布和非相同分布特征所带来的挑战。通过使用四种不同的联合策略:基于常数的权重、基于准确度的权重、基于 AUC 的权重和基于精确度的权重,对该提案进行了多次实验测试。结果表明,该方法在实现预期学习效果的同时,还能有效地维护隐私和保密标准。
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引用次数: 0
Drug repositioning by collaborative learning based on graph convolutional inductive network 通过基于图卷积归纳网络的协作学习重新定位药物
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-22 DOI: 10.1016/j.future.2024.107491
Zhixia Teng , Yongliang Li , Zhen Tian , Yingjian Liang , Guohua Wang

Motivation:

Computational drug repositioning is a vital path to improve efficiency of drug discovery, which aims to find potential Drug–Disease Associations (DDAs) to develop new effects of the existing drugs. Many approaches detected novel DDAs from heterogenous network which integrates similar drugs, similar diseases and the known DDAs. However, sparsity of the known DDAs and intrinsic synergic relations on representations of drugs and diseases in the heterogenous network are still the main challenges for DDAs prediction.

Results:

To address the problems, a novel drug repositioning approach is proposed here. Firstly, a drug similar network is constructed by Gaussian similarity kernel fusion of multisource drug similarities. Likewise, a disease similar network is generated by the same strategy. Secondly, the known DDAs network is extended by a bi-random walk algorithm from the above-mentioned similar networks. Meanwhile, representations of drugs and diseases are learned from their similar networks through graph convolutions and then a DDAs network is induced from the representations. Finally, to discover latent DDAs, the inductive DDAs network is refined iteratively by collaborating with the extended known DDAs network. Comprehensive experimental results show that our method outperforms several state-of-the-art methods for predicting DDAs on indicators including precision, recall, F1-score, MCC, ROC and AUPR. Moreover, case studies suggest that our method is highly effective in practices. The success of our method may be attributed to three aspects: (1) reliable similar relationships of drugs and diseases; (2) enhanced connectivity of the heterogenous network; (3) reasonable collaborative induction on DDAs network. Our method is freely available at https://github.com/BioMLab/DRCLN.

动机:计算药物重新定位是提高药物发现效率的重要途径,其目的是找到潜在的药物-疾病关联(DDA),以开发现有药物的新功效。许多方法都是从整合了相似药物、相似疾病和已知 DDAs 的异质网络中检测出新的 DDAs。结果:为了解决这些问题,本文提出了一种新的药物重新定位方法。首先,通过高斯相似核融合多源药物相似性,构建药物相似网络。同样,疾病相似网络也是通过同样的策略生成的。其次,通过双随机行走算法从上述相似网络中扩展出已知的 DDAs 网络。同时,通过图卷积从相似网络中学习药物和疾病的表征,然后从表征中诱导出 DDAs 网络。最后,为了发现潜在的 DDAs,通过与扩展的已知 DDAs 网络协作,迭代完善归纳的 DDAs 网络。综合实验结果表明,我们的方法在预测 DDA 的精确度、召回率、F1 分数、MCC、ROC 和 AUPR 等指标上都优于几种最先进的方法。此外,案例研究表明,我们的方法在实践中非常有效。我们方法的成功可归因于三个方面:(1)可靠的药物和疾病相似关系;(2)增强异质网络的连通性;(3)DDAs 网络的合理协作归纳。我们的方法可在 https://github.com/BioMLab/DRCLN 免费获取。
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引用次数: 0
JCDC: A blockchain-based framework for secure data storage and circulation in JointCloud JCDC:基于区块链的联合云数据安全存储和流通框架
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-20 DOI: 10.1016/j.future.2024.107486
Kaimin Zhang, Xingwei Wang, Lin Qiu, Enliang lv, Jingjing Guo, Bo Yi

