Chao Su;Xiaoshuang Xing;Xiaolu Cheng;Rui Guo;Chuanwen Luo
The Linux kernel is regularly updated to enhance security, improve performance, and introduce new functionalities. Traditional updating methods typically require rebooting, leading to service disruptions and potential data loss. Live-patching technology dynamically updates the kernel modules without rebooting, ensuring continuous service availability. However, this technique has its drawbacks. Since live-patching alters the original structure of data types, it can no longer utilize base offsets to access the members, imposing considerable overheads. This paper proposes LPAH (Live Patching with Alignment Holes), a live patching system that leverages the fragmented space generated by compile-time alignment for data types, to enable effective live patching updates for security vulnerability fixes, feature enhancements, and user-defined patching tasks. LPAH capitalizes on the relationship between these alignment holes and data objects. This approach ensures efficient access to extended data members while preserving the original data's integrity. This approach allows other functions to remain unaffected by updates and replacements through explicit type casts. Extensive experimental results show that LPAH offers valid and robust live patching for multiple real vulnerabilities in the Linux kernel, without degrading performance. Our method provides an efficient way to install security patches in the Linux kernel, and thus reenforces kernel security.
Linux 内核会定期更新，以增强安全性、提高性能并引入新功能。传统的更新方法通常需要重新启动，导致服务中断和潜在的数据丢失。实时补丁技术可以动态更新内核模块，无需重启，从而确保服务的持续可用性。不过，这种技术也有缺点。由于实时补丁改变了数据类型的原始结构，因此无法再利用基偏移来访问成员，从而造成了相当大的开销。本文提出的 LPAH（带对齐孔的实时补丁）是一种实时补丁系统，它利用数据类型编译时对齐所产生的碎片空间，为安全漏洞修复、功能增强和用户定义的补丁任务提供有效的实时补丁更新。LPAH 利用了这些对齐漏洞和数据对象之间的关系。这种方法可确保高效访问扩展数据成员，同时保持原始数据的完整性。这种方法允许其他函数通过显式类型转换不受更新和替换的影响。广泛的实验结果表明，LPAH 为 Linux 内核中的多个真实漏洞提供了有效、稳健的实时补丁，而且不会降低性能。我们的方法提供了一种在 Linux 内核中安装安全补丁的有效方法，从而加强了内核的安全性。
{"title":"LPAH: Illustrating Efficient Live Patching With Alignment Holes in Kernel Data","authors":"Chao Su;Xiaoshuang Xing;Xiaolu Cheng;Rui Guo;Chuanwen Luo","doi":"10.1109/TC.2024.3424263","DOIUrl":"10.1109/TC.2024.3424263","url":null,"abstract":"The Linux kernel is regularly updated to enhance security, improve performance, and introduce new functionalities. Traditional updating methods typically require rebooting, leading to service disruptions and potential data loss. Live-patching technology dynamically updates the kernel modules without rebooting, ensuring continuous service availability. However, this technique has its drawbacks. Since live-patching alters the original structure of data types, it can no longer utilize base offsets to access the members, imposing considerable overheads. This paper proposes LPAH (Live Patching with Alignment Holes), a live patching system that leverages the fragmented space generated by compile-time alignment for data types, to enable effective live patching updates for security vulnerability fixes, feature enhancements, and user-defined patching tasks. LPAH capitalizes on the relationship between these alignment holes and data objects. This approach ensures efficient access to extended data members while preserving the original data's integrity. This approach allows other functions to remain unaffected by updates and replacements through explicit type casts. Extensive experimental results show that LPAH offers valid and robust live patching for multiple real vulnerabilities in the Linux kernel, without degrading performance. Our method provides an efficient way to install security patches in the Linux kernel, and thus reenforces kernel security.","PeriodicalId":13087,"journal":{"name":"IEEE Transactions on Computers","volume":"73 10","pages":"2434-2448"},"PeriodicalIF":3.6,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thai-Hoang Nguyen;Muhammad Imran;Jaehyuk Choi;Joon-Sung Yang
In-memory Computing (IMC) using Phase Change Memory (PCM) has proven to be effective for efficient processing of Deep Neural Networks (DNNs). However, with the use of multi-level cell PCM (MLC-PCM) in NVMs-based accelerators, errors due to resistance drift in MLC-PCM can severely degrade the DNNs accuracy. In this paper, an analysis of the impact of resistance drift errors on accuracy of MLC-PCM based DNN accelerator shows that the drift errors alone can significantly impact the accuracy. This paper proposes Hydra, which is a hybrid resistance drift resilient architecture for MLC-PCM based DNN accelerators which use IMC for efficient computations. Hydra utilizes Tri-level cell PCM, which has a negligible resistance drift error rate, to store the critical bits of DNNs parameters and MLC-PCM (4-level cell), which has a higher error rate (but offers more storage density), for the non-critical bits. Experimental results on various DNN architectures, configurations and datasets show that, with the presence of resistance drift errors in PCM, Hydra can maintain the baseline accuracy of DNNs for up to 1 year (resistance drift is time-dependent), whereas conventional drift tolerance techniques lead to a significant accuracy drop in just a few seconds.
