2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)最新文献_第2页

Additive and Subtractive Cuckoo Filters 加法和减法杜鹃过滤器

2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS) Pub Date : 2020-06-01 DOI: 10.1109/IWQoS49365.2020.9213014

Kun Huang, Tong Yang

{"title":"Additive and Subtractive Cuckoo Filters","authors":"Kun Huang, Tong Yang","doi":"10.1109/IWQoS49365.2020.9213014","DOIUrl":"https://doi.org/10.1109/IWQoS49365.2020.9213014","url":null,"abstract":"Bloom filters (BFs) are fast and space-efficient data structures used for set membership queries in many applications. BFs are required to satisfy three key requirements: low space cost, high-speed lookups, and fast updates. Prior works do not satisfy these requirements at the same time. The standard BF does not support deletions of items and the variants that support deletions need additional space or performance overhead. The state-of-the-art cuckoo filters (CF) has high performance with seemingly low space cost. However, the CF suffers a critical issue of varying space cost per item. This is because the exclusive-OR (XOR) operation used by the CF requires the total number of buckets to be a power of two, leading to the space inflation. To address the issue, in this paper we propose a scalable variant of the cuckoo filter called additive and subtractive cuckoo filter (ASCF). We aim to improve the space efficiency while sustaining comparably high performance. The ASCF uses the addition and subtraction (ADD/SUB) operations instead of the XOR operation to compute an item's two candidate bucket indexes based on its fingerprint. Experimental results show that the ASCF achieves both low space cost and high performance. Compared to the CF, the ASCF reduces up to 1.9x space cost per item while maintaining the same lookup and update throughput. In addition, the ASCF outperforms other filters in both space cost and performance.","PeriodicalId":177899,"journal":{"name":"2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123600681","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

Decimeter-Level WiFi Tracking in Real-Time 分米级实时WiFi跟踪

2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS) Pub Date : 2020-06-01 DOI: 10.1109/IWQoS49365.2020.9212880

Zheng Yang, Wei Gong

引用次数: 4

Private Deep Neural Network Models Publishing for Machine Learning as a Service 面向机器学习即服务的私有深度神经网络模型发布

2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS) Pub Date : 2020-06-01 DOI: 10.1109/IWQoS49365.2020.9212853

Yunlong Mao, Boyu Zhu, Wenbo Hong, Zhifei Zhu, Yuan Zhang, Sheng Zhong

{"title":"Private Deep Neural Network Models Publishing for Machine Learning as a Service","authors":"Yunlong Mao, Boyu Zhu, Wenbo Hong, Zhifei Zhu, Yuan Zhang, Sheng Zhong","doi":"10.1109/IWQoS49365.2020.9212853","DOIUrl":"https://doi.org/10.1109/IWQoS49365.2020.9212853","url":null,"abstract":"Machine learning as a service has emerged recently to relieve tensions between heavy deep learning tasks and increasing application demands. A deep learning service provider could help its clients to benefit from deep learning techniques at an affordable price instead of huge resource consumption. However, the service provider may have serious concerns about model privacy when a deep neural network model is published. Previous model publishing solutions mainly depend on additional artificial noise. By adding elaborated noises to parameters or gradients during the training phase, strong privacy guarantees like differential privacy could be achieved. However, this kind of approach cannot give guarantees on some other aspects, such as the quality of the disturbingly trained model and the convergence of the modified learning algorithm. In this paper, we propose an alternative private deep neural network model publishing solution, which caused no interference in the original training phase. We provide privacy, convergence and quality guarantees for the published model at the same time. Furthermore, our solution can achieve a smaller privacy budget when compared with artificial noise based training solutions proposed in previous works. Specifically, our solution gives an acceptable test accuracy with privacy budget ∊ = 1. Meanwhile, membership inference attack accuracy will be deceased from nearly 90% to around 60% across all classes.","PeriodicalId":177899,"journal":{"name":"2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123870208","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