JointCloud computing represents a new generation cloud computing paradigm, which deeply integrates the cloud resources of multiple Cloud Service Providers (CSPs) to offer tailored cloud services to users. In contrast to traditional multi-cloud environment, JointCloud environment involve data circulation among multiple CSPs. However, in JointCloud environment, CSPs are not always fully trustworthy and they may illegally infringe upon users’ data privacy and security for their own benefit. Additionally, the heterogeneity arising from different data storage formats, structures, access control, and permission management mechanisms adopted by various CSPs makes achieving unified data management in JointCloud challenging. Therefore, to ensure secure storage and efficient circulation of data within JointCloud, it is essential to prevent violations for user privacy and data ownership, shield the heterogeneity of underlying data management mechanisms across different CSPs, and establish trusted transactions between CSPs. In this paper, we propose a framework called JointCloud Data Chain (JCDC) based on JointCloud computing and blockchain for data storage and circulation, aiming to ensure secure data storage and trustworthy transactions. JCDC utilizes blockchain to record data ownership and control data circulation, while integrating storage resources from various CSPs to construct a distributed off-chain Personal Data Storage (PDS) for expanding system storage capacity. Additionally, JCDC employs Certificateless Public Key Cryptography (CL-PKC) and Proxy Re-encryption technologies for user identity management and secure data transactions. Furthermore, smart contracts are designed to enable automated data storage and sharing. We conduct a security analysis of JCDC and develop a prototype system to validate its performance and practicality. Finally, extensive experimentation and analysis demonstrate that JCDC exhibits low time latency and cost, which makes it practical.

联合云计算(JointCloud computing)是新一代云计算模式的代表,它深度整合了多个云服务提供商(CSP)的云资源,为用户提供量身定制的云服务。与传统的多云环境相比,联合云环境涉及多个云服务提供商之间的数据流通。然而,在联合云环境中,CSP 并不总是完全可信的,它们可能会为了自身利益非法侵犯用户的数据隐私和安全。此外,不同的 CSP 采用不同的数据存储格式、结构、访问控制和权限管理机制,这些异质性使得在 JointCloud 中实现统一的数据管理具有挑战性。因此,为确保联合云内数据的安全存储和高效流通,必须防止侵犯用户隐私和数据所有权,屏蔽不同 CSP 之间底层数据管理机制的异构性,并建立 CSP 之间的可信交易。本文提出了一种基于联合云计算和区块链的联合云数据链(JointCloud Data Chain,JCDC)框架,用于数据存储和流通,旨在确保安全的数据存储和可信的交易。JCDC 利用区块链记录数据所有权并控制数据流通,同时整合不同 CSP 的存储资源,构建分布式链外个人数据存储(PDS),以扩展系统存储容量。此外,JCDC 还采用了无证书公钥加密技术(CL-PKC)和代理重加密技术,用于用户身份管理和安全数据交易。此外,智能合约旨在实现自动数据存储和共享。我们对 JCDC 进行了安全分析,并开发了一个原型系统来验证其性能和实用性。最后,大量实验和分析表明,JCDC 具有低时间延迟和低成本的特点,因此非常实用。
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引用次数: 0
Load-aware switch migration for controller load balancing in edge–cloud architectures 在边缘云架构中实现控制器负载平衡的负载感知交换机迁移
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-19 DOI: 10.1016/j.future.2024.107489
Yong Liu , Qian Meng , Kefei Chen , Zhonghua Shen

As the fundamental infrastructure for edge–cloud architectures, the inter-datacenter elastic optical network is used for data analysis and processing. As the demand for applications increases, the large number of service requests increases the processing overhead in the control plane, resulting in unbalanced controller loads. Existing switch migration mechanisms have been proposed for controller load balancing. Unfortunately, most of the existing mechanisms only consider the switch with the highest flow request rate as the migration object in the process of switch selection, and ignore the migration cost generated in the switch migration activity, such as the update cost of flow request message and the deployment cost of migration rule, which may increase the controller load. Additionally, most of them choose the controller with light load as the target controller to associate with the switch to be migrated, without considering whether the target controller is overloaded after the switch migration, which leads to the low load balancing performance of the controller. In view of the above problems, this paper proposes a Load-Aware Switch Migration (LASM) mechanism in edge–cloud architectures. The LASM mechanism models and analyses the cost metrics affecting switch migration and selects lower-cost switches from overloaded controller-controlled domain networks for migration activities. Besides, the LASM mechanism models switch migration based on the 0-1 knapsack problem and avoids overloading the target controllers through a greedy policy to achieving optimal migration activities. The experimental results show that the proposed LASM mechanism improves controller load balancing performance by an average of 34.3%, eliminates migration costs by 30.2%, and reduces response times by an average of 39.3%, respectively, compared to existing solutions.

