Pub Date : 2016-07-28DOI: 10.1109/INFOCOM.2016.7524527
Ping-Chun Hsieh, I.-Hong Hou
This paper proposes online scheduling policies to optimize quality of experience (QoE) for video-on-demand applications in wireless networks. We consider wireless systems where an access point (AP) transmits video content to clients over fading channels. The QoE of each flow is measured by its duration of video playback interruption. We are specifically interested in systems operating in the heavy-traffic regime. We first consider a special case of ON-OFF channels and establish a scheduling policy that achieves every point in the capacity region under heavy-traffic conditions. This policy is then extended for more general fading channels, and we prove that it remains optimal under some mild conditions. We then formulate a network utility maximization problem based on the QoE of each flow. We demonstrate that our policies achieve the optimal overall utility when their parameters are chosen properly. Finally, we compare our policies against three popular policies. Simulation results validate that the proposed policy indeed outperforms existing policies.
{"title":"Heavy-traffic analysis of QoE optimality for on-demand video streams over fading channels","authors":"Ping-Chun Hsieh, I.-Hong Hou","doi":"10.1109/INFOCOM.2016.7524527","DOIUrl":"https://doi.org/10.1109/INFOCOM.2016.7524527","url":null,"abstract":"This paper proposes online scheduling policies to optimize quality of experience (QoE) for video-on-demand applications in wireless networks. We consider wireless systems where an access point (AP) transmits video content to clients over fading channels. The QoE of each flow is measured by its duration of video playback interruption. We are specifically interested in systems operating in the heavy-traffic regime. We first consider a special case of ON-OFF channels and establish a scheduling policy that achieves every point in the capacity region under heavy-traffic conditions. This policy is then extended for more general fading channels, and we prove that it remains optimal under some mild conditions. We then formulate a network utility maximization problem based on the QoE of each flow. We demonstrate that our policies achieve the optimal overall utility when their parameters are chosen properly. Finally, we compare our policies against three popular policies. Simulation results validate that the proposed policy indeed outperforms existing policies.","PeriodicalId":274591,"journal":{"name":"IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications","volume":"1992 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128611306","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 : 2016-07-27DOI: 10.1109/INFOCOM.2016.7524552
Marco Chiesa, Ilya Nikolaevskiy, Slobodan Mitrovic, Aurojit Panda, A. Gurtov, A. Madry, Michael Schapira, S. Shenker
Fast Reroute (FRR) and other forms of immediate failover have long been used to recover from certain classes of failures without invoking the network control plane. While the set of such techniques is growing, the level of resiliency to failures that this approach can provide is not adequately understood. We embark upon a systematic algorithmic study of the resiliency of immediate failover in a variety of models (with/without packet marking/duplication, etc.). We leverage our findings to devise new schemes for immediate failover and show, both theoretically and experimentally, that these outperform existing approaches.
{"title":"The quest for resilient (static) forwarding tables","authors":"Marco Chiesa, Ilya Nikolaevskiy, Slobodan Mitrovic, Aurojit Panda, A. Gurtov, A. Madry, Michael Schapira, S. Shenker","doi":"10.1109/INFOCOM.2016.7524552","DOIUrl":"https://doi.org/10.1109/INFOCOM.2016.7524552","url":null,"abstract":"Fast Reroute (FRR) and other forms of immediate failover have long been used to recover from certain classes of failures without invoking the network control plane. While the set of such techniques is growing, the level of resiliency to failures that this approach can provide is not adequately understood. We embark upon a systematic algorithmic study of the resiliency of immediate failover in a variety of models (with/without packet marking/duplication, etc.). We leverage our findings to devise new schemes for immediate failover and show, both theoretically and experimentally, that these outperform existing approaches.","PeriodicalId":274591,"journal":{"name":"IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124913404","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 : 2016-07-27DOI: 10.1109/INFOCOM.2016.7524604
Fabio Cecchi, S. Borst, J. V. Leeuwaarden, P. Whiting
With the rapid advance of the Internet of Everything, both the number of devices and the range of applications that rely on wireless connectivity show huge growth. Driven by these pervasive trends, wireless networks grow in size and complexity, supporting immense numbers of nodes and data volumes, with highly diverse traffic profiles and performance requirements. While well-established methods are available for evaluating the throughput of persistent sessions with saturated buffers, these provide no insight in the delay performance of flows with intermittent packet arrivals. The occurrence of empty buffers in the latter scenario results in a complex interaction between activity states and packet queues, which severely complicates the performance analysis. Motivated by these challenges, we develop a mean-field approach to analyze buffer contents and packet delays in wireless networks in a many-sources regime. The mean-field behavior simplifies the analysis of a large-scale network with packet arrivals and buffer dynamics to a low-dimensional fixed-point calculation for a network with saturated buffers. In particular, the analysis yields explicit expressions for the buffer content and packet delay distribution in terms of the fixed-point solution. Extensive simulation experiments demonstrate that these expressions provide highly accurate approximations, even for a fairly moderate number of sources.
