Pub Date : 2018-06-01DOI: 10.1109/IWQoS.2018.8624145
Xiaoyu Zhu, Jie Wu, Wei Chang, Guojun Wang, Qin Liu
Consider a database where each record has multiple attributes. An untrusted server is in charge of processing queries over this database, and we want to provide a mechanism for users to verify the correctness of their query results. Here each query, referred to as a multi-dimensional top-$k$ query, retrieves $k$ records whose output with user-supplied ranking function is among top $k$. Multi-dimensional top-$k$ query is widely used in real applications. However, as the traditional query authentication methods cannot be directly deployed on multi-dimensional top-$k$ query, it is still a challenging problem to authenticate the multi-dimensional top-$k$ query results. In this paper, we propose an authentication solution to support multi-dimensional top-$k$ query based on signature chain. By using signature chain for each record and its successors on each dimension, our solution allows users to efficiently verify the soundness and completeness of multi-dimensional top-$k$ query results. Through theoretical analysis and simulation, we demonstrate the effectiveness of our proposed solution.
考虑一个数据库，其中每条记录都有多个属性。一个不受信任的服务器负责处理该数据库上的查询，我们希望为用户提供一种机制来验证其查询结果的正确性。这里的每个查询(称为多维top- k查询)检索k条记录，这些记录的输出(使用用户提供的排序函数)位于top- k之间。多维top- k查询在实际应用中得到了广泛的应用。然而，由于传统的查询认证方法不能直接部署在多维top- k -$查询上，因此对多维top- k -$查询结果的认证仍然是一个具有挑战性的问题。本文提出了一种基于签名链的支持多维top- k查询的认证方案。通过对每个维度上的每个记录及其后续记录使用签名链，我们的解决方案允许用户有效地验证多维top- k查询结果的健全性和完整性。通过理论分析和仿真，验证了该方法的有效性。
{"title":"Authentication of Multi-Dimensional Top-$K$ Query on Untrusted Server","authors":"Xiaoyu Zhu, Jie Wu, Wei Chang, Guojun Wang, Qin Liu","doi":"10.1109/IWQoS.2018.8624145","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624145","url":null,"abstract":"Consider a database where each record has multiple attributes. An untrusted server is in charge of processing queries over this database, and we want to provide a mechanism for users to verify the correctness of their query results. Here each query, referred to as a multi-dimensional top-$k$ query, retrieves $k$ records whose output with user-supplied ranking function is among top $k$. Multi-dimensional top-$k$ query is widely used in real applications. However, as the traditional query authentication methods cannot be directly deployed on multi-dimensional top-$k$ query, it is still a challenging problem to authenticate the multi-dimensional top-$k$ query results. In this paper, we propose an authentication solution to support multi-dimensional top-$k$ query based on signature chain. By using signature chain for each record and its successors on each dimension, our solution allows users to efficiently verify the soundness and completeness of multi-dimensional top-$k$ query results. Through theoretical analysis and simulation, we demonstrate the effectiveness of our proposed solution.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131580580","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}
In online approximate stream processing, customers generally submit their requests with some specific quality requirements (e.g. maximum error). This raises a critical problem that online quality control is necessary to meet customized requirements. Since continuous arriving data needs to be processed immediately, it brings the difficulty of acquiring knowledge which significantly affects the efficiency of sampling. Hence, it's more challenging to ensure a prescribed level of quality without knowledge about data. In this paper, we present an adaptive approximate processing framework for online stream applications to address the challenges mentioned above. Specially, we first design a new data knowledge learning scheme to stratify the arriving stream data. Then, based on the online learning results, we propose a dynamic sampling strategy with the consideration of the stream rate. Finally, we further present a double-check error control mechanism to manage the output quality. Experiments with real world datasets show that the proposed approximate framework is not only applicable to different data distributions, but also provides a customized error control.
