Proceedings of the ACM on Measurement and Analysis of Computing Systems最新文献_第5页

The Online Knapsack Problem with Departures 带出发的在线背包问题

Proceedings of the ACM on Measurement and Analysis of Computing Systems Pub Date : 2022-09-24 DOI: 10.1145/3570618

Bo Sun, L. Yang, M. Hajiesmaili, A. Wierman, John C.S. Lui, D. Towsley, D. Tsang

引用次数: 6

Optimistic No-regret Algorithms for Discrete Caching 离散缓存的乐观无遗憾算法

Proceedings of the ACM on Measurement and Analysis of Computing Systems Pub Date : 2022-08-15 DOI: 10.1145/3570608

N. Mhaisen, Abhishek Sinha, G. Paschos, Georgios Iosifidis

{"title":"Optimistic No-regret Algorithms for Discrete Caching","authors":"N. Mhaisen, Abhishek Sinha, G. Paschos, Georgios Iosifidis","doi":"10.1145/3570608","DOIUrl":"https://doi.org/10.1145/3570608","url":null,"abstract":"We take a systematic look at the problem of storing whole files in a cache with limited capacity in the context of optimistic learning, where the caching policy has access to a prediction oracle (provided by, e.g., a Neural Network). The successive file requests are assumed to be generated by an adversary, and no assumption is made on the accuracy of the oracle. In this setting, we provide a universal lower bound for prediction-assisted online caching and proceed to design a suite of policies with a range of performance-complexity trade-offs. All proposed policies offer sublinear regret bounds commensurate with the accuracy of the oracle. Our results substantially improve upon all recently-proposed online caching policies, which, being unable to exploit the oracle predictions, offer only O(√T) regret. In this pursuit, we design, to the best of our knowledge, the first comprehensive optimistic Follow-the-Perturbed leader policy, which generalizes beyond the caching problem. We also study the problem of caching files with different sizes and the bipartite network caching problem. Finally, we evaluate the efficacy of the proposed policies through extensive numerical experiments using real-world traces.","PeriodicalId":426760,"journal":{"name":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125227898","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: 6

Enabling Long-term Fairness in Dynamic Resource Allocation 启用动态资源分配的长期公平性

Proceedings of the ACM on Measurement and Analysis of Computing Systems Pub Date : 2022-08-11 DOI: 10.1145/3570606

T. Si Salem, G. Iosifidis, G. Neglia

引用次数: 7

On the Stochastic and Asymptotic Improvement of First-Come First-Served and Nudge Scheduling 先到先得和助推调度的随机渐近改进

Proceedings of the ACM on Measurement and Analysis of Computing Systems Pub Date : 2022-06-21 DOI: 10.1145/3570610

B. Van Houdt

{"title":"On the Stochastic and Asymptotic Improvement of First-Come First-Served and Nudge Scheduling","authors":"B. Van Houdt","doi":"10.1145/3570610","DOIUrl":"https://doi.org/10.1145/3570610","url":null,"abstract":"Recently it was shown that, contrary to expectations, the First-Come-First-Served (FCFS) scheduling algorithm can be stochastically improved upon by a scheduling algorithm called Nudge for light-tailed job size distributions. Nudge partitions jobs into 4 types based on their size, say small, medium, large and huge jobs. Nudge operates identical to FCFS, except that whenever a small job arrives that finds a large job waiting at the back of the queue, Nudge swaps the small job with the large one unless the large job was already involved in an earlier swap. In this paper, we show that FCFS can be stochastically improved upon under far weaker conditions. We consider a system with 2 job types and limited swapping between type-1 and type-2 jobs, but where a type-1 job is not necessarily smaller than a type-2 job. More specifically, we introduce and study the Nudge-K scheduling algorithm which allows type-1 jobs to be swapped with up to K type-2 jobs waiting at the back of the queue, while type-2 jobs can be involved in at most one swap. We present an explicit expression for the response time distribution under Nudge-K when both job types follow a phase-type distribution. Regarding the asymptotic tail improvement ratio (ATIR), we derive a simple expression for the ATIR, as well as for the K that maximizes the ATIR. We show that the ATIR is positive and the optimal K tends to infinity in heavy traffic as long as the type-2 jobs are on average longer than the type-1 jobs.","PeriodicalId":426760,"journal":{"name":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121022900","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: 1

Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure 克服样本高效低秩结构强化学习的长视界障碍

Proceedings of the ACM on Measurement and Analysis of Computing Systems Pub Date : 2022-06-07 DOI: 10.1145/3589973

