Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems最新文献_第2页

Trade or Trick?: Detecting and Characterizing Scam Tokens on Uniswap Decentralized Exchange 交易还是诡计?:在Uniswap去中心化交易所检测和表征诈骗令牌

Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems Pub Date : 2022-06-06 DOI: 10.1145/3489048.3522636

Pengcheng Xia, Haoyu Wang, Bingyu Gao, Weihang Su, Zhou Yu, Xiapu Luo, Chao Zhang, Xusheng Xiao, Guoai Xu

{"title":"Trade or Trick?: Detecting and Characterizing Scam Tokens on Uniswap Decentralized Exchange","authors":"Pengcheng Xia, Haoyu Wang, Bingyu Gao, Weihang Su, Zhou Yu, Xiapu Luo, Chao Zhang, Xusheng Xiao, Guoai Xu","doi":"10.1145/3489048.3522636","DOIUrl":"https://doi.org/10.1145/3489048.3522636","url":null,"abstract":"The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Uniswap, as the most prominent cryptocurrency decentralized exchange (DEX), is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first investigate the landscape of cryptocurrency trading on Uniswap from different perspectives based on its transactions. Then, we propose an accurate approach for flagging scam tokens on Uniswap. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens are created specialized for the \"rug pull\" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams. The scammers have gained a profit of at least $16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem.","PeriodicalId":264598,"journal":{"name":"Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130432876","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: 29

Competitive Online Optimization with Multiple Inventories: A Divide-and-Conquer Approach 竞争在线优化与多个库存:分而治之的方法

Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems Pub Date : 2022-06-06 DOI: 10.1145/3489048.3530969

Qiulin Lin, Yanfang Mo, Junyan Su, Minghua Chen

{"title":"Competitive Online Optimization with Multiple Inventories: A Divide-and-Conquer Approach","authors":"Qiulin Lin, Yanfang Mo, Junyan Su, Minghua Chen","doi":"10.1145/3489048.3530969","DOIUrl":"https://doi.org/10.1145/3489048.3530969","url":null,"abstract":"We study an online inventory trading problem where a user seeks to maximize the aggregate revenue of trading multiple inventories over a time horizon. The trading constraints and concave revenue functions are revealed sequentially in time, and the user needs to make irrevocable decisions. The problem has wide applications in various engineering domains. Existing works employ the primal-dual framework to design online algorithms with sub-optimal, albeit near-optimal, competitive ratios (CR). We exploit the problem structure to develop a new divide-and-conquer approach to solve the online multi-inventory problem by solving multiple calibrated single-inventory ones separately and combining their solutions. The approach achieves the optimal CR of $łn θ + 1$ if $Nłeq łn θ + 1$, where N is the number of inventories and θ represents the revenue function uncertainty; it attains a CR of $1/[1-e^-1/(łnθ+1) ] in [łn θ +1, łn θ +2)$ otherwise. The divide-and-conquer approach reveals novel structural insights for the problem, (partially) closes a gap in existing studies, and generalizes to broader settings. For example, it gives an algorithm with a CR within a constant factor to the lower bound for a generalized one-way trading problem with price elasticity with no previous results. When developing the above results, we also extend a recent CR-Pursuit algorithmic framework and introduce an online allocation problem with allowance augmentation, both of which can be of independent interest.","PeriodicalId":264598,"journal":{"name":"Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133160473","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: 2

CachePerf 缓存性能

Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems Pub Date : 2022-06-06 DOI: 10.1145/3489048.3526954

Jin Zhou, Steven J. Tang, Hanmei Yang, Tongping Liu

{"title":"CachePerf","authors":"Jin Zhou, Steven J. Tang, Hanmei Yang, Tongping Liu","doi":"10.1145/3489048.3526954","DOIUrl":"https://doi.org/10.1145/3489048.3526954","url":null,"abstract":"The cache plays a key role in determining the performance of applications, no matter for sequential or concurrent programs on homogeneous and heterogeneous architecture. Fixing cache misses requires to understand the origin and the type of cache misses. However, this remains to be an unresolved issue even after decades of research. This paper proposes a unified profiling tool--CachePerf--that could correctly identify different types of cache misses, differentiate allocator-induced issues from those of applications, and exclude minor issues without much performance impact. The core idea behind CachePerf is a hybrid sampling scheme: it employs the PMU-based coarse-grained sampling to select very few susceptible instructions (with frequent cache misses) and then employs the breakpoint-based fine-grained sampling to collect the memory access pattern of these instructions. Based on our evaluation, CachePerf only imposes 14% performance overhead and 19% memory overhead (for applications with large footprints), while identifying the types of cache misses correctly. CachePerf detected 9 previous-unknown bugs. Fixing the reported bugs achieves from 3% to 3788% performance speedup. CachePerf will be an indispensable complementary to existing profilers due to its effectiveness and low overhead.","PeriodicalId":264598,"journal":{"name":"Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134095221","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