{"title":"HYDRA: A Hybrid Resistance Drift Resilient Architecture for Phase Change Memory-Based Neural Network Accelerators","authors":"Thai-Hoang Nguyen;Muhammad Imran;Jaehyuk Choi;Joon-Sung Yang","doi":"10.1109/TC.2024.3404096","DOIUrl":"10.1109/TC.2024.3404096","url":null,"abstract":"In-memory Computing (IMC) using Phase Change Memory (PCM) has proven to be effective for efficient processing of Deep Neural Networks (DNNs). However, with the use of multi-level cell PCM (MLC-PCM) in NVMs-based accelerators, errors due to resistance drift in MLC-PCM can severely degrade the DNNs accuracy. In this paper, an analysis of the impact of resistance drift errors on accuracy of MLC-PCM based DNN accelerator shows that the drift errors alone can significantly impact the accuracy. This paper proposes Hydra, which is a hybrid resistance drift resilient architecture for MLC-PCM based DNN accelerators which use IMC for efficient computations. Hydra utilizes Tri-level cell PCM, which has a negligible resistance drift error rate, to store the critical bits of DNNs parameters and MLC-PCM (4-level cell), which has a higher error rate (but offers more storage density), for the non-critical bits. Experimental results on various DNN architectures, configurations and datasets show that, with the presence of resistance drift errors in PCM, Hydra can maintain the baseline accuracy of DNNs for up to 1 year (resistance drift is time-dependent), whereas conventional drift tolerance techniques lead to a significant accuracy drop in just a few seconds.","PeriodicalId":13087,"journal":{"name":"IEEE Transactions on Computers","volume":"73 9","pages":"2123-2135"},"PeriodicalIF":3.6,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Video analytics at mobile edge servers offers significant benefits like reduced response time and enhanced privacy. However, guaranteeing various quality-of-service (QoS) requirements of dynamic video analysis requests on heterogeneous edge devices remains challenging. In this paper, we propose a scalable online video analytics scheme, called Novas, which automatically makes precise service configuration adjustments upon constant video content changes. Specifically, Novas leverages the filtered confidence sum and a two-window t-test to online detect accuracy fluctuations without ground truth information. In such cases, Novas efficiently estimates the performance of all potential service configurations through a singular value decomposition (SVD)-based collaborative filtering method. Finally, given the NP-hardness of the optimal scheduling problem, a heuristic scheduling strategy that maximizes the minimum remaining resources is devised to schedule the most suitable configurations to servers for execution. We evaluate the effectiveness of Novas through extensive hybrid experiments conducted on a dedicated testbed. Results show that Novas can achieve a substantial over 27 $times$ improvement in satisfying the accuracy requirements compared with existing methods adopting fixed configurations, while ensuring latency requirements. Moreover, Novas improves the goodput of the system by an average of 37.86% compared to existing state-of-the-art scheduling solutions.