作为边缘云架构的基础架构,数据中心间弹性光网络用于数据分析和处理。随着应用需求的增加,大量服务请求会增加控制平面的处理开销,导致控制器负载不平衡。现有的交换机迁移机制已被提出用于控制器负载平衡。遗憾的是,大多数现有机制在选择交换机的过程中只考虑流量请求率最高的交换机作为迁移对象,而忽略了交换机迁移活动中产生的迁移成本,如流量请求报文的更新成本和迁移规则的部署成本,这可能会增加控制器负载。此外,大多数方案选择负载较轻的控制器作为目标控制器与待迁移交换机关联,而没有考虑交换机迁移后目标控制器是否过载,导致控制器负载均衡性能低下。针对上述问题,本文提出了边缘云架构中的负载感知交换机迁移(LASM)机制。LASM 机制对影响交换机迁移的成本指标进行建模和分析,并从过载的控制器控制域网络中选择成本较低的交换机进行迁移活动。此外,LASM 机制基于 0-1 knapsack 问题对交换机迁移进行建模,并通过贪婪策略避免目标控制器过载,从而实现最优迁移活动。实验结果表明,与现有解决方案相比,所提出的 LASM 机制平均提高了 34.3% 的控制器负载平衡性能,消除了 30.2% 的迁移成本,并平均缩短了 39.3% 的响应时间。
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引用次数: 0
Enhancing IoT device security in Kubernetes: An approach adopted for network policies and the SARIK framework 增强 Kubernetes 中物联网设备的安全性:网络策略和 SARIK 框架采用的方法
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-17 DOI: 10.1016/j.future.2024.107485
Jonathan G.P. dos Santos , Geraldo P. Rocha Filho , Rodolfo I. Meneguette , Rodrigo Bonacin , Gustavo Pessin , Vinícius P. Gonçalves

The Internet of Things (IoT) has ushered in an era of connected devices that, while facilitating real-time data collection and sharing, also exposes these devices to significant security risks. This study addresses the challenges of security risks and vulnerabilities by employing the Network Policy in Kubernetes and focusing on the SARIK framework. SARIK is designed to automate the creation and implementation of network policies, with the aim of enhancing the efficiency and strengthening the protection of IoT devices. Experiments conducted in a controlled environment with Minikube in Kubernetes showed that the implementation of SARIK notably improved the security of IoT devices. Key observations included a noticeable reduction in vulnerability to cyberattacks and a significant increase in the overall resilience of the system. In particular, the study revealed improvements in the performance metrics analyzed, which is evidence of SARIK’s effectiveness in real-world scenarios. Compared with existing frameworks - e.g., those of Sysdig -, SARIK is notable for its integration with Kubernetes network policies and its emphasis on automated security management. Although automation is a key factor in related works, SARIK’s unique approach to leveraging the inherent capabilities of Kubernetes offers a distinct advantage in ensuring the security of IoT environments. This aspect, along with its performance benefits, underlines the value of SARIK’s contribution to IoT security. The application of SARIK in protecting IoT devices in Kubernetes environments meets the need for automated and cohesive strategies to tackle current security threats. This study not only highlights the efficiency of SARIK but also emphasizes the need for evolving security strategies, that can be adapted to dynamic threat modeling in complex and interconnected IT environments.

物联网(IoT)开创了一个连接设备的时代,在促进实时数据收集和共享的同时,也使这些设备面临巨大的安全风险。本研究通过采用 Kubernetes 中的网络策略,并重点关注 SARIK 框架,来应对安全风险和漏洞带来的挑战。SARIK 旨在自动创建和实施网络策略,以提高效率并加强对物联网设备的保护。利用 Kubernetes 中的 Minikube 在受控环境中进行的实验表明,SARIK 的实施显著提高了物联网设备的安全性。主要观察结果包括:遭受网络攻击的脆弱性明显降低,系统的整体恢复能力显著增强。特别是,研究显示,所分析的性能指标均有所改善,这证明了 SARIK 在实际应用场景中的有效性。与现有框架(如 Sysdig 框架)相比,SARIK 的显著特点是与 Kubernetes 网络策略集成,并强调自动化安全管理。虽然自动化是相关工作的一个关键因素,但 SARIK 利用 Kubernetes 固有功能的独特方法在确保物联网环境安全方面具有明显优势。这方面的优势及其性能优势凸显了 SARIK 在物联网安全方面的价值。SARIK 在保护 Kubernetes 环境中的物联网设备方面的应用满足了应对当前安全威胁的自动化和内聚性策略的需求。这项研究不仅突出了 SARIK 的效率,还强调了对不断发展的安全策略的需求,这些策略可以适应复杂、互联的 IT 环境中的动态威胁建模。
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引用次数: 0
IDAD: An improved tensor train based distributed DDoS attack detection framework and its application in complex networks IDAD:基于张量列车的改进型分布式 DDoS 攻击检测框架及其在复杂网络中的应用
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-16 DOI: 10.1016/j.future.2024.07.049
Qiyuan Fan , Xue Li , Puming Wang , Xin Jin , Shaowen Yao , Shengfa Miao , Min An , Yuqing Zhao