{"title":"An Online Approximate Stream Processing Framework with Customized Error Control","authors":"Xiaohui Wei, Yuanyuan Liu, Xingwang Wang, Shang Gao","doi":"10.1109/IWQoS.2018.8624132","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624132","url":null,"abstract":"In online approximate stream processing, customers generally submit their requests with some specific quality requirements (e.g. maximum error). This raises a critical problem that online quality control is necessary to meet customized requirements. Since continuous arriving data needs to be processed immediately, it brings the difficulty of acquiring knowledge which significantly affects the efficiency of sampling. Hence, it's more challenging to ensure a prescribed level of quality without knowledge about data. In this paper, we present an adaptive approximate processing framework for online stream applications to address the challenges mentioned above. Specially, we first design a new data knowledge learning scheme to stratify the arriving stream data. Then, based on the online learning results, we propose a dynamic sampling strategy with the consideration of the stream rate. Finally, we further present a double-check error control mechanism to manage the output quality. Experiments with real world datasets show that the proposed approximate framework is not only applicable to different data distributions, but also provides a customized error control.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131671117","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}
Pub Date : 2018-06-01DOI: 10.1109/IWQoS.2018.8624125
Weiping Zhu, Xing Meng, Xiaolei Peng, Jiannong Cao, M. Raynal
RFID-based stocktaking uses RFID technology to verify the presence of objects in a region e.g., a warehouse or a library. The existing approaches for this purpose assume that an inventory list of objects in the interrogation region of an RFID reader is known. This is not true in some cases. For example, for a handheld RFID reader, only the objects in a larger region (e.g., the warehouse) rather than in its interrogation region can be known. The additional objects significantly increase the time required for stocktaking. In this paper, we propose a time-efficient stocktaking algorithm called CLS (Coarse-grained inventory list based stocktaking) to solve this problem. We transform the problem to a missing tag identification problem with a large missing rate. CLS enables multiple missing objects to hash to a single time slot and thus verifies them together. CLS also improves the existing approaches by utilizing more kinds of RFID collisions and reducing approximately one-fourth of the amount of data sent by the reader. Extensive simulations are performed and the results show CLS outperforms the best existing algorithm.
{"title":"Time-Efficient RFID-Based Stocktaking with a Coarse-Grained Inventory List","authors":"Weiping Zhu, Xing Meng, Xiaolei Peng, Jiannong Cao, M. Raynal","doi":"10.1109/IWQoS.2018.8624125","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624125","url":null,"abstract":"RFID-based stocktaking uses RFID technology to verify the presence of objects in a region e.g., a warehouse or a library. The existing approaches for this purpose assume that an inventory list of objects in the interrogation region of an RFID reader is known. This is not true in some cases. For example, for a handheld RFID reader, only the objects in a larger region (e.g., the warehouse) rather than in its interrogation region can be known. The additional objects significantly increase the time required for stocktaking. In this paper, we propose a time-efficient stocktaking algorithm called CLS (Coarse-grained inventory list based stocktaking) to solve this problem. We transform the problem to a missing tag identification problem with a large missing rate. CLS enables multiple missing objects to hash to a single time slot and thus verifies them together. CLS also improves the existing approaches by utilizing more kinds of RFID collisions and reducing approximately one-fourth of the amount of data sent by the reader. Extensive simulations are performed and the results show CLS outperforms the best existing algorithm.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129684897","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}
Pub Date : 2018-06-01DOI: 10.1109/IWQoS.2018.8624138
Mallesham Dasari, Shruti Sanadhya, C. Vlachou, Kyu-Han Kim, Samir R Das
Mobile video traffic is dominant in cellular and enterprise wireless networks. With the advent of myriads of applications from video telephony and streaming to virtual reality, network administrators face the challenge to provide high quality of experience (QoE) in the face of diverse wireless conditions and application contents. Yet, state-of-the-art networks lack analytics for QoE, as this requires support from the application or user feedback. While there are existing techniques to map quality of service (QoS) to QoE by training machine learning (ML) models without requiring user feedback, these techniques are limited to only few applications (e.g., Skype), due to insufficient QoE ground-truth annotation for ML. To address these limitations, we focus on video telephony applications and model key artefacts of spatial and temporal video QoE. Our key contribution is designing content- and device-independent metrics and training across diverse WiFi conditions. We show that our metrics achieve a median 90% accuracy by comparing with mean-opinion-score (MOS) from more than 200 users and 800 video samples. Our content-independent metrics significantly reduce the MOS prediction error of previous works and are validated over three popular video telephony applications – Skype, FaceTime and Google Hangouts.