移动边缘服务器上的视频分析具有显著优势，如缩短响应时间和增强隐私保护。然而，在异构边缘设备上保证动态视频分析请求的各种服务质量（QoS）要求仍然具有挑战性。在本文中，我们提出了一种名为 Novas 的可扩展在线视频分析方案，它能在视频内容不断变化时自动进行精确的服务配置调整。具体来说，Novas 利用滤波置信度总和和双窗口 t 检验来在线检测精度波动，而无需地面实况信息。在这种情况下，Novas 通过基于奇异值分解（SVD）的协同过滤方法，有效地估算出所有潜在服务配置的性能。最后，考虑到最优调度问题的 NP 难度，我们设计了一种启发式调度策略，最大限度地减少剩余资源，从而将最合适的配置调度到服务器上执行。我们在专用测试平台上进行了广泛的混合实验，评估了 Novas 的有效性。结果表明，与采用固定配置的现有方法相比，Novas 在满足精度要求方面可实现超过 27 美元/次的大幅改进，同时还能确保延迟要求。此外，与现有的最先进调度解决方案相比，Novas 还能将系统的吞吐量平均提高 37.86%。
{"title":"Novas: Tackling Online Dynamic Video Analytics With Service Adaptation at Mobile Edge Servers","authors":"Liang Zhang;Hongzi Zhu;Wen Fei;Yunzhe Li;Mingjin Zhang;Jiannong Cao;Minyi Guo","doi":"10.1109/TC.2024.3416675","DOIUrl":"10.1109/TC.2024.3416675","url":null,"abstract":"Video analytics at mobile edge servers offers significant benefits like reduced response time and enhanced privacy. However, guaranteeing various quality-of-service (QoS) requirements of dynamic video analysis requests on heterogeneous edge devices remains challenging. In this paper, we propose a scalable online video analytics scheme, called Novas, which automatically makes precise service configuration adjustments upon constant video content changes. Specifically, Novas leverages the filtered confidence sum and a two-window t-test to online detect accuracy fluctuations without ground truth information. In such cases, Novas efficiently estimates the performance of all potential service configurations through a singular value decomposition (SVD)-based collaborative filtering method. Finally, given the NP-hardness of the optimal scheduling problem, a heuristic scheduling strategy that maximizes the minimum remaining resources is devised to schedule the most suitable configurations to servers for execution. We evaluate the effectiveness of Novas through extensive hybrid experiments conducted on a dedicated testbed. Results show that Novas can achieve a substantial over 27\u0000<inline-formula><tex-math>$times$</tex-math></inline-formula>\u0000 improvement in satisfying the accuracy requirements compared with existing methods adopting fixed configurations, while ensuring latency requirements. Moreover, Novas improves the goodput of the system by an average of 37.86% compared to existing state-of-the-art scheduling solutions.","PeriodicalId":13087,"journal":{"name":"IEEE Transactions on Computers","volume":"73 9","pages":"2220-2232"},"PeriodicalIF":3.6,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a common defense against side-channel attacks, random delay insertion introduces noise into the executive flow of encryption, which increases attack complexity. Accordingly, various techniques are exploited to mitigate the defense effect of such insertions. As an advanced mathematical technique, wavelet analysis is considered to be a more effective technology according to its detailed and comprehensive interpretation of signals. In this paper, we propose a unified and fully automated wavelet-based attack framework (denoted as UWAF ), whose data processing is kept within one unified wavelet domain, with three enhanced components: denoising, alignment and key extraction. We put forward a new idea of combining machine learning with wavelet analysis to realize the full automation of the program for attack framework, rendering it possible to search exhaustively for the optimal combination of parameter settings in wavelet transform. Our proposal finds a new setting of wavelet parameters that have not been exploited ever before and achieves the performance enhancement for about 20 times fewer traces required for successful key recovery. UWAF is compared with several mainstream attack frameworks. Experimental results show that it outperforms those counterparts, and can be considered as an effective framework-level solution to defeat the countermeasure of random delay insertion.
