Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems最新文献

Trusted Heterogeneous Disaggregated Architectures 可信异构分解架构

Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems Pub Date : 2023-08-24 DOI: 10.1145/3609510.3609812

Atsushi Koshiba, Felix Gust, Julian Pritzi, Anjo Vahldiek-Oberwagner, Nuno Santos, Pramod Bhatotia

引用次数: 0

Understanding the Security of Linux eBPF Subsystem 了解Linux eBPF子系统的安全性

Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems Pub Date : 2023-08-24 DOI: 10.1145/3609510.3609822

Mohamed Husain Noor Mohamed, Xiaoguang Wang, B. Ravindran

引用次数: 0

ZapRAID: Toward High-Performance RAID for ZNS SSDs via Zone Append 通过Zone Append实现ZNS ssd的高性能RAID

Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems Pub Date : 2023-08-24 DOI: 10.1145/3609510.3609810

Qiuping Wang, P. Lee

引用次数: 0

Towards OS Heterogeneity Aware Cluster Management for HPC 面向操作系统异构的高性能计算集群管理

Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems Pub Date : 2023-08-24 DOI: 10.1145/3609510.3609819

Zhida An, Ding Li, Yao Guo, Guijin Gao, Yuxin Ren, Ning Jia, Xinwei Hu

引用次数: 0

First steps in verifying the seL4 Core Platform 验证seL4核心平台的第一步

Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems Pub Date : 2023-08-24 DOI: 10.1145/3609510.3609821

Mathieu Paturel, Isitha Subasinghe, G. Heiser

引用次数: 0

Family Classification based on Tree Representations for Malware 基于树表示的恶意软件族分类

Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems Pub Date : 2023-08-24 DOI: 10.1145/3609510.3609818

Yang Xu, Zhuotai Chen

{"title":"Family Classification based on Tree Representations for Malware","authors":"Yang Xu, Zhuotai Chen","doi":"10.1145/3609510.3609818","DOIUrl":"https://doi.org/10.1145/3609510.3609818","url":null,"abstract":"Malware classification is helpful for malware detection and analysis. Family classification of malware is a multi-classification task. Many studies have exploited API call sequences as malware features. However, API call sequences do not explicitly express the information about control structures between API calls, which may be useful to represent malware behavior features more accurately. In this paper, we propose a novel malware familial classification method. We model each malware as a Behavioral Tree from API call sequence obtained from dynamic analysis, which describes the control structure between the API calls. To reduce the computational complexity, we capture a set of binary relations, called as Heighted Behavior Relations, from the behavior tree as behavior features of malware. The TF-IDF technology is used to calculate the family behavior features from the behavior features of malware. Then the similarity vector of each malware is constructed based on the similarity between it and all the families. For family classification purpose, the similarity vectors of malware are fed into Naive Bayes algorithm to train a classifier. The experiments on dataset with 10620 malware samples from 43 malware families show that the classification accuracy of our approach is 10% higher than that of the classical methods based on API call sequences.","PeriodicalId":149629,"journal":{"name":"Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121492622","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

Cost-Efficient Serverless Inference Serving with Joint Batching and Multi-Processing 具有联合批处理和多处理的低成本无服务器推理服务

Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems Pub Date : 2023-08-24 DOI: 10.1145/3609510.3609816

Shen Cai, Zhi Zhou, Kongyange Zhao, Xu Chen

{"title":"Cost-Efficient Serverless Inference Serving with Joint Batching and Multi-Processing","authors":"Shen Cai, Zhi Zhou, Kongyange Zhao, Xu Chen","doi":"10.1145/3609510.3609816","DOIUrl":"https://doi.org/10.1145/3609510.3609816","url":null,"abstract":"With the emerging of machine learning, many commercial companies increasingly utilize machine learning inference systems as backend services to improve their products. Serverless computing is a modern paradigm that provides auto-scaling, event-driven services, making it particularly well-suited for various domains, including video stream analysis, IoT serving and machine learning applications. The flexible scaling feature of serverless computing is adept at handling the burstiness of ML workloads. However, despite its compatibility with ML inference tasks, the cost of serverless inference systems remain relatively high in comparison to traditional serving paradigms, primarily due to the under-utilization of CPU resources offered by serverless platforms. To tackle this challenge, we design and deploy a serverless inference serving system that incorporates batching and multi-process mechanisms to enhance cost efficiency. By applying a change-point detection algorithm to manage bursty workloads, it optimizes resource usage and achieves lower costs. We employ an Amazon EC2 server for handling request packaging and running the core Bayesian Optimization algorithm without any prior information. The preliminary system, implemented on AWS Lambda, can significantly reduce expenses and save up to 62% compared to the original serverless inference system.","PeriodicalId":149629,"journal":{"name":"Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129600020","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

