arXiv - CS - Operating Systems最新文献_第7页

Dynamic Voltage and Frequency Scaling for Intermittent Computing 间歇计算的动态电压和频率缩放

arXiv - CS - Operating Systems Pub Date : 2024-01-15 DOI: arxiv-2401.08710

Andrea Maioli, Kevin A. Quinones, Saad Ahmed, Muhammad H. Alizai, Luca Mottola

{"title":"Dynamic Voltage and Frequency Scaling for Intermittent Computing","authors":"Andrea Maioli, Kevin A. Quinones, Saad Ahmed, Muhammad H. Alizai, Luca Mottola","doi":"arxiv-2401.08710","DOIUrl":"https://doi.org/arxiv-2401.08710","url":null,"abstract":"We present hardware/software techniques to intelligently regulate supply\u0000voltage and clock frequency of intermittently-computing devices. These devices\u0000rely on ambient energy harvesting to power their operation and small capacitors\u0000as energy buffers. Statically setting their clock frequency fails to capture\u0000the unique relations these devices expose between capacitor voltage, energy\u0000efficiency at a given operating frequency, and the corresponding operating\u0000range. Existing dynamic voltage and frequency scaling techniques are also\u0000largely inapplicable due to extreme energy scarcity and peculiar hardware\u0000features. We introduce two hardware/software co-designs that accommodate the\u0000distinct hardware features and function within a constrained energy envelope,\u0000offering varied trade-offs and functionalities. Our experimental evaluation\u0000combines tests on custom-manufactured hardware and detailed emulation\u0000experiments. The data gathered indicate that our approaches result in up to\u00003.75x reduced energy consumption and 12x swifter execution times compared to\u0000the considered baselines, all while utilizing smaller capacitors to accomplish\u0000identical workloads.","PeriodicalId":501333,"journal":{"name":"arXiv - CS - Operating Systems","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139500450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

When eBPF Meets Machine Learning: On-the-fly OS Kernel Compartmentalization 当 eBPF 遇到机器学习：实时操作系统内核分区

arXiv - CS - Operating Systems Pub Date : 2024-01-11 DOI: arxiv-2401.05641

Zicheng Wang, Tiejin Chen, Qinrun Dai, Yueqi Chen, Hua Wei, Qingkai Zeng

引用次数: 0

RASP for LSASS: Preventing Mimikatz-Related Attacks LSASS 的 RASP：防止与 Mimikatz 相关的攻击

arXiv - CS - Operating Systems Pub Date : 2023-12-30 DOI: arxiv-2401.00316

Anna Revazova, Igor Korkin

引用次数: 0

ALPC Is In Danger: ALPChecker Detects Spoofing and Blinding ALPC 处于危险之中ALPChecker 检测欺骗和欺骗行为

arXiv - CS - Operating Systems Pub Date : 2023-12-30 DOI: arxiv-2401.01376

Anastasiia Kropova, Igor Korkin

{"title":"ALPC Is In Danger: ALPChecker Detects Spoofing and Blinding","authors":"Anastasiia Kropova, Igor Korkin","doi":"arxiv-2401.01376","DOIUrl":"https://doi.org/arxiv-2401.01376","url":null,"abstract":"The purpose of this study is to evaluate the possibility of implementing an\u0000attack on ALPC connection in the Windows operating system through the kernel\u0000without closing the connection covertly from programs and the operating system\u0000and to propose a method of protection against this type of attacks.\u0000Asynchronous Local Procedure Call technology (ALPC) is used in various Windows\u0000information protection systems, including antivirus systems (AV) and Endpoint\u0000Detection and Response systems (EDR). To ensure the concealment of malicious\u0000software, attackers need to disrupt the operation of AV, EDR tools, which in\u0000turn can be achieved by destructive impact on the components of the ALPC\u0000technology. Examples of such attacks already exist and are covered in this\u0000paper. To counteract such new threats, it is necessary to advance the\u0000improvement of information security systems and the ALPC security research was\u0000conducted. The most difficult case, Windows kernel driver attack, was\u0000considered. Three attacks on the ALPC connection were carried out, based on\u0000changing the ALPC structures in the kernel memory, which led to creation of\u0000illegitimate connections in the system and the disruption of correct\u0000connections. ALPChecker protection tool has been developed. The tool was\u0000successfully tested on three demonstrated attacks.","PeriodicalId":501333,"journal":{"name":"arXiv - CS - Operating Systems","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139096135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attention, Distillation, and Tabularization: Towards Practical Neural Network-Based Prefetching 关注、提炼和表格化：基于神经网络的实用预取

