Qing Han, Phu Nguyen, R. Eguchi, K. Hsu, N. Venkatasubramanian
We present a cyber-physical-human distributed computing framework, AquaSCALE, for gathering, analyzing and localizing anomalous operations of increasingly failure-prone community water services. Today, detection of pipe breaks/leaks in water networks takes hours to days. AquaSCALE leverages dynamic data from multiple information sources including IoT (Internet of Things) sensing data, geophysical data, human input, and simulation/modeling engines to create a sensor-simulation-data integration platform that can accurately and quickly identify vul-nerable spots. We propose a two-phase workflow that begins with robust simulation methods using a commercial grade hydraulic simulator - EPANET, enhanced with the support for IoT sensor and pipe failure modelings. It generates a profile of anomalous events using diverse plug-and-play machine learning techniques. The profile then incorporates with external observations (NOAA weather reports and twitter feeds) to rapidly and reliably isolate broken water pipes. We evaluate the two-phase mechanism in canonical and real-world water networks under different failure scenarios. Our results indicate that the proposed approach with offline learning and online inference can locate multiple simultaneous pipe failures at fine level of granularity (individual pipeline level) with high level of accuracy with detection time reduced by orders of magnitude (from hours/days to minutes).
{"title":"Toward An Integrated Approach to Localizing Failures in Community Water Networks","authors":"Qing Han, Phu Nguyen, R. Eguchi, K. Hsu, N. Venkatasubramanian","doi":"10.1109/ICDCS.2017.81","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.81","url":null,"abstract":"We present a cyber-physical-human distributed computing framework, AquaSCALE, for gathering, analyzing and localizing anomalous operations of increasingly failure-prone community water services. Today, detection of pipe breaks/leaks in water networks takes hours to days. AquaSCALE leverages dynamic data from multiple information sources including IoT (Internet of Things) sensing data, geophysical data, human input, and simulation/modeling engines to create a sensor-simulation-data integration platform that can accurately and quickly identify vul-nerable spots. We propose a two-phase workflow that begins with robust simulation methods using a commercial grade hydraulic simulator - EPANET, enhanced with the support for IoT sensor and pipe failure modelings. It generates a profile of anomalous events using diverse plug-and-play machine learning techniques. The profile then incorporates with external observations (NOAA weather reports and twitter feeds) to rapidly and reliably isolate broken water pipes. We evaluate the two-phase mechanism in canonical and real-world water networks under different failure scenarios. Our results indicate that the proposed approach with offline learning and online inference can locate multiple simultaneous pipe failures at fine level of granularity (individual pipeline level) with high level of accuracy with detection time reduced by orders of magnitude (from hours/days to minutes).","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131182449","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 proliferation of mobile devices and the development of communication technology, mobile devices have permeated every aspect of our daily lives. However, in dense network where large crowd of mobile devices try to access to the network simultaneously, the severe interference between mobile devices may incur a remarkable deterioration of the wireless communication quality. How to improve individual's experience in such scenario is a critical yet open problem. Inspired by the mobile device users' usage pattern as well as the characteristic of most wireless communication systems, we propose a framework offering uplink/downlink selection recommendation to different mobile device users to enhance their utility in this paper. The design of the framework starts with formulating the problem as a link selection game. Analysis shows that the game can be categorized as a generalized ordinal potential game whose Nash Equilibrium is guaranteed. We then devise a distributed link selection algorithm to generate a Nash Equilibrium of the game. To accommodate to the characteristic of dense network and the capacity limitation of mobile device, the design of the algorithm shows a light-weight property and does not require each mobile device user to know others' current selection. The probability of incomplete information gathering is also considered. Extensive experiments are conducted to demonstrate the effectiveness and superiority of the proposed framework. Experimental results show that the global average utility increase rate reaches above 20%, and about 70% mobile device users can benefit from using our framework.
