Yan Wang, Yingying Chen, Fan Ye, J. Yang, Hongbo Liu
Many smartphone apps routinely gather various private user data and send them to advertisers. Despite recent study on protection mechanisms and analysis on apps' behavior, the understanding about the consequences of such privacy losses remains limited. In this paper we investigate how much an advertiser can infer about users' social and community relationships by combining data from multiple applications and across many users. After one month's user study involving about 200 most popular Android apps, we find that an advertiser can infer 90% of the social relationships. We further propose a privacy leakage inference framework and use real mobility traces and Foursquare data to quantify the consequences of privacy leakage. We find that achieving 90% inference accuracy of the social and community relationships requires merely 3 weeks' user data. The discoveries underscore the importance of early adoption of privacy protection mechanisms.
{"title":"Towards Understanding the Advertiser's Perspective of Smartphone User Privacy","authors":"Yan Wang, Yingying Chen, Fan Ye, J. Yang, Hongbo Liu","doi":"10.1109/ICDCS.2015.37","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.37","url":null,"abstract":"Many smartphone apps routinely gather various private user data and send them to advertisers. Despite recent study on protection mechanisms and analysis on apps' behavior, the understanding about the consequences of such privacy losses remains limited. In this paper we investigate how much an advertiser can infer about users' social and community relationships by combining data from multiple applications and across many users. After one month's user study involving about 200 most popular Android apps, we find that an advertiser can infer 90% of the social relationships. We further propose a privacy leakage inference framework and use real mobility traces and Foursquare data to quantify the consequences of privacy leakage. We find that achieving 90% inference accuracy of the social and community relationships requires merely 3 weeks' user data. The discoveries underscore the importance of early adoption of privacy protection mechanisms.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126262095","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}
S. Nikoletseas, Theofanis P. Raptis, C. Raptopoulos
Rapid technological advances in the domain of Wireless Energy Transfer (WET) pave the way for novel methods for energy management in Wireless Distributed Systems and recent research efforts have already started considering network models that take into account these new technologies. In this paper, we follow a new approach in studying the problem of efficiently charging a set of rechargeable nodes using a set of wireless energy chargers, under safety constraints on the electromagnetic radiation incurred. In particular, we define a new charging model that greatly differs from existing models in that it takes into account real technology restrictions of the chargers and nodes of the system, mainly regarding energy limitations. Our model also introduces non-linear constraints (in the time domain), that radically change the nature of the computational problems we consider. In this charging model, we present and study the Low Radiation Efficient Charging Problem (LREC), in which we wish to optimize the amount of "useful" energy transferred from chargers to nodes (under constraints on the maximum level of imposed radiation). We present several fundamental properties of this problem and provide indications of its hardness. Finally, we propose an iterative local improvement heuristic for LREC, which runs in polynomial time and we evaluate its performance via simulation. Our algorithm decouples the computation of the objective function from the computation of the maximum radiation and also does not depend on the exact formula used for the computation of the electromagnetic radiation in each point of the network, achieving good trade-offs between charging efficiency and radiation control, it also exhibits good energy balance properties. We provide extensive simulation results supporting our claims and theoretical results.
