2014 IEEE 7th International Conference on Cloud Computing最新文献_第4页

Methodology for Semi-automatic Development of Cloud-Based Business Applications 基于云的业务应用程序的半自动开发方法

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.139

Hind Benfenatki, Catarina Ferreira Da Silva, A. Benharkat, P. Ghodous, F. Biennier

引用次数: 2

On the Feasibility of Deploying Software Attestation in Cloud Environments 云环境下部署软件认证的可行性研究

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.27

Abhrajit Ghosh, Angelo Sapello, A. Poylisher, C. Chiang, A. Kubota, T. Matsunaka

引用次数: 7

CellCloud: A Novel Cost Effective Formation of Mobile Cloud Based on Bidding Incentives CellCloud:一种基于竞价激励的具有成本效益的新型移动云

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.36

S. Noor, Ragib Hasan, Munirul M. Haque

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引用次数: 44

Optimal Virtual Machine Placement in Large-Scale Cloud Systems 大规模云系统中的最佳虚拟机布局

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.64

Hana Teyeb, Ali Balma, N. Hadj-Alouane, S. Tata

引用次数: 19

Emulation-as-a-Service - The Past in the Cloud 仿真即服务——云中的过去

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.124

Thomas Liebetraut, K. Rechert, Isgandar Valizada, Konrad Meier, Dirk von Suchodoletz

引用次数: 9

Improving Hadoop Service Provisioning in a Geographically Distributed Cloud 改进地理分布式云中的Hadoop服务发放

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.65

Qi Zhang, Ling Liu, Kisung Lee, Yang Zhou, Aameek Singh, N. Mandagere, Sandeep Gopisetty, Gabriel Alatorre

{"title":"Improving Hadoop Service Provisioning in a Geographically Distributed Cloud","authors":"Qi Zhang, Ling Liu, Kisung Lee, Yang Zhou, Aameek Singh, N. Mandagere, Sandeep Gopisetty, Gabriel Alatorre","doi":"10.1109/CLOUD.2014.65","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.65","url":null,"abstract":"With more data generated and collected in a geographically distributed manner, combined by the increased computational requirements for large scale data-intensive analysis, we have witnessed the growing demand for geographically distributed Cloud datacenters and hybrid Cloud service provisioning, enabling organizations to support instantaneous demand of additional computational resources and to expand inhouse resources to maintain peak service demands by utilizing cloud resources. A key challenge for running applications in such a geographically distributed computing environment is how to efficiently schedule and perform analysis over data that is geographically distributed across multiple datacenters. In this paper, we first compare multi-datacenter Hadoop deployment with single-datacenter Hadoop deployment to identify the performance issues inherent in a geographically distributed cloud. A generalization of the problem characterization in the context of geographically distributed cloud datacenters is also provided with discussions on general optimization strategies. Then we describe the design and implementation of a suite of system-level optimizations for improving performance of Hadoop service provisioning in a geo-distributed cloud, including prediction-based job localization, configurable HDFS data placement, and data prefetching. Our experimental evaluation shows that our prediction based localization has very low error ratio, smaller than 5%, and our optimization can improve the execution time of Reduce phase by 48.6%.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126943851","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: 36

A Context Based Scheduling Approach for Adaptive Business Process in the Cloud 一种基于上下文的云适应业务流程调度方法

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.137

Molka Rekik, Khouloud Boukadi, H. Ben-Abdallah

引用次数: 7

A Privacy Maturity Model for Cloud Storage Services 云存储服务的隐私成熟度模型

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.135

Carlo Marcelo Revoredo da Silva, Jose Silva, Ricardo Marinho Melo, Ricardo Batista Rodrigues, Lucien Rocha Lucien, Sandro Pereira De Melo, Adolfo Colares, V. Garcia

引用次数: 3

PriDyn: Framework for Performance Specific QoS in Cloud Storage PriDyn:云存储中特定性能QoS的框架

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.15

Nitisha Jain, J. Lakshmi

{"title":"PriDyn: Framework for Performance Specific QoS in Cloud Storage","authors":"Nitisha Jain, J. Lakshmi","doi":"10.1109/CLOUD.2014.15","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.15","url":null,"abstract":"Virtualization is one of the key enabling technologies for cloud computing. Although it facilitates improved utilization of resources, virtualization can lead to performance degradation due to the sharing of physical resources like CPU, memory, network interfaces, disk controllers, etc. Multi-tenancy can cause highly unpredictable performance for concurrent I/O applications running inside virtual machines that share local disk storage in cloud. Disk I/O requests in a typical cloud setup may have varied requirements in terms of latency and throughput as they arise from a range of heterogeneous applications having diverse performance goals. This necessitates providing differential performance services to different I/O applications. In this paper, we present PriDyn, a novel scheduling framework which is designed to consider I/O performance metrics of applications such as acceptable latency and convert them to an appropriate priority value for disk access based on the current system state. This framework aims to provide differentiated I/O service to various applications and ensures predictable performance for critical applications in multi-tenant cloud environment. We demonstrate that this framework achieves appreciable enhancements in I/O performance indicating that this approach is a promising step towards enabling QoS guarantees on cloud storage.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130461693","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

Evaluation of Highly Reliable Cloud Computing Systems Using Non-sequential Monte Carlo Simulation 使用非顺序蒙特卡罗模拟评估高可靠性云计算系统

2014 IEEE 7th International Conference on Cloud Computing Pub Date : 2014-06-27 DOI: 10.1109/CLOUD.2014.133

B. Snyder, R. Green, V. Devabhaktuni, Mansoor Alam

引用次数: 0