Quantifying the Security Profile of Linux Applications 量化Linux应用的安全配置文件

Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems Pub Date : 2023-08-24 DOI: 10.1145/3609510.3609814

Georgios C. Androutsopoulos, Giorgos Kappes, S. Anastasiadis

引用次数: 0

Reducing Attack Surface with Container Transplantation for Lightweight Sandboxing 用容器移植减少轻量级沙箱的攻击面

Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems Pub Date : 2023-08-24 DOI: 10.1145/3609510.3609820

Yuki Nakata, Shintaro Suzuki, Katsuya Matsubara

{"title":"Reducing Attack Surface with Container Transplantation for Lightweight Sandboxing","authors":"Yuki Nakata, Shintaro Suzuki, Katsuya Matsubara","doi":"10.1145/3609510.3609820","DOIUrl":"https://doi.org/10.1145/3609510.3609820","url":null,"abstract":"Containers, which have evolved in Linux primarily, have become a significant trend in the cloud due to their lightweight virtualization and growing convenient ecosystem. However, the laxer isolation of containerization also introduces attack surfaces on the underlying Linux kernel. Unfortunately, combining other virtualizations, such as the traditional VM and interposition by application kernel, for sandboxing could spoil the lightweight and scalable nature of the containers. In this study, we propose another approach to lightweight sandboxing that focuses on the fact that such attackers have mostly assumed containers rely on Linux. It can avert major vulnerability exploits derived from Linux by transplanting Linux containers onto the FreeBSD kernel. Furthermore, it can fortify the isolation by transparently applying \"Capsicum,\" a unique sandbox mechanism that is nonstandard in Linux, to the transplanted containers. This paper analyzes vulnerabilities faced by Linux containers, identifies technical issues in transplanting Linux containers onto FreeBSD, and designs a mechanism to transparently apply the Capsicum sandbox to Linux applications to explore the feasibility of our approach.","PeriodicalId":149629,"journal":{"name":"Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134628060","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

Liquid: Mix-and-Match Multiple Image Formats to Balance DNN Training Pipeline 液体:混合和匹配多种图像格式来平衡DNN训练管道

Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems Pub Date : 2023-08-24 DOI: 10.1145/3609510.3609811

W. Baek, Jonghyun Bae, Donghyun Lee, Hyun-Cheol Bae, Yeonhong Park, Jae W. Lee

{"title":"Liquid: Mix-and-Match Multiple Image Formats to Balance DNN Training Pipeline","authors":"W. Baek, Jonghyun Bae, Donghyun Lee, Hyun-Cheol Bae, Yeonhong Park, Jae W. Lee","doi":"10.1145/3609510.3609811","DOIUrl":"https://doi.org/10.1145/3609510.3609811","url":null,"abstract":"Today's deep neural network (DNN) training pipeline utilizes hardware resources holistically, including host CPUs and storage devices for preprocessing the input data and accelerators like GPUs for computing gradients. As the performance of the accelerator scales rapidly, the frontend data preparation stages are becoming a new performance bottleneck to yield suboptimal training throughput. Since the bottleneck in the pipeline may vary depending on hardware configurations, DNN models, and datasets, overprovisioning hardware resources for data preparation such as CPU cores and disk bandwidth is not a cost-effective solution. Instead, we make a case for leveraging multiple data formats, possibly with opposing characteristics in resource utilization, to balance the training pipeline. This idea is realized by Liquid, a new system for building an efficient training pipeline with multi-format datasets. Our evaluation on three distinct execution environments demonstrates that Liquid achieves up to 3.05x and 1.54x higher data preparation throughput on Cityscapes/CityPersons (PNG) and ImageNet (JPEG) datasets, respectively, over the baseline single-format pipeline. This leads up to 2.02x and 1.25x higher end-to-end geomean training throughput with no accuracy drop.","PeriodicalId":149629,"journal":{"name":"Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130168735","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