Taming the Wildcards: Towards Dependency-free Rule Caching with FreeCache 驯服通配符:用FreeCache实现无依赖规则缓存

2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS) Pub Date : 2020-06-01 DOI: 10.1109/IWQoS49365.2020.9212969

Rui Li, Bohan Zhao, Ruixin Chen, Jin Zhao

{"title":"Taming the Wildcards: Towards Dependency-free Rule Caching with FreeCache","authors":"Rui Li, Bohan Zhao, Ruixin Chen, Jin Zhao","doi":"10.1109/IWQoS49365.2020.9212969","DOIUrl":"https://doi.org/10.1109/IWQoS49365.2020.9212969","url":null,"abstract":"Wildcard rules are implemented in various important networking scenarios, including QoS, firewall, access control, and network traffic monitoring and analysis. However, there are cross-rule dependencies between wildcard rules, which both increase significant overhead and affect the semantic correctness of packet classification when caching rules. Considerable efforts have been made to mitigate the impacts of the dependency issue in rule caching, but it is still a bottleneck for cache systems. In this paper, we show how to give applications the flexibility of completely dependency-free wildcard rule caching by decoupling the cached rules and their dependent rules. Our FreeCache scheme has wide applicability to packet classification devices with wildcard rule caching. We validate the effectiveness of FreeCache through two respects: (1) Implementing various cache algorithms (e.g., LSTM) and cache replacement algorithms (e.g., ARC, LIRS) that are difficult to use in dependency-bound situations in the cache system with FreeCache. (2) Developing a prototype in a Software-Defined Network (SDN), where hybrid OpenFlow switches use TCAM as cache and RAM as auxiliary memory. Our experimental results reveal that FreeCache improves the cache performance by up to 60.88% in the offline scenario. FreeCache also offers the promise of applying any existing caching algorithms to wildcard rule caching while guaranteeing the properties of semantic correctness and equivalence.","PeriodicalId":177899,"journal":{"name":"2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130145245","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: 9

Exploiting Rateless Codes and Cross-Layer Optimization for Low-Power Wide-Area Networks 低功耗广域网的无速率码开发与跨层优化

2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS) Pub Date : 2020-06-01 DOI: 10.1109/IWQoS49365.2020.9212919

Jiamei Lv, Gonglong Chen, Wei Dong

{"title":"Exploiting Rateless Codes and Cross-Layer Optimization for Low-Power Wide-Area Networks","authors":"Jiamei Lv, Gonglong Chen, Wei Dong","doi":"10.1109/IWQoS49365.2020.9212919","DOIUrl":"https://doi.org/10.1109/IWQoS49365.2020.9212919","url":null,"abstract":"Long communication range and low energy consumption are two most important design goals of Low-Power Wide-Area Networks (LPWAN), however, many prior works have revealed that the performance of LPWAN in practical scenarios is not satisfactory. Although there are PHY-layer and link layer approaches proposed to improve the performance of LPWAN, they either rely heavily on the hardware modifications or suffer from low data recovery capability especially with bursty packet loss pattern. In this paper, we propose a practical system, eLoRa, for COTS devices. eLoRa utilizes rateless codes and jointly decoding with multiple gateways to extend the communication range and lifetime of LoRaWAN. To further improve the performance of LoRaWAN, eLoRa optimizes parameters of the PHY-layer (e.g., spreading factor) and the link layer (e.g, block length). We implement eLoRa on COTS LoRa devices, and conduct extensive experiments on outdoor testbed to evaluate the effectiveness of eLoRa. Results show that eLoRa can effectively improve the communication range of DaRe and LoRaWAN by 43.2% and 55.7% with packet reception ratio higher than 60%, and increase the expected lifetime of DaRe and LoRaWAN by 18.3% and 46.6%.","PeriodicalId":177899,"journal":{"name":"2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130193121","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: 8

Towards Lightweight Serverless Computing via Unikernel as a Function 通过Unikernel作为函数实现轻量级无服务器计算