{"title":"A Unified and Fully Automated Framework for Wavelet-Based Attacks on Random Delay","authors":"Qianmei Wu;Fan Zhang;Shize Guo;Kun Yang;Haoting Shen","doi":"10.1109/TC.2024.3416682","DOIUrl":"10.1109/TC.2024.3416682","url":null,"abstract":"As a common defense against side-channel attacks, random delay insertion introduces noise into the executive flow of encryption, which increases attack complexity. Accordingly, various techniques are exploited to mitigate the defense effect of such insertions. As an advanced mathematical technique, wavelet analysis is considered to be a more effective technology according to its detailed and comprehensive interpretation of signals. In this paper, we propose a unified and fully automated wavelet-based attack framework (denoted as \u0000<bold>UWAF</b>\u0000), whose data processing is kept within one unified wavelet domain, with three enhanced components: denoising, alignment and key extraction. We put forward a new idea of combining machine learning with wavelet analysis to realize the full automation of the program for attack framework, rendering it possible to search exhaustively for the optimal combination of parameter settings in wavelet transform. Our proposal finds a new setting of wavelet parameters that have not been exploited ever before and achieves the performance enhancement for about 20 times fewer traces required for successful key recovery. \u0000<bold>UWAF</b>\u0000 is compared with several mainstream attack frameworks. Experimental results show that it outperforms those counterparts, and can be considered as an effective framework-level solution to defeat the countermeasure of random delay insertion.","PeriodicalId":13087,"journal":{"name":"IEEE Transactions on Computers","volume":"73 9","pages":"2206-2219"},"PeriodicalIF":3.6,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Charles Gouert;Dimitris Mouris;Nektarios Georgios Tsoutsos
Fully homomorphic encryption (FHE) has become progressively more viable in the years since its original inception in 2009. At the same time, leveraging state-of-the-art schemes in an efficient way for general computation remains prohibitively difficult for the average programmer. In this work, we introduce a new design for a fully homomorphic processor, dubbed Juliet, to enable faster operations on encrypted data using the state-of-the-art TFHE and cuFHE libraries for both CPU and GPU evaluation. To improve usability, we define an expressive assembly language and instruction set architecture (ISA) judiciously designed for end-to-end encrypted computation. We demonstrate Juliet's capabilities with a broad range of realistic benchmarks including cryptographic algorithms, such as the lightweight ciphers Simon and Speck , as well as logistic regression (LR) inference and matrix multiplication.
全同态加密（FHE）自 2009 年诞生以来，已逐渐变得更加可行。与此同时，对于普通程序员来说，以高效的方式利用最先进的方案进行一般计算仍然非常困难。在这项工作中，我们介绍了一种全新设计的全同态处理器（命名为 Juliet），利用最先进的 TFHE 和 cuFHE 库，在 CPU 和 GPU 评估中实现更快的加密数据运算。为了提高可用性，我们定义了一种富有表现力的汇编语言和指令集架构 (ISA)，该架构专为端到端加密计算而精心设计。我们利用各种实际基准来展示 Juliet 的能力，包括加密算法（如轻量级密码 Simon 和 Speck）以及逻辑回归（LR）推理和矩阵乘法。
{"title":"Juliet: A Configurable Processor for Computing on Encrypted Data","authors":"Charles Gouert;Dimitris Mouris;Nektarios Georgios Tsoutsos","doi":"10.1109/TC.2024.3416752","DOIUrl":"10.1109/TC.2024.3416752","url":null,"abstract":"Fully homomorphic encryption (FHE) has become progressively more viable in the years since its original inception in 2009. At the same time, leveraging state-of-the-art schemes in an efficient way for general computation remains prohibitively difficult for the average programmer. In this work, we introduce a new design for a fully homomorphic processor, dubbed Juliet, to enable faster operations on encrypted data using the state-of-the-art TFHE and cuFHE libraries for both CPU and GPU evaluation. To improve usability, we define an expressive assembly language and instruction set architecture (ISA) judiciously designed for end-to-end encrypted computation. We demonstrate Juliet's capabilities with a broad range of realistic benchmarks including cryptographic algorithms, such as the lightweight ciphers \u0000<sc>Simon</small>\u0000 and \u0000<sc>Speck</small>\u0000, as well as logistic regression (LR) inference and matrix multiplication.","PeriodicalId":13087,"journal":{"name":"IEEE Transactions on Computers","volume":"73 9","pages":"2335-2349"},"PeriodicalIF":3.6,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