{"title":"Scalable Ground-Truth Annotation for Video QoE Modeling in Enterprise WiFi","authors":"Mallesham Dasari, Shruti Sanadhya, C. Vlachou, Kyu-Han Kim, Samir R Das","doi":"10.1109/IWQoS.2018.8624138","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624138","url":null,"abstract":"Mobile video traffic is dominant in cellular and enterprise wireless networks. With the advent of myriads of applications from video telephony and streaming to virtual reality, network administrators face the challenge to provide high quality of experience (QoE) in the face of diverse wireless conditions and application contents. Yet, state-of-the-art networks lack analytics for QoE, as this requires support from the application or user feedback. While there are existing techniques to map quality of service (QoS) to QoE by training machine learning (ML) models without requiring user feedback, these techniques are limited to only few applications (e.g., Skype), due to insufficient QoE ground-truth annotation for ML. To address these limitations, we focus on video telephony applications and model key artefacts of spatial and temporal video QoE. Our key contribution is designing content- and device-independent metrics and training across diverse WiFi conditions. We show that our metrics achieve a median 90% accuracy by comparing with mean-opinion-score (MOS) from more than 200 users and 800 video samples. Our content-independent metrics significantly reduce the MOS prediction error of previous works and are validated over three popular video telephony applications – Skype, FaceTime and Google Hangouts.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"220 1-4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131987417","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}
Pub Date : 2018-06-01DOI: 10.1109/IWQoS.2018.8624175
Xiaohui Nie, Youjian Zhao, Dan Pei, Guo Chen, Kaixin Sui, Jiyang Zhang
Latency, which directly affects the user experience and revenue of web services, is far from ideal in reality, due to the well-known TCP flow startup problem. Specifically, since TCP starts from a conservative and static initial window (IW, 2∼4 or 10), most of the web flows are too short to have enough time to find its best congestion window before the session ends. As a result, TCP cannot fully utilize the available bandwidth in the modern Internet. In this paper, we propose to use group-based reinforcement learning (RL) to enable a web server, through trial-and-error, to dynamically set a suitable IW for a web flow before its transmission starts. Our proposed system, SmartIW, collects TCP flow performance metrics (e.g., transmission time, loss rate, RTT) in real-time without any client assistance. Then these metrics are aggregated into groups with similar features (subnet, ISP, province, etc.) to satisfy RL's requirement. SmartIW has been deployed in one of the top global search engines for more than a year. Our online and testbed experiments show that, compared to the common practice of $boldsymbol{IW}=10$, SmartIW can reduce the average transmission time by 23% to 29%.
