2010 IEEE Second International Conference on Cloud Computing Technology and Science最新文献_第10页

A Privacy Impact Assessment Tool for Cloud Computing 面向云计算的隐私影响评估工具

2010 IEEE Second International Conference on Cloud Computing Technology and Science Pub Date : 2010-11-30 DOI: 10.1109/CloudCom.2010.27

David Tancock, Siani Pearson, A. Charlesworth

引用次数: 39

Rapid Processing of Synthetic Seismograms Using Windows Azure Cloud 使用Windows Azure云快速处理合成地震记录

2010 IEEE Second International Conference on Cloud Computing Technology and Science Pub Date : 2010-11-30 DOI: 10.1109/CloudCom.2010.110

Vedaprakash Subramanian, Liqiang Wang, En-Jui Lee, Po Chen

引用次数: 38

A Study in Hadoop Streaming with Matlab for NMR Data Processing 基于Hadoop流的核磁共振数据处理研究

2010 IEEE Second International Conference on Cloud Computing Technology and Science Pub Date : 2010-11-30 DOI: 10.1109/CloudCom.2010.70

Kalpa Gunaratna, Paul E. Anderson, Ajith Ranabahu, A. Sheth

{"title":"A Study in Hadoop Streaming with Matlab for NMR Data Processing","authors":"Kalpa Gunaratna, Paul E. Anderson, Ajith Ranabahu, A. Sheth","doi":"10.1109/CloudCom.2010.70","DOIUrl":"https://doi.org/10.1109/CloudCom.2010.70","url":null,"abstract":"Applying Cloud computing techniques for analyzing large data sets has shown promise in many data-driven scientific applications. Our approach presented here is to use Cloud computing for Nuclear Magnetic Resonance (NMR)data analysis which normally consists of large amounts of data. Biologists often use third party or commercial software for ease of use. Enabling the capability to use this kind of software in a Cloud will be highly advantageous in many ways. Scripting languages especially designed for clouds may not have the flexibility biologists need for their purposes. Although this is true, they are familiar with special software packages that allow them to write complex calculations with minimum effort, but are often not compatible with a Cloud environment. Therefore, biologists who are trying to perform analysis on NMR data, acquire many advantages due to our proposed solution. Our solution gives them the flexibility to Cloud-enable their familiar software and it also enables them to perform calculations on a significant amount of data that was not previously possible. Our study is also applicable to any other environment in need of similar flexibility. We are currently in the initial stage of developing a framework for NMR data analysis.","PeriodicalId":130987,"journal":{"name":"2010 IEEE Second International Conference on Cloud Computing Technology and Science","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114827134","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

Affinity-Aware Dynamic Pinning Scheduling for Virtual Machines 虚拟机亲和性感知动态固定调度

2010 IEEE Second International Conference on Cloud Computing Technology and Science Pub Date : 2010-11-30 DOI: 10.1109/CloudCom.2010.51

Zhi Li, Yuebin Bai, Huiyong Zhang, Yao Ma

{"title":"Affinity-Aware Dynamic Pinning Scheduling for Virtual Machines","authors":"Zhi Li, Yuebin Bai, Huiyong Zhang, Yao Ma","doi":"10.1109/CloudCom.2010.51","DOIUrl":"https://doi.org/10.1109/CloudCom.2010.51","url":null,"abstract":"Virtualization provides an effective management in server consolidation. The transparence enables different kinds of servers running in the same platform, making full use of hardware resource. However, virtualization introduces two-level schedulers: one from Guest OS, where the tasks are scheduled to virtual CPUs (VCPUs), the other from the virtual machine monitor (VMM), where VCPUs are scheduled to CPUs. As a result, the lower level scheduler is ignorant of the task information so that it cannot allocate appropriate proportion of CPU resource for every Guest OS in some cases. This paper presents an affinity-aware Dynamic Pinning Scheduling scheduler (DP-Scheduling). We aim at two objects: Bridging the semantic gap between Guest OS and VMM, introducing an affinity-aware method and providing the tasks information about CPU affinity to VMM, Bringing up a novel scheduling, DP-Scheduling, so that VCPU can be pinned or unpinned on one CPU¡¯s running queue dynamically. For this purpose, we first get the Machine Address (MA) of process descriptor from the angle of VMM. The affinity information is also acquired before the task is enabled to run. To acknowledge the affinity information, DP-Scheduling calls an API provided by us. Depending on the affinity information, we put forward a series of measures to implement pinning dynamically as well as to keep workload balance. All implementation is confined to Xen VMM and Credit scheduler. Our experiments demonstrate that DP-Scheduling outperforms Credit scheduling by testing various indicators for CPU-bound tasks, without interfering the load balance.","PeriodicalId":130987,"journal":{"name":"2010 IEEE Second International Conference on Cloud Computing Technology and Science","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122975362","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: 15

Resource Provisioning for Enriched Services in Cloud Environment 云环境下丰富业务的资源发放

2010 IEEE Second International Conference on Cloud Computing Technology and Science Pub Date : 2010-11-30 DOI: 10.1109/CloudCom.2010.43