{"title":"Low Radiation Efficient Wireless Energy Transfer in Wireless Distributed Systems","authors":"S. Nikoletseas, Theofanis P. Raptis, C. Raptopoulos","doi":"10.1109/ICDCS.2015.28","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.28","url":null,"abstract":"Rapid technological advances in the domain of Wireless Energy Transfer (WET) pave the way for novel methods for energy management in Wireless Distributed Systems and recent research efforts have already started considering network models that take into account these new technologies. In this paper, we follow a new approach in studying the problem of efficiently charging a set of rechargeable nodes using a set of wireless energy chargers, under safety constraints on the electromagnetic radiation incurred. In particular, we define a new charging model that greatly differs from existing models in that it takes into account real technology restrictions of the chargers and nodes of the system, mainly regarding energy limitations. Our model also introduces non-linear constraints (in the time domain), that radically change the nature of the computational problems we consider. In this charging model, we present and study the Low Radiation Efficient Charging Problem (LREC), in which we wish to optimize the amount of \"useful\" energy transferred from chargers to nodes (under constraints on the maximum level of imposed radiation). We present several fundamental properties of this problem and provide indications of its hardness. Finally, we propose an iterative local improvement heuristic for LREC, which runs in polynomial time and we evaluate its performance via simulation. Our algorithm decouples the computation of the objective function from the computation of the maximum radiation and also does not depend on the exact formula used for the computation of the electromagnetic radiation in each point of the network, achieving good trade-offs between charging efficiency and radiation control, it also exhibits good energy balance properties. We provide extensive simulation results supporting our claims and theoretical results.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131281892","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}
The effect of coding on content retrieval latency in data center storage system is drawing more and more significant attention these days, and customizing elastic service latency for the tenants is undoubtedly appealing to cloud storage, but it also comes with great technical challenges: due to the lack of analytic latency models for erasure-coded storage, most of the literature is limited to the analysis of average service latency, e.g., [1], [2], having assumptions like homogeneous files, exponential service time distribution [3], fixed erasure codes [4], which is unsuitable for a multi-tenant cloud environment where each tenant has a different latency requirement for accessing files in an erasure-coded, online cloud storage. Optimizing differentiated service delay in an erasure-coded storage system is an open problem. This work considers an erasure-coded storage with multiple tenants and differentiated delay demands, studies two types of service policies, non-preemptive priority queue and weighted queue, quantifying service latency of these policies, propose a novel optimization framework that provides differentiated service latency to meet heterogeneous application requirements in cloud storage.
{"title":"Multi-tenant Latency Optimization in Erasure-Coded Storage with Differentiated Services","authors":"Yu Xiang, Tian Lan, V. Aggarwal, Y. Chen","doi":"10.1109/ICDCS.2015.111","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.111","url":null,"abstract":"The effect of coding on content retrieval latency in data center storage system is drawing more and more significant attention these days, and customizing elastic service latency for the tenants is undoubtedly appealing to cloud storage, but it also comes with great technical challenges: due to the lack of analytic latency models for erasure-coded storage, most of the literature is limited to the analysis of average service latency, e.g., [1], [2], having assumptions like homogeneous files, exponential service time distribution [3], fixed erasure codes [4], which is unsuitable for a multi-tenant cloud environment where each tenant has a different latency requirement for accessing files in an erasure-coded, online cloud storage. Optimizing differentiated service delay in an erasure-coded storage system is an open problem. This work considers an erasure-coded storage with multiple tenants and differentiated delay demands, studies two types of service policies, non-preemptive priority queue and weighted queue, quantifying service latency of these policies, propose a novel optimization framework that provides differentiated service latency to meet heterogeneous application requirements in cloud storage.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122055720","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}
Machine learning has played an increasing important role in big data systems due to its capability of efficiently discovering valuable knowledge and hidden information. Often times big data such as healthcare systems or financial systems may involve with multiple organizations who may have different privacy policy, and may not explicitly share their data publicly while joint data processing may be a must. Thus, how to share big data among distributed data processing entities while mitigating privacy concerns becomes a challenging problem. Traditional methods rely on cryptographic tools and/or randomization to preserve privacy. Unfortunately, this alone may be inadequate for the emerging big data systems because they are mainly designed for traditional small-scale data sets. In this paper, we propose a novel framework to achieve privacy-preserving machine learning where the training data are distributed and each shared data portion is of large volume. Specifically, we utilize the data locality property of Apache Hadoop architecture and only a limited number of cryptographic operations at the Reduce() procedures to achieve privacy-preservation. We show that the proposed scheme is secure in the semi-honest model and use extensive simulations to demonstrate its scalability and correctness.
{"title":"Privacy-Preserving Machine Learning Algorithms for Big Data Systems","authors":"Kaihe Xu, Hao Yue, Linke Guo, Yuanxiong Guo, Yuguang Fang","doi":"10.1109/ICDCS.2015.40","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.40","url":null,"abstract":"Machine learning has played an increasing important role in big data systems due to its capability of efficiently discovering valuable knowledge and hidden information. Often times big data such as healthcare systems or financial systems may involve with multiple organizations who may have different privacy policy, and may not explicitly share their data publicly while joint data processing may be a must. Thus, how to share big data among distributed data processing entities while mitigating privacy concerns becomes a challenging problem. Traditional methods rely on cryptographic tools and/or randomization to preserve privacy. Unfortunately, this alone may be inadequate for the emerging big data systems because they are mainly designed for traditional small-scale data sets. In this paper, we propose a novel framework to achieve privacy-preserving machine learning where the training data are distributed and each shared data portion is of large volume. Specifically, we utilize the data locality property of Apache Hadoop architecture and only a limited number of cryptographic operations at the Reduce() procedures to achieve privacy-preservation. We show that the proposed scheme is secure in the semi-honest model and use extensive simulations to demonstrate its scalability and correctness.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114952381","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}
Compaction plays a crucial role in NoSQL systems to ensure a high overall read throughput. In this work, we formally define compaction as an optimization problem that attempts to minimize disk I/O. We prove this problem to be NP-Hard. We then propose a set of algorithms and mathematically analyze upper bounds on worst-case cost. We evaluate the proposed algorithms on real-life workloads. Our results show that our algorithms incur low I/O costs and that a compaction approach using a balanced tree is most preferable.