arXiv - CS - Operating Systems Pub Date : 2023-12-23 DOI: arxiv-2401.06362

Pengmiao Zhang, Neelesh Gupta, Rajgopal Kannan, Viktor K. Prasanna

{"title":"Attention, Distillation, and Tabularization: Towards Practical Neural Network-Based Prefetching","authors":"Pengmiao Zhang, Neelesh Gupta, Rajgopal Kannan, Viktor K. Prasanna","doi":"arxiv-2401.06362","DOIUrl":"https://doi.org/arxiv-2401.06362","url":null,"abstract":"Attention-based Neural Networks (NN) have demonstrated their effectiveness in\u0000accurate memory access prediction, an essential step in data prefetching.\u0000However, the substantial computational overheads associated with these models\u0000result in high inference latency, limiting their feasibility as practical\u0000prefetchers. To close the gap, we propose a new approach based on\u0000tabularization that significantly reduces model complexity and inference\u0000latency without sacrificing prediction accuracy. Our novel tabularization\u0000methodology takes as input a distilled, yet highly accurate attention-based\u0000model for memory access prediction and efficiently converts its expensive\u0000matrix multiplications into a hierarchy of fast table lookups. As an exemplar\u0000of the above approach, we develop DART, a prefetcher comprised of a simple\u0000hierarchy of tables. With a modest 0.09 drop in F1-score, DART reduces 99.99%\u0000of arithmetic operations from the large attention-based model and 91.83% from\u0000the distilled model. DART accelerates the large model inference by 170x and the\u0000distilled model by 9.4x. DART has comparable latency and storage costs as\u0000state-of-the-art rule-based prefetcher BO but surpasses it by 6.1% in IPC\u0000improvement, resulting in a 37.6% speed-up. DART outperforms state-of-the-art\u0000NN-based prefetchers TransFetch by 33.1% and Voyager by 37.2% in terms of IPC\u0000improvement, primarily due to its low prefetching latency.","PeriodicalId":501333,"journal":{"name":"arXiv - CS - Operating Systems","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139470795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU PowerInfer：使用消费级 GPU 快速处理大型语言模型

arXiv - CS - Operating Systems Pub Date : 2023-12-16 DOI: arxiv-2312.12456

Yixin Song, Zeyu Mi, Haotong Xie, Haibo Chen

{"title":"PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU","authors":"Yixin Song, Zeyu Mi, Haotong Xie, Haibo Chen","doi":"arxiv-2312.12456","DOIUrl":"https://doi.org/arxiv-2312.12456","url":null,"abstract":"This paper introduces PowerInfer, a high-speed Large Language Model (LLM)\u0000inference engine on a personal computer (PC) equipped with a single\u0000consumer-grade GPU. The key underlying the design of PowerInfer is exploiting\u0000the high locality inherent in LLM inference, characterized by a power-law\u0000distribution in neuron activation. This distribution indicates that a small\u0000subset of neurons, termed hot neurons, are consistently activated across\u0000inputs, while the majority, cold neurons, vary based on specific inputs.\u0000PowerInfer exploits such an insight to design a GPU-CPU hybrid inference\u0000engine: hot-activated neurons are preloaded onto the GPU for fast access, while\u0000cold-activated neurons are computed on the CPU, thus significantly reducing GPU\u0000memory demands and CPU-GPU data transfers. PowerInfer further integrates\u0000adaptive predictors and neuron-aware sparse operators, optimizing the\u0000efficiency of neuron activation and computational sparsity. Evaluation shows\u0000that PowerInfer attains an average token generation rate of 13.20 tokens/s,\u0000with a peak of 29.08 tokens/s, across various LLMs (including OPT-175B) on a\u0000single NVIDIA RTX 4090 GPU, only 18% lower than that achieved by a top-tier\u0000server-grade A100 GPU. This significantly outperforms llama.cpp by up to 11.69x\u0000while retaining model accuracy.","PeriodicalId":501333,"journal":{"name":"arXiv - CS - Operating Systems","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138825467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On a Foundation Model for Operating Systems 关于操作系统的基础模型

arXiv - CS - Operating Systems Pub Date : 2023-12-13 DOI: arxiv-2312.07813

Divyanshu Saxena, Nihal Sharma, Donghyun Kim, Rohit Dwivedula, Jiayi Chen, Chenxi Yang, Sriram Ravula, Zichao Hu, Aditya Akella, Sebastian Angel, Joydeep Biswas, Swarat Chaudhuri, Isil Dillig, Alex Dimakis, P. Brighten Godfrey, Daehyeok Kim, Chris Rossbach, Gang Wang