3D tori are significant interconnection architectures in building supercomputers and parallel computing systems. Due to the rapid growth of edge faults and the crucial role of path structures in large-scale distributed systems, fault-tolerant path embedding and correlated issues have drawn widespread researches. However, existing path embedding methods are based on traditional fault models, allowing all faults to be near the same node, so they usually only focus on theoretical proof and generate linear fault-tolerance related to dimension $n$ . In order to improve the fault-tolerance of 3D torus, we first propose a novel conditional fault model called the Locally Faulty Block model (LFB model). On the basis of this model, the Hamiltonian paths with large-scale edge defects in torus are investigated. After that, we construct an Hamiltonian path embedding algorithm HP-LFB into torus with $O(N)$ under the LFB model, where $N$ is the number of nodes in torus. Furthermore, we present an adaptive routing algorithm HoeFA, which is based on the method of distance vector to limit the use of virtual channels (VCs). We also make a comparison with state-of-the-art schemes, indicating that our scheme enhance other comprehensive results. The experiment indicated that HP-LFB can sustain the dynamic degradation of the batting average of establishing Hamiltonian paths, with the added faulty edges exceeding fault-tolerance.
{"title":"Efficient Fault-Tolerant Path Embedding for 3D Torus Network Using Locally Faulty Blocks","authors":"Weibei Fan;Fu Xiao;Mengjie Lv;Lei Han;Shui Yu","doi":"10.1109/TC.2024.3416695","DOIUrl":"https://doi.org/10.1109/TC.2024.3416695","url":null,"abstract":"3D tori are significant interconnection architectures in building supercomputers and parallel computing systems. Due to the rapid growth of edge faults and the crucial role of path structures in large-scale distributed systems, fault-tolerant path embedding and correlated issues have drawn widespread researches. However, existing path embedding methods are based on traditional fault models, allowing all faults to be near the same node, so they usually only focus on theoretical proof and generate linear fault-tolerance related to dimension \u0000<inline-formula><tex-math>$n$</tex-math></inline-formula>\u0000. In order to improve the fault-tolerance of 3D torus, we first propose a novel conditional fault model called the Locally Faulty Block model (LFB model). On the basis of this model, the Hamiltonian paths with large-scale edge defects in torus are investigated. After that, we construct an Hamiltonian path embedding algorithm HP-LFB into torus with \u0000<inline-formula><tex-math>$O(N)$</tex-math></inline-formula>\u0000 under the LFB model, where \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000 is the number of nodes in torus. Furthermore, we present an adaptive routing algorithm HoeFA, which is based on the method of distance vector to limit the use of virtual channels (VCs). We also make a comparison with state-of-the-art schemes, indicating that our scheme enhance other comprehensive results. The experiment indicated that HP-LFB can sustain the dynamic degradation of the batting average of establishing Hamiltonian paths, with the added faulty edges exceeding fault-tolerance.","PeriodicalId":13087,"journal":{"name":"IEEE Transactions on Computers","volume":"73 9","pages":"2305-2319"},"PeriodicalIF":3.6,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141966295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuxuan Chen;Zhen Zhang;Yuhui Deng;Geyong Min;Lin Cui
Virtual machine (VM) consolidation strategies are widely used in cloud data centers (CDC) to optimize resource utilization and reduce total energy consumption. Although existing strategies consider current and future resource utilization, the impact of sudden bursts in historical resource utilization on the hosts has been underestimated in uncertain future periods. Insufficient analysis of historical resource utilization may increase the risk of host overloading and Service Level Agreement Violation (SLAV). By defining historical and future trends based on resource utilization, we propose a novel combined trend VM consolidation (CTVMC) strategy which can effectively reduce energy consumption and SLAV. The VMs with the largest combined trend are selected for migration to prevent host overloading. Based on the temporal locality and prediction technique, CTVMC then employs the past, present, and future resource utilization to filter candidate hosts, and identifies the most complementary host to place VM using combined trends. We conduct extensive simulation experiments with PlanetLab Trace and Google Cluster Trace in the CloudSim simulator. Compared with the well-known strategies, CTVMC strategy using the PlanetLab Trace can reduce the number of migrations by over 72.39%, SLAV by over 75.85%, and ESV (a combined metric that judges the trade-off between energy consumption and SLAV) by over 81.54%. According to the Google Cluster Trace, our strategy can reduce the number of migrations by over 61.51%, SLAV by over 37.37%, and ESV by over 35.30%.
