2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)最新文献_第3页

Mobile Edge Computing-Based Vehicular Cloud of Cooperative Adaptive Driving for Platooning Autonomous Self Driving 基于移动边缘计算的车辆协同自适应驾驶云队列自动驾驶

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.13

Ren-Hung Huang, Ben-Jye Chang, Yueh-Lin Tsai, Ying-Hsin Liang

{"title":"Mobile Edge Computing-Based Vehicular Cloud of Cooperative Adaptive Driving for Platooning Autonomous Self Driving","authors":"Ren-Hung Huang, Ben-Jye Chang, Yueh-Lin Tsai, Ying-Hsin Liang","doi":"10.1109/SC2.2017.13","DOIUrl":"https://doi.org/10.1109/SC2.2017.13","url":null,"abstract":"The Cooperative Adaptive Cruise Control (CACC) for Human and Autonomous Self-Driving aims to achieve active safe driving that avoids vehicle accidents or traffic jam by exchanging the road traffic information (e.g., traffic flow, traffic density, velocity variation, etc.) among neighbor vehicles. However, in CACC, the butterfly effect is happened while exhibiting asynchronous brakes that easily lead to backward shockwaves and difficult to be removed. Several critical issues should be addressed in CACC, including: 1) difficult to adaptively control the inter-vehicle distances among neighbor vehicles and the vehicle speed, 2) suffering from the butterfly effect, 3) unstable vehicle traffic flow, etc. For addressing above issues in CACC, this paper proposes the Mobile Edge Computing-based vehicular cloud of Cooperative Adaptive Driving (CAD) approach to avoid shockwaves efficiently in platoon driving. Numerical results demonstrate that CAD approach outperforms the compared approaches in number of shockwaves, average vehicle velocity, and average travel time. Additionally, the adaptive platoon length is determined according to the traffic information gathered from the global and local clouds.","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":"130769253","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: 27

Parallelization, Modeling, and Performance Prediction in the Multi-/Many Core Area: A Systematic Literature Review 多/多核心领域的并行化、建模和性能预测:系统文献综述

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.15

Markus Frank, Marcus Hilbrich, Sebastian Lehrig, Steffen Becker

{"title":"Parallelization, Modeling, and Performance Prediction in the Multi-/Many Core Area: A Systematic Literature Review","authors":"Markus Frank, Marcus Hilbrich, Sebastian Lehrig, Steffen Becker","doi":"10.1109/SC2.2017.15","DOIUrl":"https://doi.org/10.1109/SC2.2017.15","url":null,"abstract":"Context: Software developers face complex, connected, and large software projects. The development of such systems involves design decisions that directly impact the quality of the software. For an early decision making, software developers can use model-based prediction approaches for (non-)functional quality properties. Unfortunately, the accuracy of these approaches is challenged by newly introduced hardware features like multiple cores within a single CPU (multicores) and their dependence on shared memory and other shared resources. Objectives: Our goal is to understand whether and how existing model-based performance prediction approaches face this challenge. We plan to use gained insights as foundation for enriching existing prediction approaches with capabilities to predict systems running on multicores. Methods: We perform a Systematic Literature Review (SLR) to identify current model-based prediction approaches in the context of multicores. Results: Our SLR covers the software engineering, embedded systems, High Performance Computing, and Software Performance Engineering domains for which we examined 34 sources in detail. We found various performance prediction approaches which tries to increase prediction accuracy for multicore systems by including shared memory designs to the prediction models. Conclusion: However, our results show that the memory designs models are only in an initial phase. Further research has to be done to improve cache, memory, and memory bandwidth model as well as to include auto tuner support.","PeriodicalId":188326,"journal":{"name":"2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)","volume":"63 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":"121652932","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: 11

Using Data Mining Methods to Detect Simulated Intrusions on a Modbus Network 基于数据挖掘方法的Modbus网络模拟入侵检测

