Pub Date : 2018-06-01DOI: 10.1109/IWQoS.2018.8624149
Rui Zhang, S. Zhang, Zhuzhong Qian, Mingjun Xiao, Jie Wu, Jidong Ge, Sanglu Lu
Electric vehicle (EV) is a promising technological tool for diminishing environmental impact caused by gasoline-consumed transportation. Due to the limited battery capacity, EVs need to be charged frequently in a static charging station and thus waste large amounts of time being out of service. Research previously conducted in this topic have proposed solutions for deployment of charging lanes that can charge in-motion EVs. However, they cannot guarantee that every EV can be operational in their respective entire route. Meanwhile, we observe that EVs have repetitive motions and may cyclically encounter with each other which no prior research having been investigated. In addition, the development on the circuit design of energy transmit antennas can render EVs to be able to bi-directionally, highly efficiently transfer energy between themselves. These two observations enable us to distribute energy among EVs in a collaborative and interactive manner. We consider the cases of both loss-less and lossy energy transfer between EVs. In both cases, we formulate the problem of minimizing the time needed (or energy transferred) to reach a given energy distribution into a series of linear programming problems. When compared with a state-of-the-art algorithm, extensive simulation results show that the proposed algorithms can reduce the balancing time and energy loss by up to 70.60% and 36.59%, respectively.
{"title":"Collaborative Interactive Wireless Charging in a Cyclic Mobispace","authors":"Rui Zhang, S. Zhang, Zhuzhong Qian, Mingjun Xiao, Jie Wu, Jidong Ge, Sanglu Lu","doi":"10.1109/IWQoS.2018.8624149","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624149","url":null,"abstract":"Electric vehicle (EV) is a promising technological tool for diminishing environmental impact caused by gasoline-consumed transportation. Due to the limited battery capacity, EVs need to be charged frequently in a static charging station and thus waste large amounts of time being out of service. Research previously conducted in this topic have proposed solutions for deployment of charging lanes that can charge in-motion EVs. However, they cannot guarantee that every EV can be operational in their respective entire route. Meanwhile, we observe that EVs have repetitive motions and may cyclically encounter with each other which no prior research having been investigated. In addition, the development on the circuit design of energy transmit antennas can render EVs to be able to bi-directionally, highly efficiently transfer energy between themselves. These two observations enable us to distribute energy among EVs in a collaborative and interactive manner. We consider the cases of both loss-less and lossy energy transfer between EVs. In both cases, we formulate the problem of minimizing the time needed (or energy transferred) to reach a given energy distribution into a series of linear programming problems. When compared with a state-of-the-art algorithm, extensive simulation results show that the proposed algorithms can reduce the balancing time and energy loss by up to 70.60% and 36.59%, respectively.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"792 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":"116134198","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.2018.8624147
Xiuwen Sun, Hao Li, Xingxing Lu, Dan Zhao, Zheng Peng, Chengchen Hu
Nowadays, Deep Packet Inspection (DPI) becomes a critical component of the network traffic detection applications. For comprehensive analysis of traffic, regular expression matching as the core technique of DPI is widely used. However, web services tend to compress their traffic for less data transmission, which challenges the regular expression matching to achieve wire-speed processing. In this paper, we propose Twins, a fast regular expression matching method over compressed traffic that leverages the returned states encoding in the compression to skip the bytes to be scanned. In our evaluation results, Twins can skip about 90% compression data and can achieve 1.5Gbps throughput, which gains 2.7∼3.4 performance boost to the state-of-the-art work.