YourAdvalue YourAdvalue

Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems Pub Date : 2022-06-06 DOI: 10.1145/3489048.3522629

Michalis Pachilakis, P. Papadopoulos, Nikolaos Laoutaris, E. Markatos, N. Kourtellis

{"title":"YourAdvalue","authors":"Michalis Pachilakis, P. Papadopoulos, Nikolaos Laoutaris, E. Markatos, N. Kourtellis","doi":"10.1145/3489048.3522629","DOIUrl":"https://doi.org/10.1145/3489048.3522629","url":null,"abstract":"The Real Time Bidding (RTB) protocol is by now more than a decade old. During this time, a handful of measurement papers have looked at bidding strategies, personal information flow, and cost of display advertising through RTB. In this paper, we present YourAdvalue, a privacy-preserving tool for displaying to end-users in a simple and intuitive manner their advertising value as seen through RTB. Using YourAdvalue, we measure desktopRTB prices in the wild, and compare them with desktop and mobileRTB prices reported by past work. We present how it estimates ad prices that are encrypted, and how it preserves user privacy while reporting results back to a data-server for analysis. We deployed our system, disseminated its browser extension, and collected data from 200 users, including 12000 ad impressions over 11 months.\u0000 By analyzing this dataset, we show that desktop RTB prices have grown 4.6x over desktop RTB prices measured in 2013, and 3.8x over mobile RTB prices measured in 2015. We also study how user demographics associate with the intensity of RTB ecosystem tracking, leading to higher ad prices. We find that exchanging data between advertisers and/or data brokers through cookie- syncronization increases the median value of displayed ads by 19%. We also find that female and younger users are more targeted, suffering more tracking (via cookie synchronization) than male or elder users. As a result of this targeting in our dataset, the advertising value (i) of women is 2.4x higher than that of men, (ii) of 25-34 year-olds is 2.5x higher than that of 35-44 year-olds, (iii) is most expensive on weekends and early mornings.","PeriodicalId":264598,"journal":{"name":"Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122991495","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

Memory Space Recycling 内存空间回收

Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems Pub Date : 2022-06-06 DOI: 10.1145/3489048.3522654

Jihyun Ryoo, M. Kandemir, Mustafa Karaköy

{"title":"Memory Space Recycling","authors":"Jihyun Ryoo, M. Kandemir, Mustafa Karaköy","doi":"10.1145/3489048.3522654","DOIUrl":"https://doi.org/10.1145/3489048.3522654","url":null,"abstract":"Many program codes from different application domains process very large amounts of data, making their data locality/cache memory behavior critical for high performance. Prior work has addressed the data locality optimization problem in the context of both single-core and multi-core systems. Another dimension of optimization, which can be as equally important/beneficial as improving data access pattern is to reduce the data volume (total number of addresses) accessed by the program code. In this work, we explore the idea of rewriting an application program code to reduce its memory space footprint. The main idea behind this approach is to reuse/recycle, for a given data element, a memory location that has originally been assigned to another data element, provided that the lifetimes of these two data elements do not overlap with each other. We present a detailed experimental evaluation of our proposed memory space recycling strategy. The experimental results show that our proposed approach brings, respectively, 33.2%, 48.6%, 46.5%, 31.8%, and 27.9% average improvements in these metrics, in the case of single-threaded applications. With the multi-threaded versions of the same applications, the achieved improvements are 39.5%, 55.5%, 53.4%, 26.2%, and 22.2%, in the same order.","PeriodicalId":264598,"journal":{"name":"Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems","volume":"361 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121725496","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

Data-Driven Network Path Simulation with iBox 数据驱动的网络路径仿真与iBox

Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems Pub Date : 2022-06-06 DOI: 10.1145/3489048.3522646

S. Ashok, Shubham Tiwari, Nagarajan Natarajan, V. Padmanabhan, Sundararajan Sellamanickam

引用次数: 1

A Detailed Look at MIMO Performance in 60 GHz WLANs 60 GHz无线局域网中MIMO性能的详细介绍

Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems Pub Date : 2022-06-06 DOI: 10.1145/3489048.3530971

