2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)最新文献_第2页

FABMon: Enabling Fast and Accurate Network Available Bandwidth Estimation FABMon:实现快速准确的网络可用带宽估计

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812891

Tao Jin, Weichao Li, Qing Li, Qianyi Huang, Yong Jiang, Shutao Xia

{"title":"FABMon: Enabling Fast and Accurate Network Available Bandwidth Estimation","authors":"Tao Jin, Weichao Li, Qing Li, Qianyi Huang, Yong Jiang, Shutao Xia","doi":"10.1109/IWQoS54832.2022.9812891","DOIUrl":"https://doi.org/10.1109/IWQoS54832.2022.9812891","url":null,"abstract":"Characterizing the end-to-end network available bandwidth (ABW) is an important but challenging task. Although a number of ABW estimation tools have been introduced over the past two decades, applying them to the real-world networks is still difficult because of the biased results, heavy load, and long measurement time. In this paper, we propose a novel Burst Queue Recovery (BQR) model to infer the ABW. BQR first induces an instant network congestion and then observes the one-way delay (OWD) variation until the tight link recovers from the congestion. By correlating the OWDs with the queue length variation, BQR can calculate the ABW accurately. Compared to the traditional probe gap model (PGM) and probe rate model (PRM), our theoretical analysis and simulations show that BQR is more tolerant to the transient traffic burst and supports the scenarios with multiple congestible links. Based on the model, we build FABMon, a fast and accurate ABW estimation tool. Our experiments show that FABMon can measure ABW within 50 milliseconds, and achieve much more accurate measurement results than the existing tools with a very small volume of probe packets.","PeriodicalId":353365,"journal":{"name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127650523","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

Improving disk failure detection accuracy via data augmentation 通过数据增强提高磁盘故障检测的准确性

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812864

Wang Wang, Xuehai Tang, Biyu Zhou, Wenjie Xiao, Jizhong Han, Songlin Hu

{"title":"Improving disk failure detection accuracy via data augmentation","authors":"Wang Wang, Xuehai Tang, Biyu Zhou, Wenjie Xiao, Jizhong Han, Songlin Hu","doi":"10.1109/IWQoS54832.2022.9812864","DOIUrl":"https://doi.org/10.1109/IWQoS54832.2022.9812864","url":null,"abstract":"Frequently happening of disk failures seriously affects the dependability and service quality of cloud data centers. Recently, machine learning (ML) based methods are popularly adopted to proactively predict forthcoming disk failures via supervised learning. However, the high imbalance of failure samples and healthy samples is a huge obstacle for existing detection methods to establish high performance detection model. This paper presents a data augmentation method MSGMD, which can efficiently generate high quality failure samples to alleviate the data imbalance of the training set, so as to effectively improve the performance of any supervised failure detection models. First, MSGMD converts failure samples (multivariate time series) into multiple univariate time series via decomposing the spatial relations among features. Then it learns the temporal correlation of each feature via a policy-based reinforcement learning model trained in an adversarial way. After that, it generates failure samples by combining feature series sampled from learned distribution. Finally, it filters out low quality generated samples with a confidence-based method. Experimental results on real-world datasets show that, through data augmentation, MSGMD can improve the FDR and F1-Score of the state-of-the-art disk failure detection model by 31.59% and 30.74% respectively on average.","PeriodicalId":353365,"journal":{"name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","volume":"10 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125461336","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

Accelerating Blockchain-enabled Distributed Machine Learning by Proof of Useful Work 通过证明有用的工作加速区块链支持的分布式机器学习

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812927

Yao Du, Cyril Leung, Zehua Wang, Victor C. M. Leung

引用次数: 1

BUDA: Budget and Deadline Aware Scheduling Algorithm for Task Graphs in Heterogeneous Systems 异构系统中任务图的预算和截止日期感知调度算法

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812865

Hamza Djigal, Linfeng Liu, Jian Luo, Jia Xu

{"title":"BUDA: Budget and Deadline Aware Scheduling Algorithm for Task Graphs in Heterogeneous Systems","authors":"Hamza Djigal, Linfeng Liu, Jian Luo, Jia Xu","doi":"10.1109/IWQoS54832.2022.9812865","DOIUrl":"https://doi.org/10.1109/IWQoS54832.2022.9812865","url":null,"abstract":"Task graphs are widely used to represent data-intensive applications. To efficiently execute these applications on heterogeneous systems, each task must be properly scheduled on the processors of the system. The NP-completeness of the task scheduling problem has motivated researchers to propose various heuristic methods. Recently, Quality of Service (QoS) aware scheduling is becoming an active research area in heterogeneous systems because the end-user has different QoS requirements. Generally, time and cost are the most relevant user concerns. However, it is challenging to find a feasible scheduling plan which minimizes the total execution time of the user’s application (makespan) while satisfying both budget and deadline constraints. In this paper, we present a novel heuristic algorithm called Budget-Deadline-Aware-Scheduling (BUDA) that addresses task graphs scheduling under budget and deadline constraints in heterogeneous systems. The novelty of the BUDA algorithm is based on a Heterogeneous Time-Cost Matrix (HTCM) that is used to prioritize tasks and for processor selection. In addition, we introduce a new Heterogeneous Time-Cost Trade-off factor (HTCT) that tries to adjust the time and cost for the current task among all processors. The experiments based on randomly generated graphs and real-world applications graphs show that the BUDA algorithm outperforms the state-of-the-art algorithms in terms of makespan, time efficiency, and success rate.","PeriodicalId":353365,"journal":{"name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123087551","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