2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS) Pub Date : 2020-06-01 DOI: 10.1109/IWQoS49365.2020.9213020

B. Tan, Haikun Liu, J. Rao, Xiaofei Liao, Hai Jin, Yu Zhang

{"title":"Towards Lightweight Serverless Computing via Unikernel as a Function","authors":"B. Tan, Haikun Liu, J. Rao, Xiaofei Liao, Hai Jin, Yu Zhang","doi":"10.1109/IWQoS49365.2020.9213020","DOIUrl":"https://doi.org/10.1109/IWQoS49365.2020.9213020","url":null,"abstract":"Serverless computing, also known as “Function as a Service (FaaS)”, is emerging as an event-driven paradigm of cloud computing. In the FaaS model, applications are programmed in the form of functions that are executed and managed separately. Functions are triggered by cloud users and are provisioned dynamically through containers or virtual machines (VMs). The startup delays of containers or VMs usually lead to rather high latency of response to cloud users. Moreover, the communication between different functions generally relies on virtual net devices or shared memory, and may cause extremely high performance overhead. In this paper, we propose Unikernel-as-a-Function (UaaF), a much more lightweight approach to serverless computing. Applications are abstracted as a combination of different functions, and each function are built as an unikernel in which the function is linked with a specified minimum-sized library operating system (LibOS). UaaF offers extremely low startup latency to execute functions, and an efficient communication model to speed up inter-functions interactions. We exploit an new hardware technique (namely VMFUNC) to invoke functions in other unikernels seamlessly (mostly like inter-process communications), without suffering performance penalty of VM Exits. We implement our proof-of-concept prototype based on KVM and deploy UaaF in three unikernels (MirageOS, IncludeOS, and Solo5). Experimental results show that U aaF can significantly reduce the startup latency and memory usage of serverless cloud applications. Moreover, the VMFUNC-based communication model can also significantly improve the performance of function invocations between different unikernels.","PeriodicalId":177899,"journal":{"name":"2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117208665","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: 7

GeoClone: Online Task Replication and Scheduling for Geo-Distributed Analytics under Uncertainties GeoClone:不确定条件下地理分布式分析的在线任务复制和调度

2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS) Pub Date : 2020-06-01 DOI: 10.1109/IWQoS49365.2020.9212862

Tiantian Wang, Zhuzhong Qian, Lei Jiao, Xin Li, Sanglu Lu

{"title":"GeoClone: Online Task Replication and Scheduling for Geo-Distributed Analytics under Uncertainties","authors":"Tiantian Wang, Zhuzhong Qian, Lei Jiao, Xin Li, Sanglu Lu","doi":"10.1109/IWQoS49365.2020.9212862","DOIUrl":"https://doi.org/10.1109/IWQoS49365.2020.9212862","url":null,"abstract":"The execution and completion of analytics jobs can be significantly inflated by the slowest tasks contained. Despite task replication is well-adopted to reduce such straggler latency, existing replication strategies are unsuitable for geo-distributed analytics environments that are highly dynamic, uncertain, and heterogeneous. In this paper, we firstly model the task replication and scheduling problem over time, capturing the geo-analytics features. Afterwards, we design an online algorithm, GeoClone, to select tasks to replicate and select sites to execute the task replicas in an irrevocably online manner, through jointly considering the execution progress of each job and the resource performance in each site. We rigorously prove the competitive ratio to exhibit the theoretical performance guarantee of GeoClone, compared against the offline optimal algorithm which knows all the inputs at once beforehand. Finally, we implement GeoClone with Spark and Yarn for experiments and also conduct extensive large-scale simulations, which confirms GeoClone's practical superiority over multiple state-of-the-art replication strategies.","PeriodicalId":177899,"journal":{"name":"2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133484804","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

DeepQoE: Real-time Measurement of Video QoE from Encrypted Traffic with Deep Learning DeepQoE:利用深度学习从加密流量中实时测量视频QoE

2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS) Pub Date : 2020-06-01 DOI: 10.1109/IWQoS49365.2020.9212897