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.29

Szu-Chuang Li, Yennun Huang, Bo-Chen Tai, Chi Lin

{"title":"Using Data Mining Methods to Detect Simulated Intrusions on a Modbus Network","authors":"Szu-Chuang Li, Yennun Huang, Bo-Chen Tai, Chi Lin","doi":"10.1109/SC2.2017.29","DOIUrl":"https://doi.org/10.1109/SC2.2017.29","url":null,"abstract":"In the era of Industry 4.0 we seek to create a smart factory environment in which everything is connected and well coordinated. Smart factories will also be connected to cloud service and/or all kinds of partners outside the boundary of the factory to achieve even better efficiency. However network connectivity also brings threats along with the promise of better efficiency, and makes Smart factories more vulnerable to intruders. There were already security incidents such as Iran's nuclear facilities' infection by the Stuxnet virus and German's steel mill destroyed by hackers in 2014. To protect smart factories from such threats traditional means of intrusion detection on the Internet could be used, but we must also refine them and have them adapted to the context of Industry 4.0. For example, network traffic in a smart factory might be more uniformed and predictable compared to the traffic on the Internet, but one should tolerate much less anomaly as the traffic is usually mission critical, and will cause much more loss once intrusion happens. The most widely used signature-based intrusion detection systems come with a large library of signatures that contains known attack have been proved to be very useful, but without the ability to detect unknown attack. We turn to supervised data mining algorithms to detect intrusions, which will help us to detect intrusions with similar properties with known attacks but not necessarily fully match the signatures in the library. In this study a simulated smart factory environment was built and a series of attacks were implemented. Neural network and decision trees were used to classify the traffic generated from this simulated environment. From the experiments we conclude that for the data set we used, decision tree performed better than neural network for detecting intrusion as it provides better accuracy, lower false negative rate and faster model building time.","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":"130165786","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: 13

Quality Profile-Based Cloud Service Selection for Fulfilling Big Data Processing Requirements 基于质量概况的云服务选择，满足大数据处理需求

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.30

M. Serhani, Hadeel T. El Kassabi, Ikbal Taleb

{"title":"Quality Profile-Based Cloud Service Selection for Fulfilling Big Data Processing Requirements","authors":"M. Serhani, Hadeel T. El Kassabi, Ikbal Taleb","doi":"10.1109/SC2.2017.30","DOIUrl":"https://doi.org/10.1109/SC2.2017.30","url":null,"abstract":"Big data has emerged as promising technology to handle huge and special data. Processing Big data involves selecting the appropriate services and resources thanks to the variety of services offered by different Cloud providers. Such selection is difficult, especially if a set of Big data requirements should be met. In this paper, we propose a dynamic cloud service selection scheme that assess Big data requirements, dynamically map these to the most available cloud services, and then recommend the best match services that fulfill different Big data processing requests. Our selection is conducted in two stages: 1) relies on a Big data task profile that efficiently capture Big data task's requirements and map them to QoS parameters, and then classify cloud providers that best satisfy these requirements, 2) uses the list of selected providers from stage 1 to further select the appropriate Cloud services to fulfill the overall Big Data task requirements. We extend the Analytic Hierarchy Process (AHP) based ranking mechanism to cope with the problem of multi-criteria selection. We conduct a set of experiments using simulated cloud setup to evaluate our selection scheme as well as the extended AHP against other selection techniques. The results show that our selection approach outperforms the others and select efficiently the appropriate cloud services that guarantee Big data task's QoS requirements.","PeriodicalId":188326,"journal":{"name":"2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)","volume":"64 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":"131787766","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: 4

ExpanStor: Multiple Cloud Storage with Dynamic Data Distribution expstor:具有动态数据分布的多个云存储

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.20

Yongmei Wei, Fengmin Chen, D. C. Sheng

引用次数: 6

Pattern-Based Cloud Service Recommendation and Integration for Hybrid Cloud 基于模式的混合云服务推荐与集成

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.40

Joonseok Park, Dong Yun, Ungsoo Kim, Keunhyuk Yeom

引用次数: 3

Platform-as-a-Service for Human-Based Applications: Ontology-Driven Approach 基于人的应用的平台即服务:本体驱动的方法

