2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)最新文献

A Lightweight Approach to Detect the Low/High Rate IP Spoofed Cloud DDoS Attacks 一种低速率/高速率IP欺骗云DDoS攻击的轻量级检测方法

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.25

Neha Agrawal, S. Tapaswi

引用次数: 19

A Distributed Cloud Service for the Resolution of SAT 面向SAT分辨率的分布式云服务

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.9

Yanik Ngoko, D. Trystram, C. Cérin

{"title":"A Distributed Cloud Service for the Resolution of SAT","authors":"Yanik Ngoko, D. Trystram, C. Cérin","doi":"10.1109/SC2.2017.9","DOIUrl":"https://doi.org/10.1109/SC2.2017.9","url":null,"abstract":"In this paper, we introduce a new parallel and distributed algorithm for the resolution of the satisfiability problem. The proposed algorithm is based on algorithm portfolio and is intended to be used for servicing requests in a distributed cloud. The core of our contribution is the modeling of the optimal resource sharing schedule in parallel executions and the proposition of heuristics for its approximation. For this purpose, we reformulate a computational problem introduced in prior work. The main assumption is that it is possible to learn the optimal resource sharing from traces collected on past executions on a representative set of instances. We show that the learning can be formalized as a set coverage problem. Then, we propose to solve it by approximation and dynamic programming algorithms. These algorithms are based on classical greedy algorithms for the maximum coverage problem. Finally, we conduct an experimental evaluation for comparing the performance of the various proposed algorithms. The results show that some algorithms become more competitive if we intend to determine the trade-off between their quality and the runtime required for their computation.","PeriodicalId":188326,"journal":{"name":"2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132404993","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}

引用次数: 1

Cyber Security Body of Knowledge 网络安全知识体系

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.23

Evon M. O. Abu-Taieh

引用次数: 69

A Hidden Markov Model-Based Map-Matching Approach for Low-Sampling-Rate GPS Trajectories 基于隐马尔可夫模型的低采样率GPS轨迹匹配方法

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.52

Yu-Ling Hsueh, Ho-Chian Chen, Wei-Jie Huang

{"title":"A Hidden Markov Model-Based Map-Matching Approach for Low-Sampling-Rate GPS Trajectories","authors":"Yu-Ling Hsueh, Ho-Chian Chen, Wei-Jie Huang","doi":"10.1109/SC2.2017.52","DOIUrl":"https://doi.org/10.1109/SC2.2017.52","url":null,"abstract":"Map matching is the process of matching a series of recorded geographic coordinates (e.g., a GPS trajectory) to a road network. Due to GPS positioning errors and the sampling constraints, the GPS data collected by the GPS devices are not precise, and the location of a user cannot always be correctly shown on the map. Unfortunately, most current map-matching algorithms only consider the distance between the GPS points and the road segments, the topology of the road network, and the speed constraint of the road segment to determine the matching results. In this paper, we propose a spatio-temporal based matching algorithm (STD-matching) for low-sampling-rate GPS trajectories. STD-matching considers the spatial features such as the distance information and topology of the road network, the speed constraints of the road network, and the realtime moving direction which shows the movement of the user. In our experiments, we compare STD-matching with three existing algorithms, the ST-matching algorithm, the stMM algorithm, and the HMM-RCM algorithm, using a real data set. The experiment results show that our STD-matching algorithm outperforms the three existing algorithms in terms of matching accuracy.","PeriodicalId":188326,"journal":{"name":"2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)","volume":"73 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126106989","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}

引用次数: 10

Reducing Imbalance Ratio in MapReduce 减少MapReduce中的失衡比例

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.54

Hsing-Lung Chen, Y. Shen

引用次数: 2

A Recommendation-Based Parameter Tuning Approach for Hadoop 基于推荐的Hadoop参数调优方法

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.41

Lin Cai, Yong Qi, Jingwei Li

{"title":"A Recommendation-Based Parameter Tuning Approach for Hadoop","authors":"Lin Cai, Yong Qi, Jingwei Li","doi":"10.1109/SC2.2017.41","DOIUrl":"https://doi.org/10.1109/SC2.2017.41","url":null,"abstract":"Nowadays we have entered the big data era. Hadoop, one of the popular big data processing platforms, has many parameters that relate closely to the utilization of resources (e.g. CPU or memory). Tuning these parameters thus becomes one of the important approaches to improve the resource utilization of Hadoop. However, tuning parameters manually is impractical because the time cost fortuning is too high. Hence it is necessary to configure parameters automatically and quickly to optimize resource utilization. The former auto-tuning methods often take a long time before getting the optimal configuration, which would reduce the overall resource efficiency of cluster. In this paper, we propose mrEtalon, an adaptive tuning framework to recommend a near-optimal configuration for the new job in a short time. mrEtalon sets a configuration repository to provide candidate configurations, as well as a collaborative filtering based recommendation engine that can accelerate the optimization for parameters. We have deployed mrEtalon in our experimental cluster, and the results demonstrate that, for a new MapReduce application, compared to the former methods, mrEtalon can reduce the recommend time to 20% to 30% while keeping nearly the same recommendation quality.","PeriodicalId":188326,"journal":{"name":"2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130080521","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}

引用次数: 7

Analysis of Influential Factors in Secondary PM2.5 by K-Medoids and Correlation Coefficient 二次PM2.5影响因素的k -介质和相关系数分析