Tyler Sam, Yudong Chen, C. Yu

{"title":"Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure","authors":"Tyler Sam, Yudong Chen, C. Yu","doi":"10.1145/3589973","DOIUrl":"https://doi.org/10.1145/3589973","url":null,"abstract":"The practicality of reinforcement learning algorithms has been limited due to poor scaling with respect to the problem size, as the sample complexity of learning an ε-optimal policy is Ω(|S||A|H/ ε2) over worst case instances of an MDP with state space S, action space A, and horizon H. We consider a class of MDPs for which the associated optimal Q* function is low rank, where the latent features are unknown. While one would hope to achieve linear sample complexity in |S| and |A| due to the low rank structure, we show that without imposing further assumptions beyond low rank of Q*, if one is constrained to estimate the Q function using only observations from a subset of entries, there is a worst case instance in which one must incur a sample complexity exponential in the horizon H to learn a near optimal policy. We subsequently show that under stronger low rank structural assumptions, given access to a generative model, Low Rank Monte Carlo Policy Iteration (LR-MCPI) and Low Rank Empirical Value Iteration (LR-EVI) achieve the desired sample complexity of Õ((|S|+|A|)poly (d,H)/ε2) for a rank d setting, which is minimax optimal with respect to the scaling of |S|, |A|, and ε. In contrast to literature on linear and low-rank MDPs, we do not require a known feature mapping, our algorithm is computationally simple, and our results hold for long time horizons. Our results provide insights on the minimal low-rank structural assumptions required on the MDP with respect to the transition kernel versus the optimal action-value function.","PeriodicalId":426760,"journal":{"name":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133071796","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: 4

Monetizing Spare Bandwidth

Proceedings of the ACM on Measurement and Analysis of Computing Systems Pub Date : 2022-06-06 DOI: 10.1145/3530899

Yunming Xiao, Matteo Varvello, A. Kuzmanovic

{"title":"Monetizing Spare Bandwidth","authors":"Yunming Xiao, Matteo Varvello, A. Kuzmanovic","doi":"10.1145/3530899","DOIUrl":"https://doi.org/10.1145/3530899","url":null,"abstract":"Residential Internet speeds have been rapidly increasing, reaching averages of ~100 Mbps in most developed countries. Several studies have shown that users have way more bandwidth than they need, only using about 20-30% on a regular day. Several systems exploit this trend by enabling users to monetize their spare bandwidth, e.g., by sharing their WiFi connection or by participating in distributed proxy or VPN (dVPN) services. Despite the proliferation of such systems, little is known on how such marketplaces operate, what are the key factors that determine the price of the spare bandwidth, and how such prices differ worldwide. In this work, we shed some light on this topic using dVPNs as a use-case. We start by formalizing the problem of bandwidth monetization as an optimization between a buyer's cost and seller's income. Next, we explore three popular dVPNs (Mysterium, Sentinel, and Tachyon) using both active and passive measurements. We find that dVPNs have a large and growing footprint, and offer comparable performance to their centralized counterpart. We identify Mysterium (in the US) as the most concrete realization of a bandwidth marketplace, for which we derive a value of spare Internet bandwidth ranging between 11 and 14 cents per GB. We also show that both buyers and sellers utilize ad-hoc \"rules-of-thumb\" when choosing their prices, which results in a sub-optimal marketplace. By applying our optimization, a seller's income can be tripled by setting a price lower than the default one which allows to attract more buyers. These observations motivate us to create RING, a first and concrete system which helps sellers to automatically adjust their prices and traffic volumes across multiple marketplaces.","PeriodicalId":426760,"journal":{"name":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","volume":"19 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":"127636135","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

A Formalism of DNN Accelerator Flexibility DNN加速器灵活性的一种形式

Proceedings of the ACM on Measurement and Analysis of Computing Systems Pub Date : 2022-06-06 DOI: 10.1145/3530907

Sheng-Chun Kao, Hyoukjun Kwon, Michael Pellauer, A. Parashar, T. Krishna

{"title":"A Formalism of DNN Accelerator Flexibility","authors":"Sheng-Chun Kao, Hyoukjun Kwon, Michael Pellauer, A. Parashar, T. Krishna","doi":"10.1145/3530907","DOIUrl":"https://doi.org/10.1145/3530907","url":null,"abstract":"The high efficiency of domain-specific hardware accelerators for machine learning (ML) has come fromspecialization, with the trade-off of less configurability/ flexibility. There is growing interest in developingflexible ML accelerators to make them future-proof to the rapid evolution of Deep Neural Networks (DNNs). However, the notion of accelerator flexibility has always been used in an informal manner, restricting computer architects from conducting systematic apples-to-apples design-space exploration (DSE) across trillions of choices. In this work, we formally define accelerator flexibility and show how it can be integrated for DSE. % flows. Specifically, we capture DNN accelerator flexibility across four axes: %the map-space of DNN accelerator along four flexibility axes: tiling, ordering, parallelization, and array shape. We categorize existing accelerators into 16 classes based on their axes of flexibility support, and define a precise quantification of the degree of flexibility of an accelerator across each axis. We leverage these to develop a novel flexibility-aware DSE framework. %It respects the difference of accelerator flexibility classes and degree of flexibility support in different accelerators, creating unique map-spaces. %and forms a unique map space for exploration. % We demonstrate how this can be used to perform first-of-their-kind evaluations, including an isolation study to identify the individual impact of the flexibility axes. We demonstrate that adding flexibility features to a hypothetical DNN accelerator designed in 2014 improves runtime on future (i.e., present-day) DNNs by 11.8x geomean.","PeriodicalId":426760,"journal":{"name":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","volume":"53 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":"130733071","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: 1