R. Aoun, E. Doumith, M. Gagnaire

{"title":"Resource Provisioning for Enriched Services in Cloud Environment","authors":"R. Aoun, E. Doumith, M. Gagnaire","doi":"10.1109/CloudCom.2010.43","DOIUrl":"https://doi.org/10.1109/CloudCom.2010.43","url":null,"abstract":"Cloud services are based on the provisioning of computing, storage, and networking resources in order to satisfy requests generated by remote end-users. High speed Internet access and multi-core Virtual Machines (VMs) enable today the provisioning of diversified and enriched types of services in Cloud environment. In this paper, we consider several types of basic services and show how their orchestration may lead to the provisioning of more sophisticated services. For this purpose, we define four types of requests that cover the wide spectrum of possible services. We then formulate the resource provisioning problem as a Mixed Integer Linear Program (MILP). We assume that the underlying infrastructure is based on a set of end-to-end connections with guaranteed sustainable bandwidth such as Carrier-Grade Ethernet (CGE) circuits. We investigate the impact of two innovative services on resource allocation carried out by a Cloud Service Provider (CSP). These services correspond to distributed data storage and to multicast data transfer. For the former service, we consider the possibility of splitting a storage request onto different remote storage nodes. The latter service aims to distribute a same data sequence from one server towards multiple remote nodes assuming a limited number of network nodes have multicast capacities. These two innovative services provide a gain of 7% in terms of accepted requests when applied to the 18-node NSFnet backbone network.","PeriodicalId":130987,"journal":{"name":"2010 IEEE Second International Conference on Cloud Computing Technology and Science","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115949828","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: 47

A Mechanism of Flexible Memory Exchange in Cloud Computing Environments 云计算环境下灵活内存交换机制研究

2010 IEEE Second International Conference on Cloud Computing Technology and Science Pub Date : 2010-11-30 DOI: 10.1109/CloudCom.2010.56

T. Okuda, Eiji Kawai, S. Yamaguchi

引用次数: 6

LEEN: Locality/Fairness-Aware Key Partitioning for MapReduce in the Cloud LEEN:云中MapReduce的局部性/公平性感知键分区

2010 IEEE Second International Conference on Cloud Computing Technology and Science Pub Date : 2010-11-30 DOI: 10.1109/CloudCom.2010.25

Shadi Ibrahim, Hai Jin, Lu Lu, Song Wu, Bingsheng He, Li Qi

{"title":"LEEN: Locality/Fairness-Aware Key Partitioning for MapReduce in the Cloud","authors":"Shadi Ibrahim, Hai Jin, Lu Lu, Song Wu, Bingsheng He, Li Qi","doi":"10.1109/CloudCom.2010.25","DOIUrl":"https://doi.org/10.1109/CloudCom.2010.25","url":null,"abstract":"This paper investigates the problem of Partitioning Skew in MapReduce-based system. Our studies with Hadoop, a widely used MapReduce implementation, demonstrate that the presence of partitioning skew causes a huge amount of data transfer during the shuffle phase and leads to significant unfairness on the reduce input among different data nodes. As a result, the applications experience performance degradation due to the long data transfer during the shuffle phase along with the computation skew, particularly in reduce phase. We develop a novel algorithm named LEEN for locality-aware and fairness-aware key partitioning in MapReduce. LEEN embraces an asynchronous map and reduce scheme. All buffered intermediate keys are partitioned according to their frequencies and the fairness of the expected data distribution after the shuffle phase. We have integrated LEEN into Hadoop-0.18.0. Our experiments demonstrate that LEEN can efficiently achieve higher locality and reduce the amount of shuffled data. More importantly, LEEN guarantees fair distribution of the reduce inputs. As a result, LEEN achieves a performance improvement of up to 40% on different workloads.","PeriodicalId":130987,"journal":{"name":"2010 IEEE Second International Conference on Cloud Computing Technology and Science","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128537231","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: 197

A Framework for Evaluating Clustering Algorithm 评价聚类算法的框架

2010 IEEE Second International Conference on Cloud Computing Technology and Science Pub Date : 2010-11-30 DOI: 10.1109/CloudCom.2010.90

J. Nehinbe

引用次数: 5

Implementation and Performance Evaluation of a Hybrid Distributed System for Storing and Processing Images from the Web 一种用于存储和处理Web图像的混合分布式系统的实现和性能评估

2010 IEEE Second International Conference on Cloud Computing Technology and Science Pub Date : 2010-11-30 DOI: 10.1109/CloudCom.2010.116

M. Krishna, B. Kannan, Anand Ramani, S. Sathish

引用次数: 8

Resource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud 基于预算约束的云计算独立任务的资源分配

2010 IEEE Second International Conference on Cloud Computing Technology and Science Pub Date : 2010-11-30 DOI: 10.1109/CloudCom.2010.54

Weiming Shi, Bo Hong

{"title":"Resource Allocation with a Budget Constraint for Computing Independent Tasks in the Cloud","authors":"Weiming Shi, Bo Hong","doi":"10.1109/CloudCom.2010.54","DOIUrl":"https://doi.org/10.1109/CloudCom.2010.54","url":null,"abstract":"We consider the problem of running a large amount of independent equal-sized tasks in the cloud with a budget constraint. We model the cloud infrastructure by a node-weighted edge-weighted star-shaped graph which captures the different computing power and communication capacity of the computing resources in the cloud. Instead of trying to minimize the make span or the total-completion-time of the system, our study focuses on the maximization of the steady-state throughput of the system. We show that the specific budget-constrained steady-state throughput maximization problem can be formulated and solved as a linear programming problem. We identify two modes of the system, i.e., the budget-bound mode and the communication-bound mode where the closed-form solutions exist for the formulated problem. The best allocation scheme is benefit-first when the system is budget-bound, where the preference should be given to the nodes in the order of increasing cost, and is bandwidth-first when the system is communication-bound, where the preference should be given to compute nodes in the order of decreasing bandwidth.","PeriodicalId":130987,"journal":{"name":"2010 IEEE Second International Conference on Cloud Computing Technology and Science","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133181154","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: 33