2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)最新文献_第7页

Quality-aided Annotation Service Selection in MLaaS Market MLaaS市场中的质量辅助标注服务选择

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812877

Shanyang Jiang, Lan Zhang

{"title":"Quality-aided Annotation Service Selection in MLaaS Market","authors":"Shanyang Jiang, Lan Zhang","doi":"10.1109/IWQoS54832.2022.9812877","DOIUrl":"https://doi.org/10.1109/IWQoS54832.2022.9812877","url":null,"abstract":"The vibrant markets offering data annotation services are fast-growing and play an important part in machine learning. While many multi-label prediction services are available, it is challenging for consumers to decide which services to use for their own tasks and budgets due to the heterogeneity in those services’ labeling categories, labeling quality and price. In this paper, we focus on a practical problem of obtaining high-quality multi-label annotation data from multiple services within a budget constraint. We propose a framework that firstly parameterizes the labeling generation based on the constructed Probabilistic Graph Model, and designs an Expectation Maximization(EM)-based iteration algorithm to estimate the service labeling quality and task truth distribution. Then we transform the annotation service selection strategy into an adaptive submodular maximization coverage problem, which motivates us to design an adaptive random greedy algorithm with a constant approximation ratio 1−1/e. We evaluate our design on both real-world experiments and a series of simulations on various machine learning models and real datasets. These experiments will show that our method has more accuracy and reliability improvements.","PeriodicalId":353365,"journal":{"name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","volume":"171 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":"115480895","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

Order-preserved Tensor Completion For Accurate Network-wide Monitoring 顺序保留张量补全准确的网络范围内的监测

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812910

Xiaocan Li, Kun Xie, X. Wang, Gaogang Xie, KenLi Li, Dafang Zhang, Jigang Wen

{"title":"Order-preserved Tensor Completion For Accurate Network-wide Monitoring","authors":"Xiaocan Li, Kun Xie, X. Wang, Gaogang Xie, KenLi Li, Dafang Zhang, Jigang Wen","doi":"10.1109/IWQoS54832.2022.9812910","DOIUrl":"https://doi.org/10.1109/IWQoS54832.2022.9812910","url":null,"abstract":"Network-wide monitoring is important for many network functions. However, monitoring data are often incomplete due to the need of sampling to reduce high measurement cost, system failure, and unavoidable transmission loss under severe communication. Instead of only targeting to estimate all missing monitoring data entries with a small set of measurement samples, we study a new order-preserved monitoring data estimation problem to accurately estimate the missing data entries while preserving the data entries’ order in the dataset. We propose a novel order-preserved tensor completion model that integrates both the low rank property and the order information into a joint learning problem to estimate the missing data. With well designed non-convex function to directly approximate the tensor rank and order-preserved constraint under the linear self-recovery method, our model can not only more accurately capture the low-rank property of monitoring data to increase the estimation performance of missing data, but also can capture the order information in monitoring data to ensure the estimation accuracy. Extensive experiments using four real datasets demonstrate that compared with the state-of-the-art tensor completion algorithms, our proposed algorithm can provide more accurate estimation and keep the value order of recovered entries to more effectively retrieve top-k large entries.","PeriodicalId":353365,"journal":{"name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","volume":"30 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":"127983003","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 Multi-access Edge Computing Meets Multi-area Intelligent Reflecting Surface: A Multi-agent Reinforcement Learning Approach 当多访问边缘计算遇到多区域智能反射面:一种多智能体强化学习方法