Dremel Dremel

Proceedings of the ACM on Measurement and Analysis of Computing Systems Pub Date : 2022-06-06 DOI: 10.1145/3530903

Chenxingyu Zhao, Tapan Chugh, Jaehong Min, Ming Liu, A. Krishnamurthy

{"title":"Dremel","authors":"Chenxingyu Zhao, Tapan Chugh, Jaehong Min, Ming Liu, A. Krishnamurthy","doi":"10.1145/3530903","DOIUrl":"https://doi.org/10.1145/3530903","url":null,"abstract":"LSM-tree-based key-value stores like RocksDB are widely used to support many applications. However, configuring a RocksDB instance is challenging for the following reasons: 1) RocksDB has a massive parameter space to configure; 2) there are inherent trade-offs and dependencies between parameters; 3) right configurations are dependent on workload and hardware; and 4) evaluating configurations is time-consuming. Prior works struggle with handling the curse of dimensionality, capturing relationships between parameters, adapting configurations to workload and hardware, and evaluating quickly. In this work, we present a system, Dremel, to adaptively and quickly configure RocksDB with strategies based on the Multi-Armed Bandit model. To handle the massive parameter space, we propose using fused features, which encode domain-specific knowledge, to work as a compact and powerful representation for configurations. To adapt to the workload and hardware, we build an online bandit model to identify the best configuration. To evaluate quickly, we enable multi-fidelity evaluation and upper-confidence-bound sampling to speed up identifying the best configuration. Dremel not only achieves up to ×2.61 higher IOPS and 57% less latency than default configurations but also achieves up to 63% improvements over prior works on 18 different settings with the same or less time budget.","PeriodicalId":426760,"journal":{"name":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","volume":"23 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":"133028589","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

Prediction of the Resource Consumption of Distributed Deep Learning Systems 分布式深度学习系统的资源消耗预测

Proceedings of the ACM on Measurement and Analysis of Computing Systems Pub Date : 2022-06-06 DOI: 10.1145/3530895

Gyeongsik Yang, C. Shin, J. Lee, Yeonho Yoo, C. Yoo

引用次数: 2

POMACS V6, N2, June 2022 Editorial POMACS V6, N2, 2022年6月社论

Proceedings of the ACM on Measurement and Analysis of Computing Systems Pub Date : 2022-06-06 DOI: 10.1145/3530890

Niklas Carlsson, Edith Cohen, Philippe Robert

{"title":"POMACS V6, N2, June 2022 Editorial","authors":"Niklas Carlsson, Edith Cohen, Philippe Robert","doi":"10.1145/3530890","DOIUrl":"https://doi.org/10.1145/3530890","url":null,"abstract":"The ACM Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented during the ACM SIGMETRICS/Performance 2022 conference. The issue contains papers selected by the editorial board via a rigorous review process that follows a hybrid conference and journal model, with reviews conducted by the 101 members of our POMACS editorial board. Each paper was either conditionally accepted (and shepherded), allowed a \"one-shot\" revision (to be resubmitted to one of the subsequent two deadlines), or rejected (with resubmission allowed after a year). For this issue, which represents the winter deadline, we accepted 17 papers out of 126 submissions (including 4 papers that had been given a \"one-shot\" revision opportunity). All submitted papers received at least 3 reviews and we held an online TPC meeting. Based on the indicated primary track, roughly 31% of the submissions were in the Measurement & Applied Modeling track, 25% were in the Systems track, 23% were in the Theory track, and 21% were in the Learning track. Many people contributed to the success of this issue of POMACS. First, we would like to thank the authors, who submitted their work to SIGMETRICS/POMACS. Second, we would like to thank the TPC members for their work: constructive feedback in their reviews to authors, participation to online discussions and also to TPC meetings. We also thank several external reviewers who provided their expert opinion on specific submissions that required additional input. We are also grateful to the SIGMETRICS Board Chair, Giuliano Casale, and to past TPC Chairs. Finally, we are grateful to the Organization Committee and to the SIGMETRICS Board for their ongoing efforts and initiatives for creating an exciting program for ACM SIGMETRICS/Performance 2022.","PeriodicalId":426760,"journal":{"name":"Proceedings of the ACM on Measurement and Analysis of Computing 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":"128853649","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