{"title":"Towards a Fast Regular Expression Matching Method Over Compressed Traffic","authors":"Xiuwen Sun, Hao Li, Xingxing Lu, Dan Zhao, Zheng Peng, Chengchen Hu","doi":"10.1109/IWQoS.2018.8624147","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624147","url":null,"abstract":"Nowadays, Deep Packet Inspection (DPI) becomes a critical component of the network traffic detection applications. For comprehensive analysis of traffic, regular expression matching as the core technique of DPI is widely used. However, web services tend to compress their traffic for less data transmission, which challenges the regular expression matching to achieve wire-speed processing. In this paper, we propose Twins, a fast regular expression matching method over compressed traffic that leverages the returned states encoding in the compression to skip the bytes to be scanned. In our evaluation results, Twins can skip about 90% compression data and can achieve 1.5Gbps throughput, which gains 2.7∼3.4 performance boost to the state-of-the-art work.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"9 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":"115861148","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.2018.8624168
Zhihan Li, Youjian Zhao, Rong Liu, Dan Pei
For large Internet companies, it is very important to monitor a large number of KPIs (Key Performance Indicators) and detect anomalies to ensure the service quality and reliability. However, large-scale anomaly detection on millions of KPIs is very challenging due to the large overhead of model selection, parameter tuning, model training, or labeling. In this paper we argue that KPI clustering can help: we can cluster millions of KPIs into a small number of clusters and then select and train model on a per-cluster basis. However, KPI clustering faces new challenges that are not present in classic time series clustering: KPIs are typically much longer than other time series, and noises, anomalies, phase shifts and amplitude differences often change the shape of KPIs and mislead the clustering algorithm. To tackle the above challenges, in this paper we propose a robust and rapid KPI clustering algorithm, ROCKA. It consists of four steps: preprocessing, baseline extraction, clustering and assignment. These techniques help group KPIs according to their underlying shapes with high accuracy and efficiency. Our evaluation using real-world KPIs shows that ROCKA gets F-score higher than 0.85, and reduces model training time of a state-of-the-art anomaly detection algorithm by 90%, with only 15% performance loss.
{"title":"Robust and Rapid Clustering of KPIs for Large-Scale Anomaly Detection","authors":"Zhihan Li, Youjian Zhao, Rong Liu, Dan Pei","doi":"10.1109/IWQoS.2018.8624168","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624168","url":null,"abstract":"For large Internet companies, it is very important to monitor a large number of KPIs (Key Performance Indicators) and detect anomalies to ensure the service quality and reliability. However, large-scale anomaly detection on millions of KPIs is very challenging due to the large overhead of model selection, parameter tuning, model training, or labeling. In this paper we argue that KPI clustering can help: we can cluster millions of KPIs into a small number of clusters and then select and train model on a per-cluster basis. However, KPI clustering faces new challenges that are not present in classic time series clustering: KPIs are typically much longer than other time series, and noises, anomalies, phase shifts and amplitude differences often change the shape of KPIs and mislead the clustering algorithm. To tackle the above challenges, in this paper we propose a robust and rapid KPI clustering algorithm, ROCKA. It consists of four steps: preprocessing, baseline extraction, clustering and assignment. These techniques help group KPIs according to their underlying shapes with high accuracy and efficiency. Our evaluation using real-world KPIs shows that ROCKA gets F-score higher than 0.85, and reduces model training time of a state-of-the-art anomaly detection algorithm by 90%, with only 15% performance loss.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"61 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":"128201538","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.2018.8624171
Zijian Wang, Fuliang Li, Xingwei Wang, Tengfei Li, Tao Hong
The infrastructure-based networks will be unavailable in the case of infrastructure failures (e.g., earthquake and tsunami) or in crowded areas (e.g., concert and conference hall). This promotes the evolution of location communication systems, which also benefit offloading computing, mobile edge computing and mobile crowdsourcing. In this paper, we utilize Wi-Fi Direct (WFD) to develop a local communication system. We not only present an intra-group communication solution by the native characteristics of WFD, but also propose a general solution for the bidirectional inter-group communication, which is beyond the scope of WFD specifications. Experimental results show that the maximum throughput of intra-group communication could reach up to 31.7 Mbps, and the maximum throughput of inter-group communication is 8.26 Mbps.
