Proceedings of the ACM on Measurement and Analysis of Computing Systems最新文献

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Near-Optimal Packet Scheduling in Multihop Networks with End-to-End Deadline Constraints 具有端到端截止时间约束的多跳网络中的近优数据包调度

Proceedings of the ACM on Measurement and Analysis of Computing Systems Pub Date : 2023-10-29 DOI: 10.1145/3626781

Christos Tsanikidis, Javad Ghaderi

{"title":"Near-Optimal Packet Scheduling in Multihop Networks with End-to-End Deadline Constraints","authors":"Christos Tsanikidis, Javad Ghaderi","doi":"10.1145/3626781","DOIUrl":"https://doi.org/10.1145/3626781","url":null,"abstract":"Scheduling packets with end-to-end deadline constraints in multihop networks is an important problem that has been notoriously difficult to tackle. Recently, there has been progress on this problem in the worst-case traffic setting, with the objective of maximizing the number of packets delivered within their deadlines. Specifically, the proposed algorithms were shown to achieve Ω(1/log(L)) fraction of the optimal objective value if the minimum link capacity in the network is Cmin =Ω(log (L)), where L is the maximum length of a packet's route in the network (which is bounded by the packet's maximum deadline). However, such guarantees can be quite pessimistic due to the strict worst-case traffic assumption and may not accurately reflect real-world settings. In this work, we aim to address this limitation by exploring whether it is possible to design algorithms that achieve a constant fraction of the optimal value while relaxing the worst-case traffic assumption. We provide a positive answer by demonstrating that in stochastic traffic settings, such as i.i.d. packet arrivals, near-optimal, (1-ε)-approximation algorithms can be designed if Cmin = Ω(log (L/ε)/ε2). To the best of our knowledge, this is the first result that shows this problem can be solved near-optimally under nontrivial assumptions on traffic and link capacity. We further present extended simulations using real network traces with non-stationary traffic, which demonstrate that our algorithms outperform worst-case-based algorithms in practical settings.","PeriodicalId":510624,"journal":{"name":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","volume":"3 1","pages":"1 - 32"},"PeriodicalIF":0.0,"publicationDate":"2023-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139311506","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

Sampling for Remote Estimation of the Wiener Process over an Unreliable Channel 不可靠信道上的维纳过程远程估计采样

Proceedings of the ACM on Measurement and Analysis of Computing Systems Pub Date : 2023-10-17 DOI: 10.1145/3626791

Jiayu Pan, Yin Sun, N. Shroff

{"title":"Sampling for Remote Estimation of the Wiener Process over an Unreliable Channel","authors":"Jiayu Pan, Yin Sun, N. Shroff","doi":"10.1145/3626791","DOIUrl":"https://doi.org/10.1145/3626791","url":null,"abstract":"In this paper, we study a sampling problem where a source takes samples from a Wiener process and transmits them through a wireless channel to a remote estimator. Due to channel fading, interference, and potential collisions, the packet transmissions are unreliable and could take random time durations. Our objective is to devise an optimal causal sampling policy that minimizes the long-term average mean square estimation error. This optimal sampling problem is a recursive optimal stopping problem, which is generally quite difficult to solve. However, we prove that the optimal sampling strategy is, in fact, a simple threshold policy where a new sample is taken whenever the instantaneous estimation error exceeds a threshold. This threshold remains a constant value that does not vary over time. By exploring the structure properties of the recursive optimal stopping problem, a low-complexity iterative algorithm is developed to compute the optimal threshold. This work generalizes previous research by incorporating both transmission errors and random transmission times into remote estimation. Numerical simulations are provided to compare our optimal policy with the zero-wait and age-optimal policies.","PeriodicalId":510624,"journal":{"name":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","volume":"5 1","pages":"1 - 41"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139317886","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