{"title":"A Combined Trend Virtual Machine Consolidation Strategy for Cloud Data Centers","authors":"Yuxuan Chen;Zhen Zhang;Yuhui Deng;Geyong Min;Lin Cui","doi":"10.1109/TC.2024.3416734","DOIUrl":"10.1109/TC.2024.3416734","url":null,"abstract":"Virtual machine (VM) consolidation strategies are widely used in cloud data centers (CDC) to optimize resource utilization and reduce total energy consumption. Although existing strategies consider current and future resource utilization, the impact of sudden bursts in historical resource utilization on the hosts has been underestimated in uncertain future periods. Insufficient analysis of historical resource utilization may increase the risk of host overloading and Service Level Agreement Violation (SLAV). By defining historical and future trends based on resource utilization, we propose a novel combined trend VM consolidation (CTVMC) strategy which can effectively reduce energy consumption and SLAV. The VMs with the largest combined trend are selected for migration to prevent host overloading. Based on the temporal locality and prediction technique, CTVMC then employs the past, present, and future resource utilization to filter candidate hosts, and identifies the most complementary host to place VM using combined trends. We conduct extensive simulation experiments with PlanetLab Trace and Google Cluster Trace in the CloudSim simulator. Compared with the well-known strategies, CTVMC strategy using the PlanetLab Trace can reduce the number of migrations by over 72.39%, SLAV by over 75.85%, and ESV (a combined metric that judges the trade-off between energy consumption and SLAV) by over 81.54%. According to the Google Cluster Trace, our strategy can reduce the number of migrations by over 61.51%, SLAV by over 37.37%, and ESV by over 35.30%.","PeriodicalId":13087,"journal":{"name":"IEEE Transactions on Computers","volume":"73 9","pages":"2150-2164"},"PeriodicalIF":3.6,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bo Ding;Wei Tong;Yu Hua;Zhangyu Chen;Xueliang Wei;Dan Feng
Persistent memory (PM) is promising to be the next-generation storage device with better I/O performance. Since the traditional I/O path is too lengthy to drive PM featuring low latency and high bandwidth, prior works proposed memory-mapped I/O (MMIO) to shorten the I/O path to PM. However, native MMIO directly maps files into the user address space, which puts files at risk of being corrupted by scribbles and non-atomic I/O interfaces, causing serious reliability issues. To address these issues, we propose RMMIO, an efficient user-space library that provides reliable MMIO for PM systems. RMMIO provides atomic I/O interfaces and lightweight snapshots to ensure the reliability of MMIO. Compared with existing schemes, RMMIO mitigates additional writes and extra software overheads caused by reliability guarantees, thus achieving MMIO-like performance. In addition, we also propose an automatic snapshot with efficient memory management for RMMIO to minimize data loss incurred by reliability issues. The experimental results of microbenchmarks show that RMMIO achieves 8.49x and 2.31x higher throughput than ext4-DAX and the state-of-the-art MMIO-based scheme, respectively, while ensuring data reliability. The real-world application accelerated by RMMIO achieves at most 7.06x higher throughput than that of ext4-DAX.