引用次数: 0

Security, extensibility, and redundancy in the Metabolic Operating System 新陈代谢操作系统的安全性、可扩展性和冗余性

arXiv - CS - Operating Systems Pub Date : 2023-12-11 DOI: arxiv-2401.01357

Samuel T. King

{"title":"Security, extensibility, and redundancy in the Metabolic Operating System","authors":"Samuel T. King","doi":"arxiv-2401.01357","DOIUrl":"https://doi.org/arxiv-2401.01357","url":null,"abstract":"People living with Type 1 Diabetes (T1D) lose the ability to produce insulin\u0000naturally. To compensate, they inject synthetic insulin. One common way to\u0000inject insulin is through automated insulin delivery systems, which use sensors\u0000to monitor their metabolic state and an insulin pump device to adjust insulin\u0000to adapt. In this paper, we present the Metabolic Operating System, a new automated\u0000insulin delivery system that we designed from the ground up using security\u0000first principles. From an architecture perspective, we apply separation\u0000principles to simplify the core system and isolate non-critical functionality\u0000from the core closed-loop algorithm. From an algorithmic perspective, we\u0000evaluate trends in insulin technology and formulate a simple, but effective,\u0000algorithm given the state-of-the-art. From a safety perspective, we build in\u0000multiple layers of redundancy to ensure that the person using our system\u0000remains safe. Fundamentally, this paper is a paper on real-world experiences building and\u0000running an automated insulin delivery system. We report on the design\u0000iterations we make based on experiences working with one individual using our\u0000system. Our evaluation shows that an automated insulin delivery system built\u0000from the ground up using security first principles can still help manage T1D\u0000effectively. Our source code is open source and available on GitHub (link omitted).","PeriodicalId":501333,"journal":{"name":"arXiv - CS - Operating Systems","volume":"215 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139096273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

KEN: Kernel Extensions using Natural Language KEN：使用自然语言的内核扩展

arXiv - CS - Operating Systems Pub Date : 2023-12-09 DOI: arxiv-2312.05531

Yusheng Zheng, Yiwei Yang, Maolin Chen, Andrew Quinn

{"title":"KEN: Kernel Extensions using Natural Language","authors":"Yusheng Zheng, Yiwei Yang, Maolin Chen, Andrew Quinn","doi":"arxiv-2312.05531","DOIUrl":"https://doi.org/arxiv-2312.05531","url":null,"abstract":"The ability to modify and extend an operating system is an important feature\u0000for improving a system's security, reliability, and performance. The extended\u0000Berkeley Packet Filters (eBPF) ecosystem has emerged as the standard mechanism\u0000for extending the Linux kernel and has recently been ported to Windows. eBPF\u0000programs inject new logic into the kernel that the system will execute before\u0000or after existing logic. While the eBPF ecosystem provides a flexible mechanism\u0000for kernel extension, it is difficult for developers to write eBPF programs\u0000today. An eBPF developer must have deep knowledge of the internals of the\u0000operating system to determine where to place logic and cope with programming\u0000limitations on the control flow and data accesses of their eBPF program\u0000enforced by the eBPF verifier. This paper presents KEN, an alternative\u0000framework that alleviates the difficulty of writing an eBPF program by allowing\u0000Kernel Extensions to be written in Natural language. KEN uses recent advances\u0000in large language models (LLMs) to synthesize an eBPF program given a user's\u0000English language prompt. To ensure that LLM's output is semantically equivalent\u0000to the user's prompt, KEN employs a combination of LLM-empowered program\u0000comprehension, symbolic execution, and a series of feedback loops. KEN's key\u0000novelty is the combination of these techniques. In particular, the system uses\u0000symbolic execution in a novel structure that allows it to combine the results\u0000of program synthesis and program comprehension and build on the recent success\u0000that LLMs have shown for each of these tasks individually. To evaluate KEN, we\u0000developed a new corpus of natural language prompts for eBPF programs. We show\u0000that KEN produces correct eBPF programs on 80% which is an improvement of a\u0000factor of 2.67 compared to an LLM-empowered program synthesis baseline.","PeriodicalId":501333,"journal":{"name":"arXiv - CS - Operating Systems","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138575611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SYSFLOW: Efficient Execution Platform for IoT Devices SYSFLOW：物联网设备的高效执行平台

arXiv - CS - Operating Systems Pub Date : 2023-12-08 DOI: arxiv-2312.04871

Jun Lu, Zhenya Ma, Yinggang Gao, Ju Ren, Yaoxue Zhang

引用次数: 0