在基础设施发生故障(例如，地震和海啸)或在拥挤地区(例如，音乐会和会议厅)时，基于基础设施的网络将无法使用。这促进了位置通信系统的发展，这也有利于卸载计算、移动边缘计算和移动众包。在本文中，我们利用Wi-Fi Direct (WFD)来开发一个本地通信系统。我们不仅利用WFD的固有特性提出了一种组内通信解决方案，而且还提出了一种超出WFD规范范围的组间双向通信的通用解决方案。实验结果表明，组内通信的最大吞吐量可达31.7 Mbps，组间通信的最大吞吐量可达8.26 Mbps。
{"title":"A WiFi-Direct Based Local Communication System","authors":"Zijian Wang, Fuliang Li, Xingwei Wang, Tengfei Li, Tao Hong","doi":"10.1109/IWQoS.2018.8624171","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624171","url":null,"abstract":"The infrastructure-based networks will be unavailable in the case of infrastructure failures (e.g., earthquake and tsunami) or in crowded areas (e.g., concert and conference hall). This promotes the evolution of location communication systems, which also benefit offloading computing, mobile edge computing and mobile crowdsourcing. In this paper, we utilize Wi-Fi Direct (WFD) to develop a local communication system. We not only present an intra-group communication solution by the native characteristics of WFD, but also propose a general solution for the bidirectional inter-group communication, which is beyond the scope of WFD specifications. Experimental results show that the maximum throughput of intra-group communication could reach up to 31.7 Mbps, and the maximum throughput of inter-group communication is 8.26 Mbps.","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":"130912274","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.2018.8624158
Jingxiu Su, Zhenyu Li, S. Grumbach, Kave Salamatian, Chunjing Han, Gaogang Xie
DNS is a critical component of Internet architecture. Almost all applications, in particular web based applications that constitute the large majority of current Internet traffic, leverage heavily on DNS. This makes DNS based measurements a promising tool for understanding global properties of Internet traffic, e.g., sites audience, traffic matrix. However, using passive DNS traces from local DNS servers is challenging because of DNS caching and NATs. The goal of this paper is twofold. First, we show how to correct the bias due to DNS cache and the wide use of NATs, to extract meaningful traffic information from DNS traces. The techniques are then used and validated over a large dataset (1011 records) containing two days of full DNS access from a major ISP providing both mobile and landline ADSL in China. Second, we focus on the tracking activity and show that although most sites accessed from China belong to Chinese corporations, most trackers belong to US ones. Mobile and ADSL platforms are alike.
{"title":"Toward Accurate Inference of Web Activities from Passive DNS Data","authors":"Jingxiu Su, Zhenyu Li, S. Grumbach, Kave Salamatian, Chunjing Han, Gaogang Xie","doi":"10.1109/IWQoS.2018.8624158","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624158","url":null,"abstract":"DNS is a critical component of Internet architecture. Almost all applications, in particular web based applications that constitute the large majority of current Internet traffic, leverage heavily on DNS. This makes DNS based measurements a promising tool for understanding global properties of Internet traffic, e.g., sites audience, traffic matrix. However, using passive DNS traces from local DNS servers is challenging because of DNS caching and NATs. The goal of this paper is twofold. First, we show how to correct the bias due to DNS cache and the wide use of NATs, to extract meaningful traffic information from DNS traces. The techniques are then used and validated over a large dataset (1011 records) containing two days of full DNS access from a major ISP providing both mobile and landline ADSL in China. Second, we focus on the tracking activity and show that although most sites accessed from China belong to Chinese corporations, most trackers belong to US ones. Mobile and ADSL platforms are alike.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"21 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":"133847757","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.2018.8624152
Fangxin Wang, Yifei Zhu, Feng Wang, Jiangchuan Liu
Nowadays online shopping has become explosively popular and the vast numbers of generated packages have brought great challenges to the traditional logistics industry, especially the last mile package delivery. Traditional delivery approaches rely on dedicated couriers for package dispatch, while the labor cost is quite expensive and the quality is hard to guarantee due to the diverse delivery addresses and tight deadlines. On the other hand, modern cities are full of available transportation resources such as private car trips. The mobile crowdsourcing through 4G/5G and vehicle-related communications enables the vehicle resources to be connected as an intelligent transportation system. As such, we believe ridesharing will be a core service for connected vehicles, which we refer to as Ridesharing as a Service (RaaS). In this paper, we focus on the quality of service (QoS) of RaaS in the last mile package delivery. Mining from real-world car trips, we build up a citywide routing graph and conduct a personalized travel cost prediction considering both the travel time of each driver and the fuel consumption of each vehicle. We then design an online algorithm to assign proper package delivery tasks to the submitted car trips, aiming to maximize the utility of the ridesharing service provider. Our extensive real-world trace-driven evaluations further demonstrate the superiority of our RaaS based package delivery.