{"title":"Enabling Reliable Memory-Mapped I/O With Auto-Snapshot for Persistent Memory Systems","authors":"Bo Ding;Wei Tong;Yu Hua;Zhangyu Chen;Xueliang Wei;Dan Feng","doi":"10.1109/TC.2024.3416683","DOIUrl":"10.1109/TC.2024.3416683","url":null,"abstract":"Persistent memory (PM) is promising to be the next-generation storage device with better I/O performance. Since the traditional I/O path is too lengthy to drive PM featuring low latency and high bandwidth, prior works proposed memory-mapped I/O (MMIO) to shorten the I/O path to PM. However, native MMIO directly maps files into the user address space, which puts files at risk of being corrupted by scribbles and non-atomic I/O interfaces, causing serious reliability issues. To address these issues, we propose RMMIO, an efficient user-space library that provides reliable MMIO for PM systems. RMMIO provides atomic I/O interfaces and lightweight snapshots to ensure the reliability of MMIO. Compared with existing schemes, RMMIO mitigates additional writes and extra software overheads caused by reliability guarantees, thus achieving MMIO-like performance. In addition, we also propose an automatic snapshot with efficient memory management for RMMIO to minimize data loss incurred by reliability issues. The experimental results of microbenchmarks show that RMMIO achieves 8.49x and 2.31x higher throughput than ext4-DAX and the state-of-the-art MMIO-based scheme, respectively, while ensuring data reliability. The real-world application accelerated by RMMIO achieves at most 7.06x higher throughput than that of ext4-DAX.","PeriodicalId":13087,"journal":{"name":"IEEE Transactions on Computers","volume":"73 9","pages":"2290-2304"},"PeriodicalIF":3.6,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bit-Trojan attacks based on Bit-Flip Attacks (BFAs) have emerged as severe threats to Deep Neural Networks (DNNs) deployed in safety-critical systems since they can inject Trojans during the model deployment stage without accessing training supply chains. Existing works are mainly devoted to improving the executability of Bit-Trojan attacks, while seriously ignoring the concerns on evasiveness. In this paper, we propose a highly Evasive Targeted Bit-Trojan (ETBT) with evasiveness improvements from three aspects, i.e., reducing the number of bit-flips (improving executability), smoothing activation distribution, and reducing accuracy fluctuation. Specifically, key neuron extraction is utilized to identify essential neurons from DNNs precisely and decouple the key neurons between different classes, thus improving the evasiveness regarding accuracy fluctuation and executability. Additionally, activation-constrained trigger generation is devised to eliminate the differences between activation distributions of Trojaned and clean models, which enhances evasiveness from the perspective of activation distribution. Ultimately, the strategy of constrained target bits search is designed to reduce bit-flip numbers, directly benefits the evasiveness of ETBT. Benchmark-based experiments are conducted to evaluate the superiority of ETBT. Compared with existing works, ETBT can significantly improve evasiveness-relevant performances with much lower computation overheads, better robustness, and generalizability. Our code is released at https://github.com/bluefier/ETBT .