PPAR: A Privacy-Preserving Adaptive Ranking Algorithm for Multi-Armed-Bandit Crowdsourcing PPAR:一种保护隐私的多武装盗贼众包自适应排序算法

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812914

Shuzhen Chen, Dongxiao Yu, Feng Li, Zong-bao Zou, W. Liang, Xiuzhen Cheng

{"title":"PPAR: A Privacy-Preserving Adaptive Ranking Algorithm for Multi-Armed-Bandit Crowdsourcing","authors":"Shuzhen Chen, Dongxiao Yu, Feng Li, Zong-bao Zou, W. Liang, Xiuzhen Cheng","doi":"10.1109/IWQoS54832.2022.9812914","DOIUrl":"https://doi.org/10.1109/IWQoS54832.2022.9812914","url":null,"abstract":"This paper studies the privacy-preserving adaptive ranking problem for multi-armed-bandit crowdsourcing, where according to the crowdsourced data, the arms are required to be ranked with a tunable granularity by the untrustworthy third-party platform. Any online worker can provide its data by arm pulls but requires its privacy preserved, which will increase the ranking cost greatly. To improve the quality of the ranking service, we propose a Privacy- Preserving Adaptive Ranking algorithm called PPAR, which can solve the problem with a high probability while differential privacy can be ensured. The total cost of the proposed algorithm is ${mathcal{O}}(Kln K)$, which is near optimal compared with the trivial lower bound Ω(K), where K is the number of arms. Our proposed algorithm can also be used to solve the well-studied fully ranking problem and the best arm identification problem, by proper setting the granularity parameter. For the fully ranking problem, PPAR attains the same order of computation complexity with the best-known results without privacy preservation. The efficacy of our algorithm is also verified by extensive experiments on public datasets.","PeriodicalId":353365,"journal":{"name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131113710","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 Control-Theoretic and Online Learning Approach to Self-Tuning Queue Management 一种自调优队列管理的控制理论在线学习方法

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812928

Jiancheng Ye, Kechao Cai, Dong Lin, Jia-Ru Li, Jianfei He, John C.S. Lui

{"title":"A Control-Theoretic and Online Learning Approach to Self-Tuning Queue Management","authors":"Jiancheng Ye, Kechao Cai, Dong Lin, Jia-Ru Li, Jianfei He, John C.S. Lui","doi":"10.1109/IWQoS54832.2022.9812928","DOIUrl":"https://doi.org/10.1109/IWQoS54832.2022.9812928","url":null,"abstract":"There is a growing trend that network applications not only require higher throughput, but also impose stricter delay requirements. The current Internet congestion control, which is driven by active queue management (AQM) algorithms interacting with the Transmission Control Protocol (TCP), has been playing an important role in supporting network applications. However, it still exhibits many open issues. Most of AQM algorithms only deploy a single-queue structure that cannot differentiate flows and easily leads to unfairness. Moreover, the parameter settings of AQM are often static, making them difficult to adapt to the dynamic network environments. In this paper, we propose a general framework for designing \"self-tuning\" queue management (SQM), which is adaptive to the changing environments and provides fair congestion control among flows. We first present a general architecture of SQM with fair queueing and propose a general fluid model to analyze it. To adapt to the stochastic environments, we formulate a stochastic network utility maximization (SNUM) problem, and utilize online convex optimization (OCO) and control theory to develop a distributed SQM algorithm which can self-tune different queue weights and control parameters. Numerical and packet-level simulation results show that our SQM algorithm significantly improves queueing delay and fairness among flows.","PeriodicalId":353365,"journal":{"name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","volume":"201 S605","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132904945","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

Online Traffic Allocation Based on Percentile Charging for Practical CDNs 基于百分位计费的实用cdn在线流量分配

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812878

Huang Chen, Huiyou Zhan, Haisheng Tan, Huan Xu, Weihua Shan, Shiteng Chen, Xiang-Yang Li