{"title":"Fast Compaction Algorithms for NoSQL Databases","authors":"Mainak Ghosh, Indranil Gupta, Shalmoli Gupta, Nirman Kumar","doi":"10.1109/ICDCS.2015.53","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.53","url":null,"abstract":"Compaction plays a crucial role in NoSQL systems to ensure a high overall read throughput. In this work, we formally define compaction as an optimization problem that attempts to minimize disk I/O. We prove this problem to be NP-Hard. We then propose a set of algorithms and mathematically analyze upper bounds on worst-case cost. We evaluate the proposed algorithms on real-life workloads. Our results show that our algorithms incur low I/O costs and that a compaction approach using a balanced tree is most preferable.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128223176","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}
Dazhao Cheng, P. Lama, Changjun Jiang, Xiaobo Zhou
The cost of powering servers, storage platforms and related cooling systems has become a major component of the operational costs in big data deployments. Hence, the design of energy-efficient Hadoop clusters has attracted significant research attentions in recent years. However, existing studies do not consider the impact of the complex interplay between workload and hardware heterogeneity on energy efficiency. In this paper, we find that heterogeneity-oblivious task assignment approaches are detrimental to both performance and energy efficiency of Hadoop clusters. Importantly, we make a counterintuitive observation that even heterogeneity-aware techniques that focus on reducing job completion time do not necessarily guarantee energy efficiency. We propose a heterogeneity-aware task assignment approach, E-Ant, that aims to minimize the overall energy consumption in a heterogeneous Hadoop cluster without sacrificing job performance. It adaptively schedules heterogeneous workloads on energy-efficient machines, without a priori knowledge of the workload properties. Furthermore, it provides the flexibility to trade off energy efficiency and job fairness in a Hadoop cluster. E-Ant employs an ant colony optimization approach that generates task assignment solutions based on the feedback of each task's energy consumption reported by Hadoop Task Trackers in an agile way. Experimental results on a heterogeneous cluster with varying hardware capabilities show that E-Ant improves the overall energy savings for a synthetic workload from Microsoft by 17% and 12% compared to Fair Scheduler and Tarazu, respectively.
{"title":"Towards Energy Efficiency in Heterogeneous Hadoop Clusters by Adaptive Task Assignment","authors":"Dazhao Cheng, P. Lama, Changjun Jiang, Xiaobo Zhou","doi":"10.1109/ICDCS.2015.44","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.44","url":null,"abstract":"The cost of powering servers, storage platforms and related cooling systems has become a major component of the operational costs in big data deployments. Hence, the design of energy-efficient Hadoop clusters has attracted significant research attentions in recent years. However, existing studies do not consider the impact of the complex interplay between workload and hardware heterogeneity on energy efficiency. In this paper, we find that heterogeneity-oblivious task assignment approaches are detrimental to both performance and energy efficiency of Hadoop clusters. Importantly, we make a counterintuitive observation that even heterogeneity-aware techniques that focus on reducing job completion time do not necessarily guarantee energy efficiency. We propose a heterogeneity-aware task assignment approach, E-Ant, that aims to minimize the overall energy consumption in a heterogeneous Hadoop cluster without sacrificing job performance. It adaptively schedules heterogeneous workloads on energy-efficient machines, without a priori knowledge of the workload properties. Furthermore, it provides the flexibility to trade off energy efficiency and job fairness in a Hadoop cluster. E-Ant employs an ant colony optimization approach that generates task assignment solutions based on the feedback of each task's energy consumption reported by Hadoop Task Trackers in an agile way. Experimental results on a heterogeneous cluster with varying hardware capabilities show that E-Ant improves the overall energy savings for a synthetic workload from Microsoft by 17% and 12% compared to Fair Scheduler and Tarazu, respectively.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114676429","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}
Julien Gascon-Samson, F. Garcia, Bettina Kemme, J. Kienzle
This paper presents Dynamoth, a dynamic, scalable, channel-based pub/sub middleware targeted at large scale, distributed and latency constrained systems. Our approach provides a software layer that balances the load generated by a high number of publishers, subscribers and messages across multiple, standard pub/sub servers that can be deployed in the Cloud. In order to optimize Cloud infrastructure usage, pub/sub servers can be added or removed as needed. Balancing takes into account the live characteristics of each channel and is done in an hierarchical manner across channels (macro) as well as within individual channels (micro) to maintain acceptable performance and low latencies despite highly varying conditions. Load monitoring is performed in an unintrusive way, and rebalancing employs a lazy approach in order to minimize its temporal impact on performance while ensuring successful and timely delivery of all messages. Extensive real-world experiments that illustrate the practicality of the approach within a massively multiplayer game setting are presented. Results indicate that with a given number of servers, Dynamoth was able to handle 60% more simultaneous clients than the consistent hashing approach, and that it was properly able to deal with highly varying conditions in the context of large workloads.