{"title":"Highly Evasive Targeted Bit-Trojan on Deep Neural Networks","authors":"Lingxin Jin;Wei Jiang;Jinyu Zhan;Xiangyu Wen","doi":"10.1109/TC.2024.3416705","DOIUrl":"10.1109/TC.2024.3416705","url":null,"abstract":"Bit-Trojan attacks based on Bit-Flip Attacks (BFAs) have emerged as severe threats to Deep Neural Networks (DNNs) deployed in safety-critical systems since they can inject Trojans during the model deployment stage without accessing training supply chains. Existing works are mainly devoted to improving the executability of Bit-Trojan attacks, while seriously ignoring the concerns on evasiveness. In this paper, we propose a highly Evasive Targeted Bit-Trojan (ETBT) with evasiveness improvements from three aspects, i.e., reducing the number of bit-flips (improving executability), smoothing activation distribution, and reducing accuracy fluctuation. Specifically, key neuron extraction is utilized to identify essential neurons from DNNs precisely and decouple the key neurons between different classes, thus improving the evasiveness regarding accuracy fluctuation and executability. Additionally, activation-constrained trigger generation is devised to eliminate the differences between activation distributions of Trojaned and clean models, which enhances evasiveness from the perspective of activation distribution. Ultimately, the strategy of constrained target bits search is designed to reduce bit-flip numbers, directly benefits the evasiveness of ETBT. Benchmark-based experiments are conducted to evaluate the superiority of ETBT. Compared with existing works, ETBT can significantly improve evasiveness-relevant performances with much lower computation overheads, better robustness, and generalizability. Our code is released at \u0000<uri>https://github.com/bluefier/ETBT</uri>\u0000.","PeriodicalId":13087,"journal":{"name":"IEEE Transactions on Computers","volume":"73 9","pages":"2350-2363"},"PeriodicalIF":3.6,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep Convolution Neural Networks (CNNs) have become the cornerstone of image classification, but the emergence of adversarial image attacks brings serious security risks to CNN-based applications. As a local perturbation attack, the border attack can achieve high success rates by only modifying the pixels around the border of an image, which is a novel attack perspective. However, existing border attacks have shortcomings in stealthiness and are easily detected. In this article, we propose a novel stealthy border attack method based on deep feature alignment. Specifically, we propose a deep feature alignment algorithm based on style transfer to guarantee the stealthiness of adversarial borders. The algorithm takes the deep feature difference between the adversarial and the original borders as the stealthiness loss and thus ensures good stealthiness of the generated adversarial images. To ensure high attack success rates simultaneously, we apply cross entropy to design the targeted attack loss and use margin loss as well as Leaky ReLU to design the untargeted attack loss. Experiments show that the structural similarity between the generated adversarial images and the original images is 8.8% higher than the state-of-art border attack method, indicating that our proposed adversarial images have better stealthiness. At the same time, the success rate of our attack in the face of defense methods is much higher, which is about four times that of the state-of-art border attack under the adversarial training defense.
{"title":"Hiding in Plain Sight: Adversarial Attack via Style Transfer on Image Borders","authors":"Haiyan Zhang;Xinghua Li;Jiawei Tang;Chunlei Peng;Yunwei Wang;Ning Zhang;Yingbin Miao;Ximeng Liu;Kim-Kwang Raymond Choo","doi":"10.1109/TC.2024.3416761","DOIUrl":"10.1109/TC.2024.3416761","url":null,"abstract":"Deep Convolution Neural Networks (CNNs) have become the cornerstone of image classification, but the emergence of adversarial image attacks brings serious security risks to CNN-based applications. As a local perturbation attack, the border attack can achieve high success rates by only modifying the pixels around the border of an image, which is a novel attack perspective. However, existing border attacks have shortcomings in stealthiness and are easily detected. In this article, we propose a novel stealthy border attack method based on deep feature alignment. Specifically, we propose a deep feature alignment algorithm based on style transfer to guarantee the stealthiness of adversarial borders. The algorithm takes the deep feature difference between the adversarial and the original borders as the stealthiness loss and thus ensures good stealthiness of the generated adversarial images. To ensure high attack success rates simultaneously, we apply cross entropy to design the targeted attack loss and use margin loss as well as Leaky ReLU to design the untargeted attack loss. Experiments show that the structural similarity between the generated adversarial images and the original images is 8.8% higher than the state-of-art border attack method, indicating that our proposed adversarial images have better stealthiness. At the same time, the success rate of our attack in the face of defense methods is much higher, which is about four times that of the state-of-art border attack under the adversarial training defense.","PeriodicalId":13087,"journal":{"name":"IEEE Transactions on Computers","volume":"73 10","pages":"2405-2419"},"PeriodicalIF":3.6,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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