{"title":"Reducing Web Latency Through Dynamically Setting TCP Initial Window with Reinforcement Learning","authors":"Xiaohui Nie, Youjian Zhao, Dan Pei, Guo Chen, Kaixin Sui, Jiyang Zhang","doi":"10.1109/IWQoS.2018.8624175","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624175","url":null,"abstract":"Latency, which directly affects the user experience and revenue of web services, is far from ideal in reality, due to the well-known TCP flow startup problem. Specifically, since TCP starts from a conservative and static initial window (IW, 2∼4 or 10), most of the web flows are too short to have enough time to find its best congestion window before the session ends. As a result, TCP cannot fully utilize the available bandwidth in the modern Internet. In this paper, we propose to use group-based reinforcement learning (RL) to enable a web server, through trial-and-error, to dynamically set a suitable IW for a web flow before its transmission starts. Our proposed system, SmartIW, collects TCP flow performance metrics (e.g., transmission time, loss rate, RTT) in real-time without any client assistance. Then these metrics are aggregated into groups with similar features (subnet, ISP, province, etc.) to satisfy RL's requirement. SmartIW has been deployed in one of the top global search engines for more than a year. Our online and testbed experiments show that, compared to the common practice of $boldsymbol{IW}=10$, SmartIW can reduce the average transmission time by 23% to 29%.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132843777","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}
Pub Date : 2018-06-01DOI: 10.1109/IWQoS.2018.8624157
Asif M. Yousuf, Edward M. Rochester, Behnam Ousat, Majid Ghaderi
LoRa is a leading Low-Power Wide-Area Network (LPWAN) technology for Internet of Things (IoT). While LoRa networks are rapidly being deployed around the world, it is important to understand the capabilities and limitations of this technology in terms of its throughput, coverage and scalability. Using a combination of real-world measurements and high fidelity simulations, this paper aims at characterizing the performance of LoRa. Specifically, we present and analyze measurement data collected from a city-wide LoRa deployment in order to characterize the throughput and coverage of LoRa. Moreover, using a custom-built simulator tuned based on our measurement data, we present extensive simulation results in order to characterize the scalability of LoRa under a variety of traffic and network settings. Our measurement results show that as few as three gateways are sufficient to cover a dense urban area within an approximately 15 Km radius. Also, a single gateway can support as many as 105 end devices, each sending 50 bytes of data every hour with negligible packet drops. On the negative side, while a throughput of up to 5.5 Kbps can be achieved over a single 125 KHz channel at the physical layer, the throughput achieved at the application layer is substantially lower, less than 1 Kbps, due to the network protocols overhead.
{"title":"Throughput, Coverage and Scalability of LoRa LPWAN for Internet of Things","authors":"Asif M. Yousuf, Edward M. Rochester, Behnam Ousat, Majid Ghaderi","doi":"10.1109/IWQoS.2018.8624157","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624157","url":null,"abstract":"LoRa is a leading Low-Power Wide-Area Network (LPWAN) technology for Internet of Things (IoT). While LoRa networks are rapidly being deployed around the world, it is important to understand the capabilities and limitations of this technology in terms of its throughput, coverage and scalability. Using a combination of real-world measurements and high fidelity simulations, this paper aims at characterizing the performance of LoRa. Specifically, we present and analyze measurement data collected from a city-wide LoRa deployment in order to characterize the throughput and coverage of LoRa. Moreover, using a custom-built simulator tuned based on our measurement data, we present extensive simulation results in order to characterize the scalability of LoRa under a variety of traffic and network settings. Our measurement results show that as few as three gateways are sufficient to cover a dense urban area within an approximately 15 Km radius. Also, a single gateway can support as many as 105 end devices, each sending 50 bytes of data every hour with negligible packet drops. On the negative side, while a throughput of up to 5.5 Kbps can be achieved over a single 125 KHz channel at the physical layer, the throughput achieved at the application layer is substantially lower, less than 1 Kbps, due to the network protocols overhead.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121955104","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}
Pub Date : 2018-06-01DOI: 10.1109/IWQoS.2018.8624148
Yiting He, Xiaoyi Fan, Feng Wang, Fangxin Wang, Jiangchuan Liu
Automobiles have become one of the necessities of modern life and deeply penetrated into our daily activities. They unfortunately also introduce numerous social problems, among which traffic accidents are most notoriously threatening automobile drivers and other road users. Advanced driver-assistance systems (ADAS) are under rapid development in recent years, which can necessarily reduce or even eliminate the driver errors, significantly relieving on drivers suffering or stress. These state-of-the-art ADAS mainly rely on built-in cameras, radars and ultrasound sensors to provide road sensing services for object detection, which are further advanced by recent explosion of vision and neural network technologies.