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.31

A. Smirnov, A. Ponomarev, T. Levashova, N. Shilov

引用次数: 0

Design and Implementation of Scalable and Load-Balanced Virtual Machine Clusters 可扩展和负载均衡虚拟机集群的设计和实现

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.14

Jia-Hong Chang, Hui-Sheng Cheng, Mei-Ling Chiang

{"title":"Design and Implementation of Scalable and Load-Balanced Virtual Machine Clusters","authors":"Jia-Hong Chang, Hui-Sheng Cheng, Mei-Ling Chiang","doi":"10.1109/SC2.2017.14","DOIUrl":"https://doi.org/10.1109/SC2.2017.14","url":null,"abstract":"Server clustering is a cost-effective solution to increase the service capacity and system reliability. It also gives greater scalability for handling the growing and huge amount of service demands. Nowadays, cloud platforms take advantage of virtualization technology and make their actual hosts virtualized. In this study, we explore the issues of implementing server clusters based on virtual machines (VM), including architectures and load distribution algorithms. We utilize Linux Virtual Server (LVS) to design several kinds of VM-based server clusters with different architectures, i.e. Single VM Cluster (SVMC), Hierarchical Multiple VM Clusters (HVMC), and Distributed Multiple VM Clusters (MVMC). In order to provide better load balance among real servers in the cluster, load distribution algorithms originally developed for the server clusters should be redesigned or adapted to VM-based clusters. Therefore, we further propose two kinds of load distribution algorithms named Virtual Machine Least Connections (VMLC) and Virtual Machine Weighted Least Connections (VMWLC). These algorithms not only consider the server loading, but also take into account the difference between physical machines (PMs) and VMs to balance the server loads. Practical implementation on Linux and experimental results show that VM clusters with the single architecture (i.e. SVMC) or the hierarchical architecture (i.e. HVMC) obtain significantly higher performance than the distributed VM cluster (i.e. MVMC) that consists of multiple VM clusters with a DNS to spread the load to VM clusters. The proposed load distribution algorithms outperform the Weighted Least Connections (WLC) which does not distinguish PMs from VMs.","PeriodicalId":188326,"journal":{"name":"2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)","volume":"46 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":"134561245","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: 3

Enhancing Classification Effectiveness of Chinese News Based on Term Frequency 基于词频的中文新闻分类有效性提升

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.26

Tzu-Yi Chan, Yue-Shan Chang

{"title":"Enhancing Classification Effectiveness of Chinese News Based on Term Frequency","authors":"Tzu-Yi Chan, Yue-Shan Chang","doi":"10.1109/SC2.2017.26","DOIUrl":"https://doi.org/10.1109/SC2.2017.26","url":null,"abstract":"For the daily news published on the web, in general, they can be classified into various categories, such as social, politics, entertainment, and so on. These classifications motivate users to watch the desired information. If the classification is wrong, user cannot catch accurately context. How to accurately classify the daily news is becoming an important issue. In this paper, we will propose a method to enhance the effectiveness of news classification. We will utilize the term frequency appeared in variety of classified historical news to training the weighting of each category of each term. And then classify the test news based on the weighting. We propose a framework and an algorithm to training the weighting of each term. The training data, which are over 3500 Chinese news, are collected from UDN and LTN, which are two major electrical news portals in Taiwan. Based on the weighting mechanism, we conduct some experiments to evaluate the effectiveness of the algorithm. The test data are 170 Chinese news, which are collected from Google. The result shows that the traditional manually classification method has up to 13% error classification.","PeriodicalId":188326,"journal":{"name":"2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)","volume":"22 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":"133738468","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: 4

An Energy-Efficient SDN-Based Data Collection Strategy for Wireless Sensor Networks 基于节能sdn的无线传感器网络数据采集策略

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) Pub Date : 2017-11-01 DOI: 10.1109/SC2.2017.21

Wen-Hwa Liao, Ssu-Chi Kuai

引用次数: 4