With the vigorous development of Internet technology, the scale of systems in the network has increased sharply, which provides a great opportunity for potential attacks, especially the Distributed Denial of Service (DDoS) attack. In this case, detecting DDoS attacks is critical to system security. However, current detection methods exhibit limitations, leading to compromises in accuracy and efficiency. To cope with it, three key strategies are implemented in this paper: (i) Using tensors to model large-scale and heterogeneous data in complex networks; (ii) Proposing a denoising algorithm based on the improved and distributed tensor train (IDTT) decomposition, which optimizes the tensor train(TT) decomposition in terms of parallel computation and low-rank estimation; (iii) Combining (i), (ii) and Light Gradient Boosting Machine (LightGBM) classification model, an efficient DDoS attack detection framework is proposed. Datasets CIC-DDoS2019 and NSL-KDD are used to evaluate the framework, and results demonstrate that accuracy can reach 99.19% while having the characteristics of low storage consumption and well speedup ratio.

随着互联网技术的蓬勃发展,网络中的系统规模急剧扩大,这为潜在的攻击,尤其是分布式拒绝服务(DDoS)攻击提供了巨大的机会。在这种情况下,检测 DDoS 攻击对系统安全至关重要。然而,当前的检测方法存在局限性,导致准确性和效率大打折扣。为应对这一问题,本文采用了三种关键策略:(i) 使用张量对复杂网络中的大规模异构数据进行建模;(ii) 提出一种基于改进的分布式张量列车(IDTT)分解的去噪算法,该算法在并行计算和低秩估计方面优化了张量列车(TT)分解;(iii) 将(i)、(ii)和光梯度提升机(LightGBM)分类模型相结合,提出了一种高效的 DDoS 攻击检测框架。数据集 CIC-DDoS2019 和 NSL-KDD 用于评估该框架,结果表明准确率可达 99.19%,同时具有低存储消耗和高加速比的特点。
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引用次数: 0
Program context-assisted address translation for high-capacity SSDs 针对大容量固态硬盘的程序上下文辅助地址转换
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-14 DOI: 10.1016/j.future.2024.107483
Xiaochang Li , Minjae Kim , Sungjin Lee , Zhengjun Zhai , Jihong Kim

As the capacity of NAND flash-based SSDs keeps increasing, it becomes crucial to design a memory-efficient address translation algorithm that offers high performance when a translation table cannot be entirely loaded in a controller DRAM. Existing flash translation layers (FTL) employ demand-based address translation which caches popular mapping information in DRAM by leveraging locality of I/O references. Owing to the lack of information about detailed behaviors of applications, however, existing demand-based FTLs often suffer from many translation-table misses and thus result in sub-optimal performance. In this paper, we propose a new Program context-AssisteD Flash Translation Layer, called PADFTL. Unlike existing FTLs which are implemented as the form of firmware, PADFTL is vertically integrated with a host-level I/O classifier which provides useful hints for an FTL in an SSD to make a better decision in managing a translation table. The host-level I/O classifier monitors unique behaviors of applications by analyzing their program contexts and categorizes I/O patterns into four types, (1) Loop, (2) Hot, (3) Sequential, and (4) Random, which are then delivered to an SSD through extended interfaces. The SSD-side module of PADFTL partitions a controller DRAM into four zones and isolates mapping information associated with different I/O patterns into separate zones. By employing cache management strategies optimized for individual zones, PADFTL can lower the overall translation-table miss ratio. To evaluate the effectiveness of PADFTL, we implement the host-level classifier in the Linux kernel and PADFTL’s FTL in a trace-driven FTL simulator. In our experimental results, compared to the state-of-the-art FTL, PADFTL increases the overall table hit ratio by 16% while reducing the address translation time by up to 20% on average.