{"title":"Dynamoth: A Scalable Pub/Sub Middleware for Latency-Constrained Applications in the Cloud","authors":"Julien Gascon-Samson, F. Garcia, Bettina Kemme, J. Kienzle","doi":"10.1109/ICDCS.2015.56","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.56","url":null,"abstract":"This paper presents Dynamoth, a dynamic, scalable, channel-based pub/sub middleware targeted at large scale, distributed and latency constrained systems. Our approach provides a software layer that balances the load generated by a high number of publishers, subscribers and messages across multiple, standard pub/sub servers that can be deployed in the Cloud. In order to optimize Cloud infrastructure usage, pub/sub servers can be added or removed as needed. Balancing takes into account the live characteristics of each channel and is done in an hierarchical manner across channels (macro) as well as within individual channels (micro) to maintain acceptable performance and low latencies despite highly varying conditions. Load monitoring is performed in an unintrusive way, and rebalancing employs a lazy approach in order to minimize its temporal impact on performance while ensuring successful and timely delivery of all messages. Extensive real-world experiments that illustrate the practicality of the approach within a massively multiplayer game setting are presented. Results indicate that with a given number of servers, Dynamoth was able to handle 60% more simultaneous clients than the consistent hashing approach, and that it was properly able to deal with highly varying conditions in the context of large workloads.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116225743","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}
With the rapid development of mobile networking and end-terminals, anytime and anywhere data access becomes readily available nowadays. Given the crowd sourced content capturing and sharing, the preferred length becomes shorter and shorter, even for such multimedia content as video. A representative is Twitter's Vine service, which, mainly targeting mobile users, enables them to create ultra-short video clips, and instantly post and share them with their followers. In this paper, we present an initial study on this new generation of instant video clip sharing service enabled by mobile platforms and explore the potentials for its further enhancement. Taking Vine as a case study, we closely investigate its unique user behaviors, revealing how such Vine-enabled anytime anywhere data access patterns differentiate mobile instant video clip sharing from its traditional counterparts. We then formulate a generic scheduling problem to maximize the user watching experience as well as the efficiency on the monetary and energy costs. To better solve it, we divide the problem into two sub problems, specifically, the pre-fetching scheduling problem and the watch-time download scheduling problem, and conquer them separately. We further demonstrate the preliminary evaluation result to show the superiority of our solution. To the best of our knowledge, this is the first work on modeling and optimizing the instant video clip sharing on mobile devices.