{"title":"CSMA networks in a many-sources regime: A mean-field approach","authors":"Fabio Cecchi, S. Borst, J. V. Leeuwaarden, P. Whiting","doi":"10.1109/INFOCOM.2016.7524604","DOIUrl":"https://doi.org/10.1109/INFOCOM.2016.7524604","url":null,"abstract":"With the rapid advance of the Internet of Everything, both the number of devices and the range of applications that rely on wireless connectivity show huge growth. Driven by these pervasive trends, wireless networks grow in size and complexity, supporting immense numbers of nodes and data volumes, with highly diverse traffic profiles and performance requirements. While well-established methods are available for evaluating the throughput of persistent sessions with saturated buffers, these provide no insight in the delay performance of flows with intermittent packet arrivals. The occurrence of empty buffers in the latter scenario results in a complex interaction between activity states and packet queues, which severely complicates the performance analysis. Motivated by these challenges, we develop a mean-field approach to analyze buffer contents and packet delays in wireless networks in a many-sources regime. The mean-field behavior simplifies the analysis of a large-scale network with packet arrivals and buffer dynamics to a low-dimensional fixed-point calculation for a network with saturated buffers. In particular, the analysis yields explicit expressions for the buffer content and packet delay distribution in terms of the fixed-point solution. Extensive simulation experiments demonstrate that these expressions provide highly accurate approximations, even for a fairly moderate number of sources.","PeriodicalId":274591,"journal":{"name":"IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications","volume":"98 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125026079","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 : 2016-04-10DOI: 10.1109/INFOCOM.2016.7524504
Tuo Shi, Siyao Cheng, Zhipeng Cai, Jianzhong Li
A Wireless Sensor Network consists of a number of sensors. The energy of each sensor is limited which limits network lifetime. There are many existing energy efficiency algorithms to prolong network lifetime. Basically, there are two kinds of methods. One is energy-efficiency management, such as duty-cycling using virtual-backbones. The other one is energy provision, such as energy harvest from the environment. In this paper, we introduce a new problem, CDSEH, to combine these two methods together. We also propose a new standard to define the network lifetime of a WSN. We prove that the CDSEH problem is NP-Complete and propose two approximate algorithms accordingly. Extensive simulation results are shown to validate the performance of our algorithms.
{"title":"Adaptive connected dominating set discovering algorithm in energy-harvest sensor networks","authors":"Tuo Shi, Siyao Cheng, Zhipeng Cai, Jianzhong Li","doi":"10.1109/INFOCOM.2016.7524504","DOIUrl":"https://doi.org/10.1109/INFOCOM.2016.7524504","url":null,"abstract":"A Wireless Sensor Network consists of a number of sensors. The energy of each sensor is limited which limits network lifetime. There are many existing energy efficiency algorithms to prolong network lifetime. Basically, there are two kinds of methods. One is energy-efficiency management, such as duty-cycling using virtual-backbones. The other one is energy provision, such as energy harvest from the environment. In this paper, we introduce a new problem, CDSEH, to combine these two methods together. We also propose a new standard to define the network lifetime of a WSN. We prove that the CDSEH problem is NP-Complete and propose two approximate algorithms accordingly. Extensive simulation results are shown to validate the performance of our algorithms.","PeriodicalId":274591,"journal":{"name":"IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115285600","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 : 2016-04-10DOI: 10.1109/INFOCOM.2016.7524471
Ji Li, Zhipeng Cai, Mingyuan Yan, Yingshu Li
Online social networks have gained significant popularity recently. The problem of influence maximization in online social networks has been extensively studied. However, in prior works, influence propagation in the physical world, which is also an indispensable factor, is not considered. The Location-Based Social Networks (LBSNs) are a special kind of online social networks in which people can share location-embedded information. In this paper, we make use of mobile crowdsourced data obtained from location-based social network services to study influence maximization in LBSNs. A novel network model and an influence propagation model taking influence propagation in both online social networks and the physical world into consideration are proposed. An event activation position selection problem is formalized and a corresponding solution is provided. The experimental results indicate that the proposed influence propagation model is meaningful and the activation position selection algorithm has high performance.