{"title":"A Lightweight Recommendation Framework for Mobile User’s Link Selection in Dense Network","authors":"Ji Wang, Xiaomin Zhu, Weidong Bao, Guanlin Wu","doi":"10.1109/ICDCS.2017.34","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.34","url":null,"abstract":"With the proliferation of mobile devices and the development of communication technology, mobile devices have permeated every aspect of our daily lives. However, in dense network where large crowd of mobile devices try to access to the network simultaneously, the severe interference between mobile devices may incur a remarkable deterioration of the wireless communication quality. How to improve individual's experience in such scenario is a critical yet open problem. Inspired by the mobile device users' usage pattern as well as the characteristic of most wireless communication systems, we propose a framework offering uplink/downlink selection recommendation to different mobile device users to enhance their utility in this paper. The design of the framework starts with formulating the problem as a link selection game. Analysis shows that the game can be categorized as a generalized ordinal potential game whose Nash Equilibrium is guaranteed. We then devise a distributed link selection algorithm to generate a Nash Equilibrium of the game. To accommodate to the characteristic of dense network and the capacity limitation of mobile device, the design of the algorithm shows a light-weight property and does not require each mobile device user to know others' current selection. The probability of incomplete information gathering is also considered. Extensive experiments are conducted to demonstrate the effectiveness and superiority of the proposed framework. Experimental results show that the global average utility increase rate reaches above 20%, and about 70% mobile device users can benefit from using our framework.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133474593","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}
M. Canini, Iosif Salem, Liron Schiff, E. Schiller, S. Schmid
Adopting distributed control planes is critical towards ensuring high availability and fault-tolerance of dependable Software-Defined Networks (SDNs). However, designing and bootstrapping a distributed SDN control plane is a challenging task, especially if to be done in-band, without a dedicated control network, and without relying on legacy networking protocols. One of the most appealing and powerful notions of fault-tolerance is self-organization and this paper discusses the possibility of selforganizing algorithms for in-band control planes.
{"title":"A Self-Organizing Distributed and In-Band SDN Control Plane","authors":"M. Canini, Iosif Salem, Liron Schiff, E. Schiller, S. Schmid","doi":"10.1109/ICDCS.2017.328","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.328","url":null,"abstract":"Adopting distributed control planes is critical towards ensuring high availability and fault-tolerance of dependable Software-Defined Networks (SDNs). However, designing and bootstrapping a distributed SDN control plane is a challenging task, especially if to be done in-band, without a dedicated control network, and without relying on legacy networking protocols. One of the most appealing and powerful notions of fault-tolerance is self-organization and this paper discusses the possibility of selforganizing algorithms for in-band control planes.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133421021","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}
Zhongxing Ming, Mingwei Xu, Ning Wang, Bingjie Gao, Qi Li
User data allowance trading emerges as a promising practice in mobile data networks since it can help mobile networks to attract more users. However, to date, there is no study on user data allowance trading in mobile networks. In this paper, we develop a truthful framework that allows users to bid for data allowance. We focus on preventing price cheating, guaranteeing fairness, and minimizing trading maintenance cost in trading. We formulate the data trading process as a double auction problem and develop algorithms to solve the problem. In particular, we use a uniform price auction based on a competitive equilibrium to defend against price cheating and provide fair-ness. Meanwhile, we leverage linear programming to minimize trading maintenance cost. We conduct extensive simulations to demonstrate the performance of the proposed mechanism. The simulation results show that our trading mechanism is truthful and fair, while incurring a minimized maintenance cost.
{"title":"Truthful Auctions for User Data Allowance Trading in Mobile Networks","authors":"Zhongxing Ming, Mingwei Xu, Ning Wang, Bingjie Gao, Qi Li","doi":"10.1109/ICDCS.2017.315","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.315","url":null,"abstract":"User data allowance trading emerges as a promising practice in mobile data networks since it can help mobile networks to attract more users. However, to date, there is no study on user data allowance trading in mobile networks. In this paper, we develop a truthful framework that allows users to bid for data allowance. We focus on preventing price cheating, guaranteeing fairness, and minimizing trading maintenance cost in trading. We formulate the data trading process as a double auction problem and develop algorithms to solve the problem. In particular, we use a uniform price auction based on a competitive equilibrium to defend against price cheating and provide fair-ness. Meanwhile, we leverage linear programming to minimize trading maintenance cost. We conduct extensive simulations to demonstrate the performance of the proposed mechanism. The simulation results show that our trading mechanism is truthful and fair, while incurring a minimized maintenance cost.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132740294","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 ability to accurately track pedestrians is valuable for variant application designs. Although pedestrian tracking has been investigated excessively and owned a well-suited sensing platform, the proposed solutions are far from being mature yet. Pedestrian tracking contains step counting and stride estimation two components. Step counting already has commercial products, but the performance is still unreliable and less trustworthy in practice. Stride estimation even stays in the research stage without ready solutions released on the market. Such a non-negligible gap between long-term research investigation and technique's actual usage exists due to a series of crucial applicability issues unsolved, including design vulnerability to interfering activities, extracting purely body's movement from additive sensor signals, and parameter training without user's intervention. In this paper, we deeply analyze human's gait cycles and obtain inspiring observations to address these issues. We incorporate our techniques into existing pedestrian tracking designs and implement a prototype, PTrack, on LG smartwatch. We find PTrack effectively enhances the system applicability and achieves promising performance under very practical settings.