Shivang Aggarwal, Srisai Karthik Neelamraju, Ajit Bhat, Dimitrios Koutsonikolas

{"title":"A Detailed Look at MIMO Performance in 60 GHz WLANs","authors":"Shivang Aggarwal, Srisai Karthik Neelamraju, Ajit Bhat, Dimitrios Koutsonikolas","doi":"10.1145/3489048.3530971","DOIUrl":"https://doi.org/10.1145/3489048.3530971","url":null,"abstract":"One of the key enhancements in the upcoming 802.11ay standard for 60 GHz WLANs is the support for simultaneous transmissions of up to 8 data streams via SU- and MU-MIMO, which has the potential to enable data rates up to 100 Gbps. However, in spite of the key role MIMO is expected to play in 802.11ay, experimental evaluation of MIMO performance in 60 GHz WLANs has been limited to date, primarily due to lack of hardware supporting MIMO transmissions at millimeter wave frequencies. In this work, we fill this gap by conducting the first large-scale experimental evaluation of SU- and MU-MIMO performance in 60 GHz WLANs. Unlike previous studies, our study involves multiple environments with very different multipath characteristics. We analyze the performance in each environment, identify the factors that affect it, and compare it against the performance of SISO. Further, we seek to identify factors that can guide beam and user selection to limit the (often prohibitive in practice) overhead of exhaustive search. Finally, we propose two heuristics that perform both user and beam selection with low overhead, and show that they perform close to an Oracle solution and outperform previously proposed approaches in both static and mobile scenarios, regardless of the environment and number of users.","PeriodicalId":264598,"journal":{"name":"Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116919318","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

The First 5G-LTE Comparative Study in Extreme Mobility 首个5G-LTE极端移动性对比研究

Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems Pub Date : 2022-06-06 DOI: 10.1145/3489048.3522660

Yueyang Pan, Ruihan Li, Chenren Xu

引用次数: 3

Understanding I/O Direct Cache Access Performance for End Host Networking 了解终端主机组网中I/O直接缓存访问性能

Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems Pub Date : 2022-06-06 DOI: 10.1145/3489048.3522662

Minhu Wang, Mingwei Xu, Jianping Wu

{"title":"Understanding I/O Direct Cache Access Performance for End Host Networking","authors":"Minhu Wang, Mingwei Xu, Jianping Wu","doi":"10.1145/3489048.3522662","DOIUrl":"https://doi.org/10.1145/3489048.3522662","url":null,"abstract":"Direct Cache Access (DCA) enables a network interface card (NIC) to load and store data directly on the processor cache, as conventional Direct Memory Access (DMA) is no longer suitable as the bridge between NIC and CPU in the era of 100 Gigabit Ethernet. As numerous I/O devices and cores compete for scarce cache resources, making the most of DCA for networking applications with varied objectives and constraints is a challenge, especially given the increasing complexity of modern cache hardware and I/O stacks. In this paper, we reverse engineer details of one commercial implementation of DCA, Intel's Data Direct I/O (DDIO), to explicate the importance of hardware-level investigation into DCA. Based on the learned knowledge of DCA and network I/O stacks, we (1) develop an analytical framework to predict the effectiveness of DCA (i.e., its hit rate) under certain hardware specifications, system configurations, and application properties; (2) measure penalties of the ineffective use of DCA (i.e., its miss penalty) to characterize its benefits; and (3) show that our reverse engineering, measurement, and model contribute to a deeper understanding of DCA, which in turn helps diagnose, optimize, and design end-host networking.","PeriodicalId":264598,"journal":{"name":"Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126318176","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: 2

Differentially Private Reinforcement Learning with Linear Function Approximation 基于线性函数逼近的差分私有强化学习

Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems Pub Date : 2022-06-06 DOI: 10.1145/3489048.3522648

Xingyu Zhou

{"title":"Differentially Private Reinforcement Learning with Linear Function Approximation","authors":"Xingyu Zhou","doi":"10.1145/3489048.3522648","DOIUrl":"https://doi.org/10.1145/3489048.3522648","url":null,"abstract":"Motivated by the wide adoption of reinforcement learning (RL) in real-world personalized services, where users' sensitive and private information needs to be protected, we study regret minimization in finite-horizon Markov decision processes (MDPs) under the constraints of differential privacy (DP). Compared to existing private RL algorithms that work only on tabular finite-state, finite-actions MDPs, we take the first step towards privacy-preserving learning in MDPs with large state and action spaces. Specifically, we consider MDPs with linear function approximation (in particular linear mixture MDPs) under the notion of joint differential privacy (JDP), where the RL agent is responsible for protecting users' sensitive data. We design two private RL algorithms that are based on value iteration and policy optimization, respectively, and show that they enjoy sub-linear regret performance while guaranteeing privacy protection. Moreover, the regret bounds are independent of the number of states, and scale at most logarithmically with the number of actions, making the algorithms suitable for privacy protection in nowadays large-scale personalized services. Our results are achieved via a general procedure for learning in linear mixture MDPs under changing regularizers, which not only generalizes previous results for non-private learning, but also serves as a building block for general private reinforcement learning.","PeriodicalId":264598,"journal":{"name":"Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132201769","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: 2