{"title":"Edge Computing Empowered Generative Adversarial Networks for Realtime Road Sensing","authors":"Yiting He, Xiaoyi Fan, Feng Wang, Fangxin Wang, Jiangchuan Liu","doi":"10.1109/IWQoS.2018.8624148","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624148","url":null,"abstract":"Automobiles have become one of the necessities of modern life and deeply penetrated into our daily activities. They unfortunately also introduce numerous social problems, among which traffic accidents are most notoriously threatening automobile drivers and other road users. Advanced driver-assistance systems (ADAS) are under rapid development in recent years, which can necessarily reduce or even eliminate the driver errors, significantly relieving on drivers suffering or stress. These state-of-the-art ADAS mainly rely on built-in cameras, radars and ultrasound sensors to provide road sensing services for object detection, which are further advanced by recent explosion of vision and neural network technologies.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122174062","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}
Pub Date : 2018-06-01DOI: 10.1109/IWQoS.2018.8624122
Renhai Xu, Wenxin Li, Keqiu Li, Xiaobo Zhou
Data-parallel applications generate a mix of coflows with and without deadlines. Deadline coflows are mission-critical and must be completed within deadlines, while non-deadline coflows desire to be completed as soon as possible. Scheduling such mix-coflows is an important problem in modern datacenters. However, existing solutions only focus on one of the two types of coflows: they either solely focus on meeting the deadlines of deadline-aware coflows or reducing the coflow completion times (CCTs) of non-deadline coflows. In this paper, we study the problem of optimizing deadline and non-deadline coflows simultaneously. To this end, we present a new optimization framework, mixCoflow, to schedule deadline coflows with the objective of minimizing and balancing their bandwidth footprint, such that non-deadline coflows can be scheduled as early as possible. Specifically, we develop the mathematical model and formulate the scheduling problem for deadline coflows as a lexicographical min-max integer linear programming (ILP) problem. Through rigorous theoretical analysis, this ILP problem has been proved to be equivalent to a linear programming (LP) problem that can be solved with standard LP solvers. By solving this LP, mixCoflow is able to balance the bandwidth footprint of deadline coflows while guaranteeing their deadlines. As a result, non-deadline coflows can be scheduled as soon as possible whenever they arrive. To demonstrate the effectiveness of our work, we have conducted extensive simulations based on a widely used Facebook data trace. The simulation results verify that mixCoflow can achieve significant improvement on the average CCT of non-deadline coflows, at no expense of increasing the deadline miss rates of deadline coflows, when compared to the state-of-art solutions.
{"title":"Shaping Deadline Coflows to Accelerate Non-Deadline Coflows","authors":"Renhai Xu, Wenxin Li, Keqiu Li, Xiaobo Zhou","doi":"10.1109/IWQoS.2018.8624122","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624122","url":null,"abstract":"Data-parallel applications generate a mix of coflows with and without deadlines. Deadline coflows are mission-critical and must be completed within deadlines, while non-deadline coflows desire to be completed as soon as possible. Scheduling such mix-coflows is an important problem in modern datacenters. However, existing solutions only focus on one of the two types of coflows: they either solely focus on meeting the deadlines of deadline-aware coflows or reducing the coflow completion times (CCTs) of non-deadline coflows. In this paper, we study the problem of optimizing deadline and non-deadline coflows simultaneously. To this end, we present a new optimization framework, mixCoflow, to schedule deadline coflows with the objective of minimizing and balancing their bandwidth footprint, such that non-deadline coflows can be scheduled as early as possible. Specifically, we develop the mathematical model and formulate the scheduling problem for deadline coflows as a lexicographical min-max integer linear programming (ILP) problem. Through rigorous theoretical analysis, this ILP problem has been proved to be equivalent to a linear programming (LP) problem that can be solved with standard LP solvers. By solving this LP, mixCoflow is able to balance the bandwidth footprint of deadline coflows while guaranteeing their deadlines. As a result, non-deadline coflows can be scheduled as soon as possible whenever they arrive. To demonstrate the effectiveness of our work, we have conducted extensive simulations based on a widely used Facebook data trace. The simulation results verify that mixCoflow can achieve significant improvement on the average CCT of non-deadline coflows, at no expense of increasing the deadline miss rates of deadline coflows, when compared to the state-of-art solutions.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131690312","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}
Pub Date : 2018-06-01DOI: 10.1109/IWQoS.2018.8624143
Shanshan Wang, Zhenxiang Chen, Qiben Yan, Ke Ji, Lin Wang, Bo Yang, M. Conti
In recent years, the scale and diversity of malicious software on mobile networks are constantly increasing, thereby causing considerable danger to users' property and personal privacy. In this study, we devise a method that uses the URLs visited by applications to identify malicious apps. A multi-view neural network is used to create a malware detection model that emphasizes depth and width. This neural network can create multiple views of the input automatically and distribute soft attention weights to focus on different features of input. Multiple views preserve rich semantic information from input for classification without requiring complicated feature engineering. In addition, we conduct comprehensive experiments to compare the proposed method with others and verify the validity of the detection model. The experimental results show that our method has a certain timeliness. It can not only effectively detect malware discovered in different months of a certain year, but also detect potentially malicious apps in the third-party app market. We also compare the detection results of the proposed method on wild apps with 10 popular anti-virus scanners, and the final result shows that our approach ranks second in terms of detection performance.