{"title":"Online Traffic Allocation Based on Percentile Charging for Practical CDNs","authors":"Huang Chen, Huiyou Zhan, Haisheng Tan, Huan Xu, Weihua Shan, Shiteng Chen, Xiang-Yang Li","doi":"10.1109/IWQoS54832.2022.9812878","DOIUrl":"https://doi.org/10.1109/IWQoS54832.2022.9812878","url":null,"abstract":"With the explosion of data transmitted over the Internet, Content Delivery Networks (CDNs) carry massive network traffic globally and suffer an increasingly higher bandwidth cost. A critical issue for CDN service providers is how to allocate network traffic among CDN facilities to reduce the total bandwidth cost without violating the quality of service. This work studies online traffic allocation in CDNs to minimize the bandwidth cost under the 95th percentile charging model. Specifically, we here take into account practical deployment issues in large-scale CDN systems, e.g., allocation granularity and deviation. We first theoretically prove the approximation hardness of the traffic allocation problem. We then propose a novel prediction-based algorithm named OnTPC, which effectively addresses constraints raised in practical deployment. Extensive experiments demonstrate that OnTPC outperforms state-of-the-art baselines and is expected to save over a million dollars per month for our large-scale commercial CDN collaborator. Moreover, the performance of OnTPC is consistently outstanding under various settings, and specifically robust to large allocation deviation.","PeriodicalId":353365,"journal":{"name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128670570","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: 3

ACCeSS: Adaptive QoS-aware Congestion Control for Multipath TCP ACCeSS:多路径TCP的自适应qos感知拥塞控制

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812886

Xiaolan Ji, Biao Han, Ruidong Li, Cao Xu, Yahui Li, Jinshu Su

{"title":"ACCeSS: Adaptive QoS-aware Congestion Control for Multipath TCP","authors":"Xiaolan Ji, Biao Han, Ruidong Li, Cao Xu, Yahui Li, Jinshu Su","doi":"10.1109/IWQoS54832.2022.9812886","DOIUrl":"https://doi.org/10.1109/IWQoS54832.2022.9812886","url":null,"abstract":"Multipath TCP (MPTCP) enables multi-home devices to establish multiple paths for simultaneous data transmission. However, due to diverse Quality of Service (QoS) requirements in real network, existing multipath congestion control algorithms (CCAs) fail to fast adapt to dynamic traffic, which leads to performance degradation, especially in heterogeneous network environments. To tackle these problems, in this paper, we first observe the performance limitations of current multipath CCAs by conducting extensive experiments. Then we propose ACCeSS, an adaptive QoS-aware multipath congestion control framework, which is able to promptly adapt to network changes and QoS requirements with a novel control policy optimization phase. In order to adjust and stimulate improvement of the preferred performance metric, ACCeSS exploits Random Forest Regressing (RFR) method to perform QoS-specific utility function optimization. ACCeSS is implemented and compared with other multipath CCAs in Linux kernel. Performances of ACCeSS are evaluated in both emulated and real-world networks, which reveal that ACCeSS outperforms classic multipath CCAs and the state-of-the-art learning based multipath CCA with better adaptive capability of QoS.","PeriodicalId":353365,"journal":{"name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124551790","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: 3

Timely-throughput Optimal Scheduling for Wireless Flows with Deep Reinforcement Learning 基于深度强化学习的无线流实时吞吐量最优调度

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812916

Qi Wang, Chentao He, K. Jaffrès-Runser, Jianhui Huang, Yongjun Xu

{"title":"Timely-throughput Optimal Scheduling for Wireless Flows with Deep Reinforcement Learning","authors":"Qi Wang, Chentao He, K. Jaffrès-Runser, Jianhui Huang, Yongjun Xu","doi":"10.1109/IWQoS54832.2022.9812916","DOIUrl":"https://doi.org/10.1109/IWQoS54832.2022.9812916","url":null,"abstract":"This paper addresses the problem of scheduling real-time wireless flows under dynamic network conditions and general traffic patterns. The objective is to maximize the fraction of packets of each flow to be delivered within their deadlines, referred to as timely-throughput. The scheduling problem under restrictive frame-based traffic models or greedy maximal scheduling schemes like LDF has been extensively studied so far, but scheduling algorithms to provide deadline guarantees on packet delivery for general traffic under dynamic network conditions are very limited. We propose two scheduling algorithms using deep reinforcement learning approach to optimize timely-throughput for general traffic in dynamic wireless networks: RL-Centralized scheduling algorithm and RL-Decentralized scheduling algo-rithm. Specifically, we formulate the centralized scheduling problem as a Markov Decision Process (MDP) and a multi-environments double deep Q-network (ME-DDQN) structure is proposed to adapt to the dynamic network conditions. The decentralized scheduling problem is formulated as a Partially Observable Markov Decision Process (POMDP) and an expert-apprentice centralized training and decentralized execution (EA-CTDE) structure is designed to accelerate the training speed and achieve the optimal timely-throughput. The extensive results show that the proposed scheduling algorithms converge fast and adapt well to network dynamics with superior performance compared to baseline policies. Finally, experimental tests confirm simulation results and also show that the proposed algorithms are feasible in practice on resource limited platforms.","PeriodicalId":353365,"journal":{"name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114915987","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

When Power-of-d-Choices Meets Priority 当选择的力量遇到优先级

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812880

Jianyu Niu, Chunpu Wang, Chen Feng, Hong Chao Xu

引用次数: 0