{"title":"Using crowdsourced data in location-based social networks to explore influence maximization","authors":"Ji Li, Zhipeng Cai, Mingyuan Yan, Yingshu Li","doi":"10.1109/INFOCOM.2016.7524471","DOIUrl":"https://doi.org/10.1109/INFOCOM.2016.7524471","url":null,"abstract":"Online social networks have gained significant popularity recently. The problem of influence maximization in online social networks has been extensively studied. However, in prior works, influence propagation in the physical world, which is also an indispensable factor, is not considered. The Location-Based Social Networks (LBSNs) are a special kind of online social networks in which people can share location-embedded information. In this paper, we make use of mobile crowdsourced data obtained from location-based social network services to study influence maximization in LBSNs. A novel network model and an influence propagation model taking influence propagation in both online social networks and the physical world into consideration are proposed. An event activation position selection problem is formalized and a corresponding solution is provided. The experimental results indicate that the proposed influence propagation model is meaningful and the activation position selection algorithm has high performance.","PeriodicalId":274591,"journal":{"name":"IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127535301","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 : 2016-04-10DOI: 10.1109/INFOCOM.2016.7524447
Peyman Siyari, M. Krunz, Diep N. Nguyen
In this paper, we expand the scope of PHY-layer security by investigating TX-based friendly jamming (FJ) for the wiretap channel in multi-link settings. For the single-link scenario, creating a TX-based FJ is an effective and practical method in improving the secrecy rate. In a multi-link setting, several information signals must be transmitted simultaneously. Thus, the design must guarantee that the FJ signal of a given transmitter does not interfere with unintended but legitimate receivers. Under the assumption of exact knowledge of the eavesdropping channel, we first propose a distributed price-based approach to improve the secrecy sum-rate of a two-link network with one eavesdropper while satisfying an information-rate constraint for both link. Simulations show that price-based FJ control outperforms greedy FJ, and is close to the performance of a centralized approach. Next, we propose a method based on mixed strategic games that can offer robust solutions to the distributed secrecy sum-rate maximization problem under the assumption of an unknown eavesdropping channel. Lastly, we use simulations to show that in addition to outperforming the greedy approach, our robust optimization also satisfies practical network considerations. In particular, the transmission time for the robust optimization can be determined flexibly to match the channel's coherence time.
{"title":"Price-based friendly jamming in a MISO interference wiretap channel","authors":"Peyman Siyari, M. Krunz, Diep N. Nguyen","doi":"10.1109/INFOCOM.2016.7524447","DOIUrl":"https://doi.org/10.1109/INFOCOM.2016.7524447","url":null,"abstract":"In this paper, we expand the scope of PHY-layer security by investigating TX-based friendly jamming (FJ) for the wiretap channel in multi-link settings. For the single-link scenario, creating a TX-based FJ is an effective and practical method in improving the secrecy rate. In a multi-link setting, several information signals must be transmitted simultaneously. Thus, the design must guarantee that the FJ signal of a given transmitter does not interfere with unintended but legitimate receivers. Under the assumption of exact knowledge of the eavesdropping channel, we first propose a distributed price-based approach to improve the secrecy sum-rate of a two-link network with one eavesdropper while satisfying an information-rate constraint for both link. Simulations show that price-based FJ control outperforms greedy FJ, and is close to the performance of a centralized approach. Next, we propose a method based on mixed strategic games that can offer robust solutions to the distributed secrecy sum-rate maximization problem under the assumption of an unknown eavesdropping channel. Lastly, we use simulations to show that in addition to outperforming the greedy approach, our robust optimization also satisfies practical network considerations. In particular, the transmission time for the robust optimization can be determined flexibly to match the channel's coherence time.","PeriodicalId":274591,"journal":{"name":"IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125152315","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 : 2016-04-10DOI: 10.1109/INFOCOM.2016.7524528
Huaxin Li, Zheyu Xu, Haojin Zhu, Di Ma, Shuai Li, Kai Xing
Although privacy leaking through content analysis of Wi-Fi traffic has received an increased attention, privacy inference through meta-data (e.g. IP, Host) analysis of Wi-Fi traffic represents a potentially more serious threat to user privacy. Firstly, it represents a more efficient and scalable approach to infer users' sensitive information without checking the content of Wi-Fi traffic. Secondly, meta-data based demographics inference can work on both unencrypted and encrypted traffic (e.g., HTTPS traffic). In this study, we present a novel approach to infer user demographic information by exploiting the meta-data of Wi-Fi traffic. We develop a proof-of-concept prototype, Demographic Information Predictor (DIP) system, and evaluate its performance on a real-world dataset, which includes the Wi-Fi access of 28,158 users in 5 months. DIP extracts four kinds of features from real-world Wi-Fi traffic and proposes a novel machine learning based inference technique to predict user demographics. Our analytical results show that, for unencrypted traffic, DIP can predict gender and education level of users with an accuracy of 78% and 74% respectively. It is surprising to show that, even for HTTPS traffic, user demographics can still be predicted at a precision of 67% and 72% respectively, which well demonstrates the practicality of the proposed privacy inference scheme.