{"title":"PTrack: Enhancing the Applicability of Pedestrian Tracking with Wearables","authors":"Yonghang Jiang, Zhenjiang Li, Jianping Wang","doi":"10.1109/ICDCS.2017.111","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.111","url":null,"abstract":"The ability to accurately track pedestrians is valuable for variant application designs. Although pedestrian tracking has been investigated excessively and owned a well-suited sensing platform, the proposed solutions are far from being mature yet. Pedestrian tracking contains step counting and stride estimation two components. Step counting already has commercial products, but the performance is still unreliable and less trustworthy in practice. Stride estimation even stays in the research stage without ready solutions released on the market. Such a non-negligible gap between long-term research investigation and technique's actual usage exists due to a series of crucial applicability issues unsolved, including design vulnerability to interfering activities, extracting purely body's movement from additive sensor signals, and parameter training without user's intervention. In this paper, we deeply analyze human's gait cycles and obtain inspiring observations to address these issues. We incorporate our techniques into existing pedestrian tracking designs and implement a prototype, PTrack, on LG smartwatch. We find PTrack effectively enhances the system applicability and achieves promising performance under very practical settings.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114365989","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}
Dimitrios Georgakopoulos, Ali Yavari, P. Jayaraman, R. Ranjan
The Internet of Things (IoT) is a new internet evolution that involves connecting billions of internet-connected devices that we refer to as IoT things. These devices can communicate directly and intelligently over the Internet, and generate a massive amount of data that needs to be consumed by a variety of IoT applications. This paper focuses on the automatic contextualisation of IoT data, which also involves distilling information and knowledge from the IoT aiming to simplify answering the following fundamental questions that often arises in IoT applications: Which data collected by IoT are relevant to myself and the IoT Things I care for? Related work around context management and contextualisation ranges from database techniques that involve query re-writing, to semantic web and rule-based context management approaches, to machine learning and data science-based solutions in mobile and ambient computing. All such existing approaches have two main aspects in common: They are highly incompatible and horribly inefficient from a scalability and performance perspective. In this paper, we discuss a new RISC Contextualisation Framework (RCF) we have developed, implemented key aspects of, and assess its scalability. RCF provides fundamental contextualisation concepts that can be mapped to all existing contextualisation approaches for IoT data (and in this sense, it provides a common denominator that unifies the contextualisation space). RCF can be easily implemented as a cloud-based service, and provides better scalability and performance that any of the existing content management and contextualisation approaches in the IoT space.
{"title":"Towards a RISC Framework for Efficient Contextualisation in the IoT","authors":"Dimitrios Georgakopoulos, Ali Yavari, P. Jayaraman, R. Ranjan","doi":"10.1109/ICDCS.2017.308","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.308","url":null,"abstract":"The Internet of Things (IoT) is a new internet evolution that involves connecting billions of internet-connected devices that we refer to as IoT things. These devices can communicate directly and intelligently over the Internet, and generate a massive amount of data that needs to be consumed by a variety of IoT applications. This paper focuses on the automatic contextualisation of IoT data, which also involves distilling information and knowledge from the IoT aiming to simplify answering the following fundamental questions that often arises in IoT applications: Which data collected by IoT are relevant to myself and the IoT Things I care for? Related work around context management and contextualisation ranges from database techniques that involve query re-writing, to semantic web and rule-based context management approaches, to machine learning and data science-based solutions in mobile and ambient computing. All such existing approaches have two main aspects in common: They are highly incompatible and horribly inefficient from a scalability and performance perspective. In this paper, we discuss a new RISC Contextualisation Framework (RCF) we have developed, implemented key aspects of, and assess its scalability. RCF provides fundamental contextualisation concepts that can be mapped to all existing contextualisation approaches for IoT data (and in this sense, it provides a common denominator that unifies the contextualisation space). RCF can be easily implemented as a cloud-based service, and provides better scalability and performance that any of the existing content management and contextualisation approaches in the IoT space.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"224 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120861186","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}
As Electric Vehicles (EVs) become increasingly popular, their battery-related problems (e.g., short driving range and heavy battery weight) must be resolved as soon as possible. Velocity optimization of EVs to minimize energy consumption in driving is an effective alternative to handle these problems. However, previous velocity optimization methods assume that vehicles will pass through traffic lights immediately at green traffic signals. Actually, a vehicle may still experience a delay to pass a green traffic light due to a vehicle waiting queue in front of the traffic light. In this paper, for the first time, we propose a velocity optimization system which enables EVs to immediately pass green traffic lights without delay. We collected real driving data on a 4.0 km long road section of US-25 highway to conduct extensive trace-driven simulation studies. The experimental results from Matlab and Simulation for Urban MObility (SUMO) traffic simulator show that our velocity optimization system reduces energy consumption by up to 17.5% compared with real driving patterns without increasing trip time.
