2011 IEEE Third International Conference on Cloud Computing Technology and Science最新文献

myTrustedCloud: Trusted Cloud Infrastructure for Security-critical Computation and Data Managment myTrustedCloud:安全关键计算和数据管理的可信云基础设施

2011 IEEE Third International Conference on Cloud Computing Technology and Science Pub Date : 2012-08-20 DOI: 10.1145/2361999.2362014

D. Wallom, M. Turilli, Andrew P. Martin, Anbang Ruan, G. Taylor, N. Hargreaves, Alan McMoran

{"title":"myTrustedCloud: Trusted Cloud Infrastructure for Security-critical Computation and Data Managment","authors":"D. Wallom, M. Turilli, Andrew P. Martin, Anbang Ruan, G. Taylor, N. Hargreaves, Alan McMoran","doi":"10.1145/2361999.2362014","DOIUrl":"https://doi.org/10.1145/2361999.2362014","url":null,"abstract":"Cloud Computing provides an optimal infrastructure to utilise and share both computational and data resources whilst allowing a pay-per-use model, useful to cost-effectively manage hardware investment or to maximise its utilisation. Cloud Computing also offers transitory access to scalable amounts of computational resources, something that is particularly important due to the time and financial constraints of many user communities. The growing number of communities that are adopting large public cloud resources such as Amazon Web Services [1] or Microsoft Azure [2] proves the success and hence usefulness of the Cloud Computing paradigm. Nonetheless, the typical use cases for public clouds involve non-business critical applications, particularly where issues around security of utilization of applications or deposited data within shared public services are binding requisites. In this paper, a use case is presented illustrating how the integration of Trusted Computing technologies into an available cloud infrastructure -- Eucalyptus -- allows the security-critical energy industry to exploit the flexibility and potential economical benefits of the Cloud Computing paradigm for their business-critical applications.","PeriodicalId":427190,"journal":{"name":"2011 IEEE Third International Conference on Cloud Computing Technology and Science","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131884725","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: 24

Byzantine Fault-Tolerant MapReduce: Faults are Not Just Crashes 拜占庭容错MapReduce:故障不只是崩溃

2011 IEEE Third International Conference on Cloud Computing Technology and Science Pub Date : 2011-11-29 DOI: 10.1109/CloudCom.2011.15

Pedro Costa, Marcelo Pasin, A. Bessani, M. Correia

引用次数: 51

Optimization-Based Virtual Machine Manager for Private Cloud Computing 基于优化的私有云计算虚拟机管理器

2011 IEEE Third International Conference on Cloud Computing Technology and Science Pub Date : 2011-11-29 DOI: 10.1109/CloudCom.2011.23

D. Niyato

引用次数: 10

VM Leakage and Orphan Control in Open-Source Clouds 开源云中的虚拟机泄漏和孤儿控制

2011 IEEE Third International Conference on Cloud Computing Technology and Science Pub Date : 2011-11-29 DOI: 10.1109/CloudCom.2011.84

Christopher E. Dabrowski, K. Mills

引用次数: 21

A-Tree: Distributed Indexing of Multidimensional Data for Cloud Computing Environments A-Tree:云计算环境下多维数据的分布式索引

2011 IEEE Third International Conference on Cloud Computing Technology and Science Pub Date : 2011-11-29 DOI: 10.1109/CloudCom.2011.61

A. Papadopoulos, Dimitrios Katsaros

引用次数: 31

Towards a Contextualization Solution for Cloud Platform Services 面向云平台服务的情境化解决方案

2011 IEEE Third International Conference on Cloud Computing Technology and Science Pub Date : 2011-11-29 DOI: 10.1109/CLOUDCOM.2011.51

Django Armstrong, K. Djemame, S. Nair, Johan Tordsson, W. Ziegler

引用次数: 23

A Front-end, Hadoop-based Data Management Service for Efficient Federated Clouds 高效联邦云的前端、基于hadoop的数据管理服务

2011 IEEE Third International Conference on Cloud Computing Technology and Science Pub Date : 2011-11-29 DOI: 10.1109/CloudCom.2011.76

George Kousiouris, G. Vafiadis, T. Varvarigou

{"title":"A Front-end, Hadoop-based Data Management Service for Efficient Federated Clouds","authors":"George Kousiouris, G. Vafiadis, T. Varvarigou","doi":"10.1109/CloudCom.2011.76","DOIUrl":"https://doi.org/10.1109/CloudCom.2011.76","url":null,"abstract":"In the recent years, cloud computing has emerged as the new IT paradigm that promises elastic resources on a pay-per-use basis. The challenges of cloud computing are focused around massive data storage and efficient large scale distributed computation. Hadoop, a community driven Apache project has provided an efficient and cost effective platform for large scale computation using the map-reduce methodology, pioneered by Google. In this paper, the design of a Hadoop-based data management system as the front-end service for Cloud data management is investigated. This framework is enriched with Restful APIs in front of Hadoop and a series of components that aim to extend Hadoop's functionality beyond its well known back-end, heavy data processing scope. These components are used to enrich security, logging and data analysis features and also data access compatibility between different but interconnected Cloud providers (federated Clouds). Hadoop capabilities are also extended in a quest for intelligent decision making regarding the choice of the fittest services for federation in a federated cloud scenario, in addition to legally compliant behaviour regarding the geographical location of data storage.","PeriodicalId":427190,"journal":{"name":"2011 IEEE Third International Conference on Cloud Computing Technology and Science","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116673860","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