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.34

Jui-Hung Chang, Chien-Yuan Tseng, Hung-Hsi Chiang, Ren-Hung Hwang

{"title":"Analysis of Influential Factors in Secondary PM2.5 by K-Medoids and Correlation Coefficient","authors":"Jui-Hung Chang, Chien-Yuan Tseng, Hung-Hsi Chiang, Ren-Hung Hwang","doi":"10.1109/SC2.2017.34","DOIUrl":"https://doi.org/10.1109/SC2.2017.34","url":null,"abstract":"There are many influential factors in PM2.5, reducing the emission of PM2.5 is one of international subjects. In recent years, it is indicated that one of the sources of secondary PM2.5 is the complex chemical reaction between NH3 and air pollutants (VOCs, particulate matter, NOx, SOx). The Committee on Agriculture of FAO indicates that 64% of NH3 emission on the earth surface is derived from stock raising which motivates this study to discuss following two subjects based on Open Government Data. Subject 1 calculates the effect of the controlled air pollutants (VOCs, particulate matter, NOx, SOx) and the quantity of livestock (e.g. pigs, chickens and so on) nearby the air monitoring stations on the annual mean of PM2.5. Subject 2 uses Apache Spark as Cloud computing platform, the air monitoring stations are geographically clustered by K-medoids to calculate the Spearman's correlation coefficient of pollution source and PM2.5 of each cluster. The experimental results show that the monitoring station with more air pollutants and livestock raised nearby has higher annual mean PM2.5 concentration. The results are expected to provide the government bodies to make environmental decisions and the plants and livestock farms to install air monitors to analyze the air quality data. Our ultimate goals are to improve the environment and reduce both the emission of PM2.5 and the probability of getting cardiovascular disease.","PeriodicalId":188326,"journal":{"name":"2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129061613","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}

引用次数: 0

Building Microclouds at the Network Edge with the Cloudy Platform 使用云平台在网络边缘构建微云

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.49

Felix Freitag, R. P. Centelles, L. Navarro

引用次数: 1

Using ANN to Analyze the Correlation Between Tourism-Related Hot Words and Tourist Numbers: A Case Study in Japan 使用 ANN 分析旅游相关热词与游客人数之间的相关性：日本案例研究

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.27

Jui-Hung Chang, Chien-Yuan Tseng, Ren-Hung Hwang, Mingcao Ma

{"title":"Using ANN to Analyze the Correlation Between Tourism-Related Hot Words and Tourist Numbers: A Case Study in Japan","authors":"Jui-Hung Chang, Chien-Yuan Tseng, Ren-Hung Hwang, Mingcao Ma","doi":"10.1109/SC2.2017.27","DOIUrl":"https://doi.org/10.1109/SC2.2017.27","url":null,"abstract":"Google's search engine has recorded the popularity of a great number of tourism-related hot words. Prior to vacationing, many people will search the four dimensions of tourism, namely food, fashion, accommodation and transportation, on the Internet before an overseas trip. Exploring the correlation between popularity trends of tourism-related hot words and the number of tourists visiting a particular destination is a potentially valuable research area for the tourist industry. Therefore, this study counted the occurrence frequency of words related to Japanese tourism in the Google search engine and in tourism articles on electronic news websites. With these data, it calculated the Pearson correlation coefficient of the number of Taiwanese tourists visiting Japan \"n\" months later. Additionally, a deep learning (Artificial Neural Network) model was established, and the relationship between the popularity scores of tourism-related hot words and the interval of the number of Taiwanese tourists in Japan was examined. The research results show that the popularity of tourism-related hot words on Google is highly related to the number of Taiwanese tourists visiting Japan.","PeriodicalId":188326,"journal":{"name":"2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127046436","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}

引用次数: 2

A Mobile Cloud-Based Biofeedback Platform for Evaluating Medication Response 一种基于云的移动生物反馈平台，用于评估药物反应

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.35

Yu-Zheng Lai, Chih-Hua Tai, Yue-Shan Chang, Kuo-Hsuan Chung

{"title":"A Mobile Cloud-Based Biofeedback Platform for Evaluating Medication Response","authors":"Yu-Zheng Lai, Chih-Hua Tai, Yue-Shan Chang, Kuo-Hsuan Chung","doi":"10.1109/SC2.2017.35","DOIUrl":"https://doi.org/10.1109/SC2.2017.35","url":null,"abstract":"In recent years, biofeedback has been widely applied into diagnosis and treatment of various diseases. There are also increasingly research exploiting various ICT (Information & Communication Technology) technologies, such as cloud technology, to achieve diagnosis and treatment. Therefore, how to use mobile cloud technology to assist the disease's diagnosis, to record treatment status, and to infer the result will be an important issue. In this paper, we will propose a mobile cloud platform and framework for the patient of mental illness for evaluating medication response through a variety of biofeedback information collection, integration, and fusion, so that physician can know the patient's situation. The physiological data including Heart Rate Variability and Brain Wave are collected through wearable sensors. And the psychological data is collected through monthly mood chart. The biofeedback physiological and psychological data can be fused into together to show the medication response after patient taking some medicines. An APP for the framework has been developed to show the effectiveness.","PeriodicalId":188326,"journal":{"name":"2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116492138","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}

引用次数: 1