如今，网络购物已经爆炸式地流行起来，大量产生的包裹给传统的物流行业带来了巨大的挑战，尤其是最后一英里的包裹递送。传统的快递方式依赖于专门的快递员进行包裹的派送，但由于派送地址多样、期限紧迫，人工成本相当昂贵，而且质量难以保证。另一方面，现代城市充满了可用的交通资源，比如私家车出行。通过4G/5G移动众包和车辆相关通信，将车辆资源连接起来，形成智能交通系统。因此，我们相信拼车将成为互联汽车的核心服务，我们称之为拼车即服务(ridessharing As a service，简称RaaS)。本文主要研究RaaS在最后一英里包交付中的服务质量问题。从现实世界的汽车出行中挖掘，我们建立了一个全市范围的路线图，并进行了个性化的出行成本预测，同时考虑了每个司机的出行时间和每辆车的燃油消耗。然后，我们设计了一个在线算法，为提交的汽车行程分配适当的包裹递送任务，旨在最大限度地提高拼车服务提供商的效用。我们广泛的实际跟踪驱动评估进一步证明了我们基于RaaS的包交付的优越性。
{"title":"Ridesharing as a Service: Exploring Crowdsourced Connected Vehicle Information for Intelligent Package Delivery","authors":"Fangxin Wang, Yifei Zhu, Feng Wang, Jiangchuan Liu","doi":"10.1109/IWQoS.2018.8624152","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624152","url":null,"abstract":"Nowadays online shopping has become explosively popular and the vast numbers of generated packages have brought great challenges to the traditional logistics industry, especially the last mile package delivery. Traditional delivery approaches rely on dedicated couriers for package dispatch, while the labor cost is quite expensive and the quality is hard to guarantee due to the diverse delivery addresses and tight deadlines. On the other hand, modern cities are full of available transportation resources such as private car trips. The mobile crowdsourcing through 4G/5G and vehicle-related communications enables the vehicle resources to be connected as an intelligent transportation system. As such, we believe ridesharing will be a core service for connected vehicles, which we refer to as Ridesharing as a Service (RaaS). In this paper, we focus on the quality of service (QoS) of RaaS in the last mile package delivery. Mining from real-world car trips, we build up a citywide routing graph and conduct a personalized travel cost prediction considering both the travel time of each driver and the fuel consumption of each vehicle. We then design an online algorithm to assign proper package delivery tasks to the submitted car trips, aiming to maximize the utility of the ridesharing service provider. Our extensive real-world trace-driven evaluations further demonstrate the superiority of our RaaS based package delivery.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"36 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":"121431218","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.2018.8624141
Weibin Meng, Y. Liu, Shenglin Zhang, Dan Pei, Hui Dong, Lei Song, Xulong Luo
Anomaly classification, i.e., detecting whether a network device is anomalous and determining its anomaly category if yes, plays a crucial role in troubleshooting. Compared to KPI curves, device logs contain too much more valuable information for anomaly classification. However, the regular expression based anomaly classification techniques cannot tackle the challenges lying in log anomaly classification. We propose LogClass, a data-driven framework to detect and classify anomalies based on device logs. LogClass combines a word representation method and the PU learning model to construct device-agnostic vocabulary with partial labels. We evaluate LogClass on tens of millions of switch logs collected from several real-world datacenters owned by a top global search engine. Our results show that LogClass achieves 99.515% F1 score in anomalous log detection, 95.32% Macro-F1 and 99.74% Micro-F1 in anomalous log classification in a computationally efficient manner.