{"title":"Demographics inference through Wi-Fi network traffic analysis","authors":"Huaxin Li, Zheyu Xu, Haojin Zhu, Di Ma, Shuai Li, Kai Xing","doi":"10.1109/INFOCOM.2016.7524528","DOIUrl":"https://doi.org/10.1109/INFOCOM.2016.7524528","url":null,"abstract":"Although privacy leaking through content analysis of Wi-Fi traffic has received an increased attention, privacy inference through meta-data (e.g. IP, Host) analysis of Wi-Fi traffic represents a potentially more serious threat to user privacy. Firstly, it represents a more efficient and scalable approach to infer users' sensitive information without checking the content of Wi-Fi traffic. Secondly, meta-data based demographics inference can work on both unencrypted and encrypted traffic (e.g., HTTPS traffic). In this study, we present a novel approach to infer user demographic information by exploiting the meta-data of Wi-Fi traffic. We develop a proof-of-concept prototype, Demographic Information Predictor (DIP) system, and evaluate its performance on a real-world dataset, which includes the Wi-Fi access of 28,158 users in 5 months. DIP extracts four kinds of features from real-world Wi-Fi traffic and proposes a novel machine learning based inference technique to predict user demographics. Our analytical results show that, for unencrypted traffic, DIP can predict gender and education level of users with an accuracy of 78% and 74% respectively. It is surprising to show that, even for HTTPS traffic, user demographics can still be predicted at a precision of 67% and 72% respectively, which well demonstrates the practicality of the proposed privacy inference scheme.","PeriodicalId":274591,"journal":{"name":"IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125938481","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 : 2016-04-10DOI: 10.1109/INFOCOM.2016.7524487
Michael Lin, N. Bartolini, T. L. Porta
Densely-deployed femtocell networks are used to enhance wireless coverage in public spaces like office buildings, subways, and academic buildings. These networks can increase throughput for users, but edge users can suffer from co-channel interference, leading to service outages. This paper introduces a distributed algorithm for network configuration, called Radius Reduction and Scheduling (RRS), to improve the performance and fairness of the network. RRS determines cell sizes using a Voronoi-Laguerre framework, then schedules users using a scheduling algorithm that includes vacancy requests to increase fairness in dense femtocell networks. We prove that our algorithm always terminate in a finite time, producing a configuration that guarantees user or area coverage. Simulation results show a decrease in outage probability of up to 50%, as well as an increase in Jain's fairness index of almost 200%.