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812883

Shen Zhuang, Ying He, Fei Yu, Chengxi Gao, Weike Pan, Zhong Ming

{"title":"When Multi-access Edge Computing Meets Multi-area Intelligent Reflecting Surface: A Multi-agent Reinforcement Learning Approach","authors":"Shen Zhuang, Ying He, Fei Yu, Chengxi Gao, Weike Pan, Zhong Ming","doi":"10.1109/IWQoS54832.2022.9812883","DOIUrl":"https://doi.org/10.1109/IWQoS54832.2022.9812883","url":null,"abstract":"In recent years, multi-access edge computing (MEC) is emerging to provide computation and storage capabilities to the Internet of things (IoT) devices to improve the quality of service (QoS) of IoT applications. In addition, intelligent reflecting surface (IRS) techniques have attracted great interests from both academia and industry to improve the communication efficiency. Although existing works leverage the IRS technique in MEC networks, they mainly focus on the single-IRS single-area scenario. However, in practice, multi-IRS will be deployed in multi-area scenarios in future networks. Consequently, considering the single-IRS single-area scenario will have inferior performance. In this paper, to address the aforementioned issue, we propose an efficient resource provisioning scheme for multi-IRS multi-area scenarios in MEC networks. We first model the problem as a cooperative multi-agent reinforcement learning process, where each agent manages one area and all agents share the network bandwidth and computation resources. Then, we propose a multi-agent actor-critic method with an attention mechanism for resource management with latency guarantee. Finally, we conduct extensive simulations to verify the effectiveness of the proposed scheme. Our scheme can reduce the required computation resources by up to 11.84% when compared with the benchmark works. It is also shown that our proposed scheme can improve the efficiency of resource allocation and scale well with the increasing demand from IoT devices.","PeriodicalId":353365,"journal":{"name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","volume":"227 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":"124508881","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

AdvTraffic: Obfuscating Encrypted Traffic with Adversarial Examples AdvTraffic:混淆加密流量与对抗性的例子

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-06-10 DOI: 10.1109/IWQoS54832.2022.9812875

Hao Liu, Jimmy Dani, Hongkai Yu, Wenhai Sun, Boyang Wang

{"title":"AdvTraffic: Obfuscating Encrypted Traffic with Adversarial Examples","authors":"Hao Liu, Jimmy Dani, Hongkai Yu, Wenhai Sun, Boyang Wang","doi":"10.1109/IWQoS54832.2022.9812875","DOIUrl":"https://doi.org/10.1109/IWQoS54832.2022.9812875","url":null,"abstract":"Website fingerprinting can reveal which sensitive website a user visits over encrypted network traffic. Obfuscating encrypted traffic, e.g., adding dummy packets, is considered as a primary approach to defend against website fingerprinting. How-ever, existing defenses relying on traffic obfuscation are either ineffective or introduce significant overheads. As recent website fingerprinting attacks heavily rely on deep neural networks to achieve high accuracy, producing adversarial examples could be utilized as a new way to obfuscate encrypted traffic. Unfortunately, existing adversarial example algorithms are designed for images and do not consider unique challenges for network traffic.In this paper, we design a new method, named AdvTraffic, which can customize perturbations produced by any existing adversarial example algorithm on images and derive adversarial examples over encrypted traffic. Our experimental results show that the integration of AdvTraffic, particularly with Generative Adversarial Networks, can effectively mitigate the accuracy of website fingerprinting from 95.0% to 10.2%, even if an attacker retrains a classifier with defended traffic. Compared to other defenses, our method outperforms most of them in mitigating attack accuracy and offers the lowest bandwidth overhead.","PeriodicalId":353365,"journal":{"name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","volume":"53 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":"129248634","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

JCSP: Joint Caching and Service Placement for Edge Computing Systems JCSP:边缘计算系统的联合缓存和服务放置

2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) Pub Date : 2022-05-09 DOI: 10.1109/IWQoS54832.2022.9812888

Yi Gao, G. Casale

{"title":"JCSP: Joint Caching and Service Placement for Edge Computing Systems","authors":"Yi Gao, G. Casale","doi":"10.1109/IWQoS54832.2022.9812888","DOIUrl":"https://doi.org/10.1109/IWQoS54832.2022.9812888","url":null,"abstract":"With constrained resources, what, where, and how to cache at the edge is one of the key challenges for edge computing systems. The cached items include not only the application data contents but also the local caching of edge services that handle incoming requests. However, current systems separate the contents and services without considering the latency interplay of caching and queueing. Therefore, in this paper, we propose a novel class of stochastic models that enable the optimization of content caching and service placement decisions jointly. We first explain how to apply layered queueing networks (LQNs) models for edge service placement and show that combining this with genetic algorithms provides higher accuracy in resource allocation than an established baseline. Next, we extend LQNs with caching components to establish a joint modeling method for content caching and service placement (JCSP) and present analytical methods to analyze the resulting model. Finally, we simulate real-world Azure traces to evaluate the JCSP method and find that JCSP achieves up to 35% improvement in response time and 500MB reduction in memory usage than baseline heuristics for edge caching resource allocation.","PeriodicalId":353365,"journal":{"name":"2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130000940","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