{"title":"Device-Agnostic Log Anomaly Classification with Partial Labels","authors":"Weibin Meng, Y. Liu, Shenglin Zhang, Dan Pei, Hui Dong, Lei Song, Xulong Luo","doi":"10.1109/IWQoS.2018.8624141","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624141","url":null,"abstract":"Anomaly classification, i.e., detecting whether a network device is anomalous and determining its anomaly category if yes, plays a crucial role in troubleshooting. Compared to KPI curves, device logs contain too much more valuable information for anomaly classification. However, the regular expression based anomaly classification techniques cannot tackle the challenges lying in log anomaly classification. We propose LogClass, a data-driven framework to detect and classify anomalies based on device logs. LogClass combines a word representation method and the PU learning model to construct device-agnostic vocabulary with partial labels. We evaluate LogClass on tens of millions of switch logs collected from several real-world datacenters owned by a top global search engine. Our results show that LogClass achieves 99.515% F1 score in anomalous log detection, 95.32% Macro-F1 and 99.74% Micro-F1 in anomalous log classification in a computationally efficient manner.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"11 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":"127542497","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.2018.8624162
Qinya Li, Fan Wu, Guihai Chen
A high-quality and large-scale image collection is a fundamental demand in the 3D reconstruction. Crowdsourcing can help us collect lots of diversified images. However, it is not easy to attract people to do this task due to their self-interest. Moreover, the collected images are quality-varying. Those low-quality images may disturb the performance of reconstruction. To avoid low-quality images and lead participants to collect high-quality data, we take images quality into account when allocating rewards. The rewards of participants should be proportionable with their contribution. In this paper, we propose a pricing mechanism, called ImgPricing, to determine the reward of participants in 3D reconstruction system. We model the process of image collection as a cooperative game, and regard each participant's contribution and corresponding image quality as critical factors when allocating rewards. ImgPricing differs from traditional pricing schemes, such as Shapley value, as it introduces the image sequence as an indispensable factor. Finally, we implement our design on the Android platform and evaluate its performance. We use some metrics, such as computational efficiency, fairness and anti-interference, to evaluate ImgPricing and compare with other traditional schemes. Our analyses show ImgPricing is superior to others in terms of computational efficiency and fairness.
{"title":"ImgPricing: Everyone Can Earn Proper Rewards by Simply Taking Photos","authors":"Qinya Li, Fan Wu, Guihai Chen","doi":"10.1109/IWQoS.2018.8624162","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624162","url":null,"abstract":"A high-quality and large-scale image collection is a fundamental demand in the 3D reconstruction. Crowdsourcing can help us collect lots of diversified images. However, it is not easy to attract people to do this task due to their self-interest. Moreover, the collected images are quality-varying. Those low-quality images may disturb the performance of reconstruction. To avoid low-quality images and lead participants to collect high-quality data, we take images quality into account when allocating rewards. The rewards of participants should be proportionable with their contribution. In this paper, we propose a pricing mechanism, called ImgPricing, to determine the reward of participants in 3D reconstruction system. We model the process of image collection as a cooperative game, and regard each participant's contribution and corresponding image quality as critical factors when allocating rewards. ImgPricing differs from traditional pricing schemes, such as Shapley value, as it introduces the image sequence as an indispensable factor. Finally, we implement our design on the Android platform and evaluate its performance. We use some metrics, such as computational efficiency, fairness and anti-interference, to evaluate ImgPricing and compare with other traditional schemes. Our analyses show ImgPricing is superior to others in terms of computational efficiency and fairness.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"70 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":"121876254","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.2018.8624135
Xue Leng, Kaiyu Hou, Yan Chen, Kai Bu, Libin Song
SDN-based cloud has the merit of allowing more flexibility in network management, however, the security of network accessing and the correctness of network configuration in SDN-based cloud have not been effectively addressed yet. In this paper, SDNKeeper, a generic and fine-grained policy enforcement system in SDN-based cloud is proposed, which can defend against unauthorized attacks and avoid network resource misconfiguration. With the usage of SDNKeeper, numerous flexible network management policies can be created by administrators, which give administrators the discretionary room on controlling the network resources. To be specific, SDNKeeper can reject any unauthorized network access request at Northbound Interface (NBI), which located between application plane and control plane. Moreover, compared with other traditional policy-based access control systems, SDNKeeper is totally application-transparent and lightweight, which is easy to implement, deploy and runtime configure. Based on the prototype implementation and evaluation, we conclude that SDNKeeper can perform access control accurately with negligible computation overhead whilst the throughput degradation is still within the acceptable range.
