Xu Zhang, Kefeng Wei, Lei Guo, Weigang Hou, Jingjing Wu
{"title":"SDN-based Resilience Solutions for Smart Grids","authors":"Xu Zhang, Kefeng Wei, Lei Guo, Weigang Hou, Jingjing Wu","doi":"10.1109/ICSN.2016.7501931","DOIUrl":"https://doi.org/10.1109/ICSN.2016.7501931","url":null,"abstract":"Due to the nature of heterogeneity and distributed control, the existing electric power/data communication network based on Synchronous Digital Hierarchy (SDH) has been unable to meet the requirements of dynamic resource allocation and fine-grained traffic monitoring in smart grids. The OpenFlow-based Software Defined Network (SDN) allows operators dynamically control and monitor the whole network using the software running on the operating system of the centralized controller (e.g., NOX controller). And NOX controller can dynamically configure end-to-end paths. Thus in this paper, we propose SDN-based resilience solutions for the scenarios of network expansion and fault in smart grids. The feasibility and efficiency of the proposed resilience solutions is verified, especially, the performances, such as the CPU utilization of NOX controller and end-to-end delay, are quantitatively evaluated in our experimental platform. The experimental results indicate that the dynamic end-to-end path restoration can be achieved within tens of milliseconds by using our SDN-based resilience solutions.","PeriodicalId":282295,"journal":{"name":"2016 International Conference on Software Networking (ICSN)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125440799","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}
{"title":"Towards Container-Based Resource Management for the Internet of Things","authors":"T. Renner, Marius Meldau, Andreas Kliem","doi":"10.1109/ICSN.2016.7501933","DOIUrl":"https://doi.org/10.1109/ICSN.2016.7501933","url":null,"abstract":"The Internet of Things (IoT) paradigm gains momentum for vendors, developers and users. A variety of available devices and technologies promote the deployment of solutions and applications in various domains. However, the increasing amount of IoT devices leads to an increasing amount of resources made available to the users. If devices like smart phones or smart TVs are considered, this includes computing and storage resources. In order to increase the utilization of these IoT resources and reduce the amount of generated network traffic, we propose a container-based resource allocation scheme. The approach allows various applications and users to dynamically allocate resources offered by edge devices and process IoT data close to the source. The approach is evaluated regarding its feasibility in terms of performance on resource constrained IoT devices.","PeriodicalId":282295,"journal":{"name":"2016 International Conference on Software Networking (ICSN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130263240","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}
{"title":"Big-Data-Enabled Software-Defined Cellular Network Management","authors":"Jiayao Wen, V. Li","doi":"10.1109/ICSN.2016.7501923","DOIUrl":"https://doi.org/10.1109/ICSN.2016.7501923","url":null,"abstract":"With the development of big data collection and analysis technologies, the abundant mobile network information and traffic data provide opportunities for researchers to analyze and understand mobile networks better. However, the existing cellular network architecture do not facilitate the deployment of big mobile data analysis in practical networks. In order to use these data for network management, in this paper, we propose a new software-defined cellular network (SDCN) architecture, namely Big-Data-Enabled Architecture (BDEA), which can support big mobile data analysis and storage for efficient cellular network resource allocation. Based on BDEA, we also propose a virtuous network management cycle of data collection, data analysis and network deployment, which can increase the efficiency of network improvement and feedback. Several application cases of network resource allocation in BDEA are discussed to illustrate how big mobile data analysis can benefit network optimization.","PeriodicalId":282295,"journal":{"name":"2016 International Conference on Software Networking (ICSN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123798145","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}
{"title":"Making Recommendations Better: The Role of User Online Purchase Intention Identification","authors":"Kuan Fang, Qi Zhang, Zhuoran Zhuang, Zi-Ke Zhang","doi":"10.1109/ICSN.2016.7501928","DOIUrl":"https://doi.org/10.1109/ICSN.2016.7501928","url":null,"abstract":"The past few years has witnessed the great success of recommender systems, which can significantly help users find relevant and interesting items from the vast array of online products. Recently, a vast class of researches in this area mainly focus on making recommendations by designing effective algorithms. Comparatively, the user intention, especially the role of purchase intention in recommender systems is relatively lack of study. In this paper, with real e-commercial data from Tmall.com, we firstly analyze users' online behaviors and propose a scenario-based identification approach to classify users into two groups: one with obvious purchase intention, and another without such motivation. We then use Random Forest classification method to validate its usefulness. Subsequently, we implement an online demo to visually detect the real-time purchase intention. Finally, we employ the classical item-based collaborative filtering framework to provide recommendations to those two group users. Experimental results show that recommendation performance indeed can be enhanced by identifying online purchase intention.","PeriodicalId":282295,"journal":{"name":"2016 International Conference on Software Networking (ICSN)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125547289","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}