{"title":"Improve Quality of Experience for Mobile Instant Video Clip Sharing","authors":"Lei Zhang, Feng Wang, Jiangchuan Liu","doi":"10.1109/ICDCS.2015.106","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.106","url":null,"abstract":"With the rapid development of mobile networking and end-terminals, anytime and anywhere data access becomes readily available nowadays. Given the crowd sourced content capturing and sharing, the preferred length becomes shorter and shorter, even for such multimedia content as video. A representative is Twitter's Vine service, which, mainly targeting mobile users, enables them to create ultra-short video clips, and instantly post and share them with their followers. In this paper, we present an initial study on this new generation of instant video clip sharing service enabled by mobile platforms and explore the potentials for its further enhancement. Taking Vine as a case study, we closely investigate its unique user behaviors, revealing how such Vine-enabled anytime anywhere data access patterns differentiate mobile instant video clip sharing from its traditional counterparts. We then formulate a generic scheduling problem to maximize the user watching experience as well as the efficiency on the monetary and energy costs. To better solve it, we divide the problem into two sub problems, specifically, the pre-fetching scheduling problem and the watch-time download scheduling problem, and conquer them separately. We further demonstrate the preliminary evaluation result to show the superiority of our solution. To the best of our knowledge, this is the first work on modeling and optimizing the instant video clip sharing on mobile devices.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116322806","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}
Yang Wang, Liusheng Huang, Hao Wei, Wei Zheng, Tianbo Gu, Hengchang Liu
Despite the clear benefits of electric vehicles (EVs) in terms of reducing greenhouse gas emissions and traditional energy consumptions, the popularization of EVs remains a challenge in the short run. When considering electric taxis, urban planners must face the additional issue of providing battery swapping services. While previous studies focused on planning battery swapping stations for private EVs, we investigate ways of supporting the upgrade of an entire urban taxi system, with demands differing both in scale and nature. With this insight, we analyze the historical sensing data of taxi routes, and evaluate the battery swapping demand profile, as well as the driving time between positions in the road network. Based on these inputs, we propose a method to calculate an optimized battery swapping station scheme. Our strategies are then evaluated via a real world 366-day, 3,976-taxi dataset. The results show that compared to uniform deployment, our planning scheme reduces the average time-cost by 67.2%.
{"title":"Planning Battery Swapping Stations for Urban Electrical Taxis","authors":"Yang Wang, Liusheng Huang, Hao Wei, Wei Zheng, Tianbo Gu, Hengchang Liu","doi":"10.1109/ICDCS.2015.87","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.87","url":null,"abstract":"Despite the clear benefits of electric vehicles (EVs) in terms of reducing greenhouse gas emissions and traditional energy consumptions, the popularization of EVs remains a challenge in the short run. When considering electric taxis, urban planners must face the additional issue of providing battery swapping services. While previous studies focused on planning battery swapping stations for private EVs, we investigate ways of supporting the upgrade of an entire urban taxi system, with demands differing both in scale and nature. With this insight, we analyze the historical sensing data of taxi routes, and evaluate the battery swapping demand profile, as well as the driving time between positions in the road network. Based on these inputs, we propose a method to calculate an optimized battery swapping station scheme. Our strategies are then evaluated via a real world 366-day, 3,976-taxi dataset. The results show that compared to uniform deployment, our planning scheme reduces the average time-cost by 67.2%.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124136138","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}
Reachability, one of the most fundamental queries over uncertain graphs, which asks the probability that two given query vertices are reachable over an uncertain graph. Although this problem has been widely studied, the existing works are all processed in a single server. However, as graph data becomes larger, it usually cannot be stored in a single server. Moreover, processing probabilistic reachability queries is #P-complete, so the calculation is very expensive even on small graphs. Thus, in this paper, our purpose is to develop efficient distributed strategies to firstly pick out all the maximal subgraphs whose reachability probabilities can be calculated in polynomial time efficiently. After this step, only a small graph remains, and we provide an approximate method. Extensive experimental studies show that our distributed algorithms are efficient and have a low communication cost.
{"title":"The Reachability Query over Distributed Uncertain Graphs","authors":"Yurong Cheng, Ye Yuan, Lei Chen, Guoren Wang","doi":"10.1109/ICDCS.2015.109","DOIUrl":"https://doi.org/10.1109/ICDCS.2015.109","url":null,"abstract":"Reachability, one of the most fundamental queries over uncertain graphs, which asks the probability that two given query vertices are reachable over an uncertain graph. Although this problem has been widely studied, the existing works are all processed in a single server. However, as graph data becomes larger, it usually cannot be stored in a single server. Moreover, processing probabilistic reachability queries is #P-complete, so the calculation is very expensive even on small graphs. Thus, in this paper, our purpose is to develop efficient distributed strategies to firstly pick out all the maximal subgraphs whose reachability probabilities can be calculated in polynomial time efficiently. After this step, only a small graph remains, and we provide an approximate method. Extensive experimental studies show that our distributed algorithms are efficient and have a low communication cost.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122435378","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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