{"title":"SDNKeeper: Lightweight Resource Protection and Management System for SDN-Based Cloud","authors":"Xue Leng, Kaiyu Hou, Yan Chen, Kai Bu, Libin Song","doi":"10.1109/IWQoS.2018.8624135","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624135","url":null,"abstract":"SDN-based cloud has the merit of allowing more flexibility in network management, however, the security of network accessing and the correctness of network configuration in SDN-based cloud have not been effectively addressed yet. In this paper, SDNKeeper, a generic and fine-grained policy enforcement system in SDN-based cloud is proposed, which can defend against unauthorized attacks and avoid network resource misconfiguration. With the usage of SDNKeeper, numerous flexible network management policies can be created by administrators, which give administrators the discretionary room on controlling the network resources. To be specific, SDNKeeper can reject any unauthorized network access request at Northbound Interface (NBI), which located between application plane and control plane. Moreover, compared with other traditional policy-based access control systems, SDNKeeper is totally application-transparent and lightweight, which is easy to implement, deploy and runtime configure. Based on the prototype implementation and evaluation, we conclude that SDNKeeper can perform access control accurately with negligible computation overhead whilst the throughput degradation is still within the acceptable range.","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":"129921135","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.2018.8624176
Haitian Pang, Jiangchuan Liu, Xiaoyi Fan, Lifeng Sun
The emerging 5G mobile networking promises ultrahigh network bandwidth and ultra-low communication latency (<1ms), benefiting a wide range of applications, including live video streaming, online gaming, virtual and augmented reality, and Vehicle-to-X, to name but a few. The backbone Internet, however, does not keep up, particularly in latency (>100ms), due to its store-and-forward design and the physical barrier from signal propagation speed, not to mention congestion that frequently happens. Caching is known to be effective to bridge the speed gap, which has become a critical component in the 5G deployment as well. Besides storage, 5G base stations (BSs) will also be powered with strong computing modules, offering mobile edge computing (MEC) capability. This paper explores the potentials of edge computing towards improving the cache performance, and we envision a learning-based framework that facilitates smart caching beyond simple frequency- and time-based replace strategies and cooperation among base stations. Within this framework, we develop DeepCache, a deep-learning-based solution to understand the request patterns in individual base stations and accordingly make intelligent cache decisions. Using mobile video, one of the most popular applications with high traffic demand, as a case, we further develop a cooperation strategy for nearby base stations to collectively serve user requests. Experimental results on real-world dataset show that using the collaborative DeepCache algorithm, the overall transmission delay is reduced by 14%∼22%, with a backhaul data traffic saving of 15%∼23%.
{"title":"Toward Smart and Cooperative Edge Caching for 5G Networks: A Deep Learning Based Approach","authors":"Haitian Pang, Jiangchuan Liu, Xiaoyi Fan, Lifeng Sun","doi":"10.1109/IWQoS.2018.8624176","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624176","url":null,"abstract":"The emerging 5G mobile networking promises ultrahigh network bandwidth and ultra-low communication latency (<1ms), benefiting a wide range of applications, including live video streaming, online gaming, virtual and augmented reality, and Vehicle-to-X, to name but a few. The backbone Internet, however, does not keep up, particularly in latency (>100ms), due to its store-and-forward design and the physical barrier from signal propagation speed, not to mention congestion that frequently happens. Caching is known to be effective to bridge the speed gap, which has become a critical component in the 5G deployment as well. Besides storage, 5G base stations (BSs) will also be powered with strong computing modules, offering mobile edge computing (MEC) capability. This paper explores the potentials of edge computing towards improving the cache performance, and we envision a learning-based framework that facilitates smart caching beyond simple frequency- and time-based replace strategies and cooperation among base stations. Within this framework, we develop DeepCache, a deep-learning-based solution to understand the request patterns in individual base stations and accordingly make intelligent cache decisions. Using mobile video, one of the most popular applications with high traffic demand, as a case, we further develop a cooperation strategy for nearby base stations to collectively serve user requests. Experimental results on real-world dataset show that using the collaborative DeepCache algorithm, the overall transmission delay is reduced by 14%∼22%, with a backhaul data traffic saving of 15%∼23%.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"95 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":"126961167","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：