iHadoop: Asynchronous Iterations for MapReduce iHadoop: MapReduce的异步迭代

2011 IEEE Third International Conference on Cloud Computing Technology and Science Pub Date : 2011-11-29 DOI: 10.1109/CloudCom.2011.21

Eslam Elnikety, T. Elsayed, Hany E. Ramadan

{"title":"iHadoop: Asynchronous Iterations for MapReduce","authors":"Eslam Elnikety, T. Elsayed, Hany E. Ramadan","doi":"10.1109/CloudCom.2011.21","DOIUrl":"https://doi.org/10.1109/CloudCom.2011.21","url":null,"abstract":"MapReduce is a distributed programming framework designed to ease the development of scalable data-intensive applications for large clusters of commodity machines. Most machine learning and data mining applications involve iterative computations over large datasets, such as the Web hyperlink structures and social network graphs. Yet, the MapReduce model does not efficiently support this important class of applications. The architecture of MapReduce, most critically its dataflow techniques and task scheduling, is completely unaware of the nature of iterative applications, tasks are scheduled according to a policy that optimizes the execution for a single iteration which wastes bandwidth, I/O, and CPU cycles when compared with an optimal execution for a consecutive set of iterations. This work presents iHadoop, a modified MapReduce model, and an associated implementation, optimized for iterative computations. The iHadoop model schedules iterations asynchronously. It connects the output of one iteration to the next, allowing both to process their data concurrently. iHadoop's task scheduler exploits inter-iteration data locality by scheduling tasks that exhibit a producer/consumer relation on the same physical machine allowing a fast local data transfer. For those iterative applications that require satisfying certain criteria before termination, iHadoop runs the check concurrently during the execution of the subsequent iteration to further reduce the application's latency. This paper also describes our implementation of the iHadoop model, and evaluates its performance against Hadoop, the widely used open source implementation of MapReduce. Experiments using different data analysis applications over real-world and synthetic datasets show that iHadoop performs better than Hadoop for iterative algorithms, reducing execution time of iterative applications by 25% on average. Furthermore, integrating iHadoop with HaLoop, a variant Hadoop implementation that caches invariant data between iterations, reduces execution time by 38% on average.","PeriodicalId":427190,"journal":{"name":"2011 IEEE Third International Conference on Cloud Computing Technology and Science","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117009805","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: 63

Towards Reproducible eScience in the Cloud 迈向云中的可复制科学

2011 IEEE Third International Conference on Cloud Computing Technology and Science Pub Date : 2011-11-29 DOI: 10.1109/CloudCom.2011.89

Jonathan Klinginsmith, M. Mahoui, Yuqing Wu

{"title":"Towards Reproducible eScience in the Cloud","authors":"Jonathan Klinginsmith, M. Mahoui, Yuqing Wu","doi":"10.1109/CloudCom.2011.89","DOIUrl":"https://doi.org/10.1109/CloudCom.2011.89","url":null,"abstract":"Whether it be data from ubiquitous devices such as sensors or data generated from telescopes or other laboratory instruments, technology apparent in many scientific disciplines is generating data at rates never witnessed before. Computational scientists are among the many who perform inductive experiments and analyses on these data with the goal of answering scientific questions. These computationally demanding experiments and analyses have become a common occurrence, resulting in a shift in scientific discovery, and thus leading to the term eScience. To perform eScience experiments and analysis at scale, one must have an infrastructure with enough computing power and storage space. The advent of cloud computing has allowed infrastructures and platforms to be created with theoretical limitless bounds, thus providing an attractive solution to this need. In this work, we create a reproducible process for the construction of eScience computing environments on top of cloud computing infrastructures. Our solution separates the construction of these environments into two distinct layers: (1) the infrastructure layer and (2) the software layer. We provide results of running our framework on two different computational clusters within two separate cloud computing environments to demonstrate that our framework can facilitate the replication or extension of an eScience experiment.","PeriodicalId":427190,"journal":{"name":"2011 IEEE Third International Conference on Cloud Computing Technology and Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129531614","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: 18

Using Mahout for Clustering Wikipedia's Latest Articles: A Comparison between K-means and Fuzzy C-means in the Cloud 使用Mahout聚类维基百科的最新文章:云中的K-means和模糊C-means的比较

2011 IEEE Third International Conference on Cloud Computing Technology and Science Pub Date : 2011-11-29 DOI: 10.1109/CLOUDCOM.2011.86

R. Esteves, Chunming Rong

引用次数: 73