Meng Shen, Jinpeng Zhang, Ke Xu, Liehuang Zhu, Jiangchuan Liu, Xiaojiang Du

{"title":"DeepQoE: Real-time Measurement of Video QoE from Encrypted Traffic with Deep Learning","authors":"Meng Shen, Jinpeng Zhang, Ke Xu, Liehuang Zhu, Jiangchuan Liu, Xiaojiang Du","doi":"10.1109/IWQoS49365.2020.9212897","DOIUrl":"https://doi.org/10.1109/IWQoS49365.2020.9212897","url":null,"abstract":"With the dramatic increase of video traffic on the Internet, video quality of experience (QoE) measurement becomes even more important, which provides network operators with an insight into the quality of their video delivery services. The widespread adoption of end-to-end encryption protocols such as SSL/TLS, however, sets a barrier to QoE monitoring as the most valuable indicators in cleartext traffic are no longer available after encryption. Existing studies on video QoE measurement in encrypted traffic support only coarse-grained QoE metrics or suffer from low accuracy. In this paper, we propose DeepQoE, a new approach that enables real-time video QoE measurement from encrypted traffic. We summarize critical fine-grained QoE metrics, including startup delay, rebuffering, and video resolutions. In order to achieve accurate and real-time inference of these metrics, we build DeepQoE by employing Convolutional Neural Networks (CNNs) with a sophisticated input and architecture design. More specifically, DeepQoE only leverages packet Round-Trip Time (RTT) in upstream traffic as its input. Evaluation results with real-world datasets collected from two popular content providers (i.e., YouTube and Bilibili) show that DeepQoE can improve QoE measurement accuracy by up to 22% over the state-of-the-art methods.","PeriodicalId":177899,"journal":{"name":"2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128950346","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: 17

Generative Adversarial Networks-based Privacy-Preserving 3D Reconstruction 基于生成对抗网络的隐私保护三维重建

2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS) Pub Date : 2020-06-01 DOI: 10.1109/IWQoS49365.2020.9213037

Qinya Li, Zhenzhe Zheng, Fan Wu, Guihai Chen

{"title":"Generative Adversarial Networks-based Privacy-Preserving 3D Reconstruction","authors":"Qinya Li, Zhenzhe Zheng, Fan Wu, Guihai Chen","doi":"10.1109/IWQoS49365.2020.9213037","DOIUrl":"https://doi.org/10.1109/IWQoS49365.2020.9213037","url":null,"abstract":"A large-scale image collection is crucial to the success of 3D reconstruction. Crowdsourcing, as a new pattern, can be utilized to collect high-quality images in an efficient way. However, the sensitive information in images may be exposed during the image transmission process. The general privacy policies perhaps will cause the loss or change of critical information, which may give rise to a decline in the performance of 3D reconstruction. Hence, how to achieve image privacy-preserving while guaranteeing to reconstruct a complete 3D model is important and significant. In this paper, we propose PicPrivacy to address this problem, which consists of three parts. (1) Using a pre-trained deep convolution neural network to segment sensitive information and erase it from images. (2) Using a GAN-based image feature completion algorithm to repair blank regions and minimize the absolute information gap between generated images and raw ones. (3) Taking generated images as the input of 3D reconstruction and using a structure-from-motion algorithm to reconstruct 3D models. Finally, we extensively evaluate the performance of PicPrivacy on realworld datasets. The results demonstrate that PicPrivacy not only achieves individual privacy-preserving but also can guarantee to create complete 3D models.","PeriodicalId":177899,"journal":{"name":"2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114876460","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

Delay-Sensitive Computation Partitioning for Mobile Augmented Reality Applications 移动增强现实应用的延迟敏感计算分区

2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS) Pub Date : 2020-06-01 DOI: 10.1109/IWQoS49365.2020.9212917

Chaokun Zhang, Rong Zheng, Yong Cui, Chenhe Li, Jianping Wu

引用次数: 5