随着基于 NAND 闪存的固态硬盘容量不断增加,设计一种内存效率高的地址转换算法变得至关重要,这种算法能在转换表无法全部加载到控制器 DRAM 中时提供高性能。现有的闪存转换层(FTL)采用基于需求的地址转换,通过利用 I/O 引用的位置性将常用映射信息缓存在 DRAM 中。然而,由于缺乏有关应用详细行为的信息,现有的基于需求的 FTL 经常会出现大量的翻译表遗漏,从而导致性能达不到最优。在本文中,我们提出了一种新的程序上下文辅助闪存转换层(Program context-AssisteD Flash Translation Layer),称为 PADFTL。与以固件形式实现的现有 FTL 不同,PADFTL 与主机级 I/O 分类器垂直集成,为固态硬盘中的 FTL 在管理转换表时做出更好的决策提供有用的提示。主机级 I/O 分类器通过分析程序上下文监控应用程序的独特行为,并将 I/O 模式分为四种类型:(1) 循环;(2) 热;(3) 顺序;(4) 随机,然后通过扩展接口传送到固态硬盘。PADFTL 的 SSD 端模块将控制器 DRAM 划分为四个区域,并将与不同 I/O 模式相关的映射信息隔离到不同的区域。通过采用针对单个区域进行优化的高速缓存管理策略,PADFTL 可以降低整体转换表未命中率。为了评估 PADFTL 的有效性,我们在 Linux 内核中实现了主机级分类器,并在跟踪驱动的 FTL 模拟器中实现了 PADFTL 的 FTL。在我们的实验结果中,与最先进的 FTL 相比,PADFTL 将总体表命中率提高了 16%,同时将地址转换时间平均缩短了 20%。
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引用次数: 0
Quantum resource estimation for large scale quantum algorithms 大规模量子算法的量子资源估算
IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2024-08-12 DOI: 10.1016/j.future.2024.107480
Vlad Gheorghiu , Michele Mosca

Quantum algorithms are often represented in terms of quantum circuits operating on ideal (logical) qubits. However, the practical implementation of these algorithms poses significant challenges. Many quantum algorithms require a substantial number of logical qubits, and the inherent susceptibility to errors of quantum computers require quantum error correction. The integration of error correction introduces overhead in terms of both space (physical qubits required) and runtime (how long the algorithm needs to be run for). This paper addresses the complexity of comparing classical and quantum algorithms, primarily stemming from the additional quantum error correction overhead. We propose a comprehensive framework that facilitates a direct and meaningful comparison between classical and quantum algorithms. By acknowledging and addressing the challenges introduced by quantum error correction, our framework aims to provide a clearer understanding of the comparative performance of classical and quantum computing approaches. This work contributes to understanding the practical viability and potential advantages of quantum algorithms in real-world applications.

We apply our framework to quantum cryptanalysis, since it is well known that quantum algorithms can break factoring and discrete logarithm based cryptography and weaken symmetric cryptography and hash functions. In order to estimate the real-world impact of these attacks, apart from tracking the development of fault-tolerant quantum computers it is important to have an estimate of the resources needed to implement these quantum attacks. This analysis provides state-of-the art snap-shot estimates of the realistic costs of implementing quantum attacks on these important cryptographic algorithms, assuming quantum fault-tolerance is achieved using surface code methods, and spanning a range of potential error rates. These estimates serve as a guide for gauging the realistic impact of these algorithms and for benchmarking the impact of future advances in quantum algorithms, circuit synthesis and optimization, fault-tolerance methods and physical error rates.

量子算法通常用在理想(逻辑)量子比特上运行的量子电路来表示。然而,这些算法的实际应用却面临着巨大的挑战。许多量子算法需要大量逻辑量子比特,而量子计算机固有的易出错特性要求进行量子纠错。纠错的整合带来了空间(所需物理比特)和运行时间(算法需要运行多长时间)方面的开销。本文探讨了比较经典算法和量子算法的复杂性,这主要源于额外的量子纠错开销。我们提出了一个综合框架,有助于对经典算法和量子算法进行直接而有意义的比较。通过承认和应对量子纠错带来的挑战,我们的框架旨在让人们更清楚地了解经典计算和量子计算方法的比较性能。我们将框架应用于量子密码分析,因为众所周知,量子算法可以破解基于因式分解和离散对数的密码学,并削弱对称密码学和哈希函数。为了估算这些攻击在现实世界中的影响,除了跟踪容错量子计算机的发展外,估算实施这些量子攻击所需的资源也很重要。这项分析提供了对这些重要加密算法实施量子攻击的现实成本的最新估算,假设使用表面代码方法实现量子容错,并跨越一系列潜在错误率。这些估算可作为衡量这些算法实际影响的指南,也可作为量子算法、电路合成与优化、容错方法和物理错误率未来发展影响的基准。
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引用次数: 0
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Future Generation Computer Systems-The International Journal of Escience
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