{"title":"Velocity Optimization of Pure Electric Vehicles with Traffic Dynamics Consideration","authors":"Liuwang Kang, Haiying Shen, Ankur Sarker","doi":"10.1109/ICDCS.2017.220","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.220","url":null,"abstract":"As Electric Vehicles (EVs) become increasingly popular, their battery-related problems (e.g., short driving range and heavy battery weight) must be resolved as soon as possible. Velocity optimization of EVs to minimize energy consumption in driving is an effective alternative to handle these problems. However, previous velocity optimization methods assume that vehicles will pass through traffic lights immediately at green traffic signals. Actually, a vehicle may still experience a delay to pass a green traffic light due to a vehicle waiting queue in front of the traffic light. In this paper, for the first time, we propose a velocity optimization system which enables EVs to immediately pass green traffic lights without delay. We collected real driving data on a 4.0 km long road section of US-25 highway to conduct extensive trace-driven simulation studies. The experimental results from Matlab and Simulation for Urban MObility (SUMO) traffic simulator show that our velocity optimization system reduces energy consumption by up to 17.5% compared with real driving patterns without increasing trip time.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124115988","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}
Jingxuan Wang, Wenting Tu, L. Hui, S. Yiu, E. Wang
Recently, researchers found a new type of attacks, called time synchronization attack (TS attack), in cyber-physical systems. Instead of modifying the measurements from the system, this attack only changes the time stamps of the measurements. Studies show that these attacks are realistic and practical. However, existing detection techniques, e.g. bad data detection (BDD) and machine learning methods, may not be able to catch these attacks. In this paper, we develop a "first difference aware" machine learning (FDML) classifier to detect this attack. The key concept behind our classifier is to use the feature of "first difference", borrowed from economics and statistics. Simulations on IEEE 14-bus system with real data from NYISO have shown that our FDML classifier can effectively detect both TS attacks and other cyber attacks.
{"title":"Detecting Time Synchronization Attacks in Cyber-Physical Systems with Machine Learning Techniques","authors":"Jingxuan Wang, Wenting Tu, L. Hui, S. Yiu, E. Wang","doi":"10.1109/ICDCS.2017.25","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.25","url":null,"abstract":"Recently, researchers found a new type of attacks, called time synchronization attack (TS attack), in cyber-physical systems. Instead of modifying the measurements from the system, this attack only changes the time stamps of the measurements. Studies show that these attacks are realistic and practical. However, existing detection techniques, e.g. bad data detection (BDD) and machine learning methods, may not be able to catch these attacks. In this paper, we develop a \"first difference aware\" machine learning (FDML) classifier to detect this attack. The key concept behind our classifier is to use the feature of \"first difference\", borrowed from economics and statistics. Simulations on IEEE 14-bus system with real data from NYISO have shown that our FDML classifier can effectively detect both TS attacks and other cyber attacks.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124593347","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}
Mincheng Chen, Jingling Yuan, Lin Li, Dongling Liu, Tao Li
Energy data, which consists of energy consumption statistics and other related data in green data centers, grows dramatically. The energy data has great value, but many attributes within it are redundant and unnecessary. Thus attribute reduction for the energy data has been conceived as a critical step. However, many existing attribute reduction algorithms are often computationally time-consuming. To address these issues, we extend the methodology of rough sets to construct data center energy consumption knowledge representation system. By taking good advantage of in-memory computing, an attribute reduction algorithm for energy data using Spark is proposed. In this algorithm, we use a heuristic formula for measuring the significance of attribute to reduce search space, and an efficient algorithm for simplifying energy consumption decision table, which further improve the computation efficiency. The experimental results show the speed of our algorithm gains up to 0.28X performance improvement over the traditional attribute reduction algorithm using Spark.