{"title":"Deep and Broad Learning Based Detection of Android Malware via Network Traffic","authors":"Shanshan Wang, Zhenxiang Chen, Qiben Yan, Ke Ji, Lin Wang, Bo Yang, M. Conti","doi":"10.1109/IWQoS.2018.8624143","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624143","url":null,"abstract":"In recent years, the scale and diversity of malicious software on mobile networks are constantly increasing, thereby causing considerable danger to users' property and personal privacy. In this study, we devise a method that uses the URLs visited by applications to identify malicious apps. A multi-view neural network is used to create a malware detection model that emphasizes depth and width. This neural network can create multiple views of the input automatically and distribute soft attention weights to focus on different features of input. Multiple views preserve rich semantic information from input for classification without requiring complicated feature engineering. In addition, we conduct comprehensive experiments to compare the proposed method with others and verify the validity of the detection model. The experimental results show that our method has a certain timeliness. It can not only effectively detect malware discovered in different months of a certain year, but also detect potentially malicious apps in the third-party app market. We also compare the detection results of the proposed method on wild apps with 10 popular anti-virus scanners, and the final result shows that our approach ranks second in terms of detection performance.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114136100","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}
Ensuring high video quality of experience (QoE) on the broadcaster side is critical for interactive live streaming. However, measurements on multiple live streaming platforms show that they all suffer from broadcaster-side video quality degradation in the presence of transient bandwidth fluctuations. This paper presents Greedy Variable Bitrate (GVBR), a suite of solutions that optimizes the QoE through an approriate keyframe interval that trades cross-frame compression for lowered inter-frame interdependency, a simple-yet-efficient frame dropping strategy to prevent excessive frame drops, and a bitrate adaptation strategy customized for broadcasters who have shallow buffer. We compare GVBR with state-of-art algorithms in different network conditions, and find that GVBR can cut video interruption incidents by 90%, while achieving comparable bitrate.
{"title":"Improving Quality of Experience for Mobile Broadcasters in Personalized Live Video Streaming","authors":"Q. Ren, Yong Cui, Wenfei Wu, Changfeng Chen, Yuchi Chen, Jiangchuan Liu, Hongyi Huang","doi":"10.1109/IWQoS.2018.8624178","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624178","url":null,"abstract":"Ensuring high video quality of experience (QoE) on the broadcaster side is critical for interactive live streaming. However, measurements on multiple live streaming platforms show that they all suffer from broadcaster-side video quality degradation in the presence of transient bandwidth fluctuations. This paper presents Greedy Variable Bitrate (GVBR), a suite of solutions that optimizes the QoE through an approriate keyframe interval that trades cross-frame compression for lowered inter-frame interdependency, a simple-yet-efficient frame dropping strategy to prevent excessive frame drops, and a bitrate adaptation strategy customized for broadcasters who have shallow buffer. We compare GVBR with state-of-art algorithms in different network conditions, and find that GVBR can cut video interruption incidents by 90%, while achieving comparable bitrate.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117311485","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}
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