{"title":"Power adjustment and scheduling in OFDMA femtocell networks","authors":"Michael Lin, N. Bartolini, T. L. Porta","doi":"10.1109/INFOCOM.2016.7524487","DOIUrl":"https://doi.org/10.1109/INFOCOM.2016.7524487","url":null,"abstract":"Densely-deployed femtocell networks are used to enhance wireless coverage in public spaces like office buildings, subways, and academic buildings. These networks can increase throughput for users, but edge users can suffer from co-channel interference, leading to service outages. This paper introduces a distributed algorithm for network configuration, called Radius Reduction and Scheduling (RRS), to improve the performance and fairness of the network. RRS determines cell sizes using a Voronoi-Laguerre framework, then schedules users using a scheduling algorithm that includes vacancy requests to increase fairness in dense femtocell networks. We prove that our algorithm always terminate in a finite time, producing a configuration that guarantees user or area coverage. Simulation results show a decrease in outage probability of up to 50%, as well as an increase in Jain's fairness index of almost 200%.","PeriodicalId":274591,"journal":{"name":"IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications","volume":"341 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123417631","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 : 2016-04-10DOI: 10.1109/INFOCOM.2016.7524508
Cheng Jin, Abhinav Srivastava, Zhi-Li Zhang
To ensure security, cloud service providers employ security groups as a key tool for cloud tenants to protect their virtual machines (VMs) from attacks. However, security groups can be complex and often hard to configure, which may result in security vulnerabilities that impact the entire cloud platform. The goal of this paper is to investigate and understand how cloud tenants configure security groups and to assist them in designing better security groups. We first conduct a measurement-based analysis of security group configuration and usage by tenants in an IaaS cloud. We then propose and develop a tool called Socrates, which enables tenants to visualize and hence understand the static and dynamic access relations among VMs. Socrates also helps diagnose potential misconfigurations and provides suggestions to refine security group configurations based on observed traffic traversing tenants' VMs. Applying Socrates to all tenants hosted on the IaaS cloud, we analyze the common usage (“good” as well as “bad” practices) of cloud security groups and report the key lessons learned in our study. To the best of our knowledge, our work is the first to analyze cloud security group usage based on real-world datasets, and to develop a system to help cloud tenants understand, diagnose and better refine their security group configurations.
{"title":"Understanding security group usage in a public IaaS cloud","authors":"Cheng Jin, Abhinav Srivastava, Zhi-Li Zhang","doi":"10.1109/INFOCOM.2016.7524508","DOIUrl":"https://doi.org/10.1109/INFOCOM.2016.7524508","url":null,"abstract":"To ensure security, cloud service providers employ security groups as a key tool for cloud tenants to protect their virtual machines (VMs) from attacks. However, security groups can be complex and often hard to configure, which may result in security vulnerabilities that impact the entire cloud platform. The goal of this paper is to investigate and understand how cloud tenants configure security groups and to assist them in designing better security groups. We first conduct a measurement-based analysis of security group configuration and usage by tenants in an IaaS cloud. We then propose and develop a tool called Socrates, which enables tenants to visualize and hence understand the static and dynamic access relations among VMs. Socrates also helps diagnose potential misconfigurations and provides suggestions to refine security group configurations based on observed traffic traversing tenants' VMs. Applying Socrates to all tenants hosted on the IaaS cloud, we analyze the common usage (“good” as well as “bad” practices) of cloud security groups and report the key lessons learned in our study. To the best of our knowledge, our work is the first to analyze cloud security group usage based on real-world datasets, and to develop a system to help cloud tenants understand, diagnose and better refine their security group configurations.","PeriodicalId":274591,"journal":{"name":"IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123777826","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 : 2016-04-10DOI: 10.1109/INFOCOM.2016.7524616
P. Chuprikov, S. Nikolenko, Kirill Kogan
Cloud computing allows on demand elastic service scaling. The capability of a service to predict resource requirements for the next operational period defines how well it will exploit the elasticity of cloud computing in order to reduce operational costs. In this work, we consider a capacity planning process for service scale-out as an online pricing model. In particular, we study the impact of buffering service requests on revenues in various settings with allocation and maintenance costs. In addition, we analyze the incurred latency implied by buffering service requests. We believe that our insights will allow to significantly simplify predictions and mitigate the unknowns of future demands on resources.
{"title":"On demand elastic capacity planning for service auto-scaling","authors":"P. Chuprikov, S. Nikolenko, Kirill Kogan","doi":"10.1109/INFOCOM.2016.7524616","DOIUrl":"https://doi.org/10.1109/INFOCOM.2016.7524616","url":null,"abstract":"Cloud computing allows on demand elastic service scaling. The capability of a service to predict resource requirements for the next operational period defines how well it will exploit the elasticity of cloud computing in order to reduce operational costs. In this work, we consider a capacity planning process for service scale-out as an online pricing model. In particular, we study the impact of buffering service requests on revenues in various settings with allocation and maintenance costs. In addition, we analyze the incurred latency implied by buffering service requests. We believe that our insights will allow to significantly simplify predictions and mitigate the unknowns of future demands on resources.","PeriodicalId":274591,"journal":{"name":"IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115327003","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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