2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)最新文献_第3页

Deterministic Dendritic Cell Algorithm Application to Smart Grid Cyber-Attack Detection 确定性树突状细胞算法在智能电网网络攻击检测中的应用

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.12

O. Igbe, Ihab Darwish, T. Saadawi

引用次数: 24

Performance of Scientific Simulations on QCT Developer Cloud: A Case Study of Molecular Dynamic and Quantum Chemistry Simulations QCT开发者云上科学模拟的性能:以分子动力学和量子化学模拟为例

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.54

P. Madhavan, P. Young, Stephen Chang

引用次数: 2

Research on Faceted Search Method for Water Data Catalogue Service 面向水数据目录服务的分面搜索方法研究

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.38

Jun Feng, Shengqiu Kong, B. Du, Jiamin Lu

引用次数: 1

Architecting Cyber Defense: A Survey of the Leading Cyber Reference Architectures and Frameworks 构建网络防御:领先的网络参考架构和框架的调查

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.37

Risa Savold, Natalie Dagher, Preston Frazier, D. McCallam

引用次数: 6

Speed Up Weather Prediction on QCT Developer Cloud: A Case Study on Knights Landing Platform 基于QCT开发者云的天气预报提速——以骑士登陆平台为例

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.48

Gong-Do Hwang, Stephen Chang

引用次数: 0

Improving the Energy Efficiency for Parallel Applications Running on Clusters 提高运行在集群上的并行应用程序的能源效率

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.52

Weifeng Liu, Bin Gong, Meng Guo

{"title":"Improving the Energy Efficiency for Parallel Applications Running on Clusters","authors":"Weifeng Liu, Bin Gong, Meng Guo","doi":"10.1109/CSCloud.2017.52","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.52","url":null,"abstract":"Now cloud computing is rapidly growing as an alternative to traditional computing architecture. However, it is based on models like cluster computing in general. Thus, improving the energy consumption of the cluster system is the basis for the green cloud. In order to reach exascale computing, more and more efforts are made to improve the energy consumption and efficiency in high performance computing systems. As the de facto standard for designing parallel applications in cluster environment, the Message Passing Interface has been widely used in high performance computing. Therefore, getting the energy consumption information of MPI applications is critical for improving the energy efficiency of cluster systems. By creating a distributed measuring framework which can collect all nodes' energy consumption without the aid of power meters, it is possible to get the detailed energy information of an MPI application. In this work, we present MEMT, a software framework that eases the energy collection in cluster environment. Using this tool, it is viable to find out parameters that affect an MPI program's energy efficiency and to build the models for execution time and energy consumption. Based on the pre-built models, energy saving strategy can be designed. The use of this tool is tested in a cluster.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"70 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":"121904448","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

A New Energy Efficient VM Scheduling Algorithm for Cloud Computing Based on Dynamic Programming 一种新的基于动态规划的云计算节能虚拟机调度算法

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.46

K. Zhang, Tong Wu, Siyuan Chen, Linsen Cai, Chao Peng

引用次数: 12

FUD — Balancing Scheduling Parameters in Shared Computing Environments FUD—在共享计算环境中均衡调度参数

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.60

A. Sedighi, Milton L. Smith, Yuefen Deng

引用次数: 3

HDFT++ Hybrid Data Flow Tracking for SaaS Cloud Services 用于SaaS云服务的hdft++混合数据流跟踪

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.9

Alexander Fromm, Vladislav Stepa

{"title":"HDFT++ Hybrid Data Flow Tracking for SaaS Cloud Services","authors":"Alexander Fromm, Vladislav Stepa","doi":"10.1109/CSCloud.2017.9","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.9","url":null,"abstract":"SaaS based cloud computing promises to provide dedicated and specialized computational resources on-premise and on a pay-per-use base to cloud consumers. These benefits, however, are traded with data confidentiality concerns: once data is transmitted to a cloud service, cloud consumers loose control over their data and remain in uncertainty about how their data is processed and disseminated inside the service. To counteract those concerns, we provide HDFT++, a hybrid data flow tracking approach to screen how data disseminate inside a cloud service. That way for instance, cloud service consumers are provided with valuable and detailed information to audit their cloud-resident data. Our approach is innovative, as we combine statically computed information flow analysis results with dynamic run-time data flow tracking mechanisms to monitor only those program locations inside a SaaS service that are actually relevant for a flow of data. Our evaluation results show, that our solution, while collecting run-time information, imposes less or at least equivalent performance overhead on the service under scrutiny than related work. Moreover, as we only track the flow of data at the service level, we could achieve by design a better balance between performance overhead and portability of the monitored service.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"190 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":"117347476","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 Improved Straggler Identification Scheme for Data-Intensive Computing on Cloud Platforms 云平台上数据密集型计算的一种改进的离散者识别方案

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.64

Wei Dai, Ibrahim Adel Ibrahim, M. Bassiouni

{"title":"An Improved Straggler Identification Scheme for Data-Intensive Computing on Cloud Platforms","authors":"Wei Dai, Ibrahim Adel Ibrahim, M. Bassiouni","doi":"10.1109/CSCloud.2017.64","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.64","url":null,"abstract":"One of the challenges faced by data-intensive computing is the problem of stragglers, which can significantly increase the job completion time. Various proactive and reactive straggler mitigation techniques have been developed to address the problem. The straggler identification scheme is a crucial part of the straggler mitigation techniques, as only when stragglers are detected not only correctly but also early enough, the improvement in job completion time can make a real difference. Although the classical standard deviation method is a widely adopted straggler identification scheme, it is not an ideal solution due to certain inherent limitations. In this paper, we present Tukey's method, another statistical method for outlier detection, which is more suitable for the identification of stragglers for two reasons. First, it is robust to extreme observations from stragglers. Second, it can identify stragglers and, more importantly, start speculative execution earlier than the standard deviation method. Our extensive simulation results confirm that Tukey's method can remarkably outperform the standard deviation method.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"47 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120878220","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