{"title":"A Fast Heuristic Attribute Reduction Algorithm Using Spark","authors":"Mincheng Chen, Jingling Yuan, Lin Li, Dongling Liu, Tao Li","doi":"10.1109/ICDCS.2017.38","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.38","url":null,"abstract":"Energy data, which consists of energy consumption statistics and other related data in green data centers, grows dramatically. The energy data has great value, but many attributes within it are redundant and unnecessary. Thus attribute reduction for the energy data has been conceived as a critical step. However, many existing attribute reduction algorithms are often computationally time-consuming. To address these issues, we extend the methodology of rough sets to construct data center energy consumption knowledge representation system. By taking good advantage of in-memory computing, an attribute reduction algorithm for energy data using Spark is proposed. In this algorithm, we use a heuristic formula for measuring the significance of attribute to reduce search space, and an efficient algorithm for simplifying energy consumption decision table, which further improve the computation efficiency. The experimental results show the speed of our algorithm gains up to 0.28X performance improvement over the traditional attribute reduction algorithm using Spark.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124626401","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}
Dong Han, Ye Yan, Tao Shu, Liuqing Yang, Shuguang Cui
Mobile cloud storage (MCS) is being extensively used nowadays toprovide data access services to various mobile platforms such assmart phones and tablets. For cross-platform mobile apps, MCS is afoundation for sharing and accessing user data as well as supportingseamless user experience in a mobile cloud computing environment. However, the mobile usage of smart phones or tablets is quite differentfrom legacy desktop computers, in the sense that each user hashis/her own mobile usage pattern. Therefore, it is challenging todesign an efficient MCS that is optimized for individual users. Inthis paper, we investigate a distributed MCS system whoseperformance is optimized by exploiting the fine-grained contextinformation of every mobile user. In this distributed system,lightweight storage servers are deployed pervasively, such that datacan be stored closer to its user. We systematically optimize thedata access efficiency of such a distributed MCS by exploiting threetypes of user context information: mobility pattern, networkcondition, and data access pattern. We propose two optimizationformulations: a centralized one based on mixed-integer linearprogramming (MILP), and a distributed one based on stable matching. We then develop solutions to both formulations. Comprehensivesimulations are performed to evaluate the effectiveness of theproposed solutions by comparing them against their counterpartsunder various network and context conditions.
{"title":"Cognitive Context-Aware Distributed Storage Optimization in Mobile Cloud Computing: A Stable Matching Based Approach","authors":"Dong Han, Ye Yan, Tao Shu, Liuqing Yang, Shuguang Cui","doi":"10.1109/ICDCS.2017.115","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.115","url":null,"abstract":"Mobile cloud storage (MCS) is being extensively used nowadays toprovide data access services to various mobile platforms such assmart phones and tablets. For cross-platform mobile apps, MCS is afoundation for sharing and accessing user data as well as supportingseamless user experience in a mobile cloud computing environment. However, the mobile usage of smart phones or tablets is quite differentfrom legacy desktop computers, in the sense that each user hashis/her own mobile usage pattern. Therefore, it is challenging todesign an efficient MCS that is optimized for individual users. Inthis paper, we investigate a distributed MCS system whoseperformance is optimized by exploiting the fine-grained contextinformation of every mobile user. In this distributed system,lightweight storage servers are deployed pervasively, such that datacan be stored closer to its user. We systematically optimize thedata access efficiency of such a distributed MCS by exploiting threetypes of user context information: mobility pattern, networkcondition, and data access pattern. We propose two optimizationformulations: a centralized one based on mixed-integer linearprogramming (MILP), and a distributed one based on stable matching. We then develop solutions to both formulations. Comprehensivesimulations are performed to evaluate the effectiveness of theproposed solutions by comparing them against their counterpartsunder various network and context conditions.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129731803","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：