2012 IEEE Fifth International Conference on Cloud Computing最新文献_第3页

WIQ: Work-Intensive Query Scheduling for In-Memory Database Systems WIQ:内存数据库系统的工作密集型查询调度

2012 IEEE Fifth International Conference on Cloud Computing Pub Date : 2012-06-24 DOI: 10.1109/CLOUD.2012.120

Stephan Kraft, G. Casale, Alin Jula, P. Kilpatrick, D. Greer

引用次数: 15

A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing 移动云计算中数据流应用程序的划分与执行框架

2012 IEEE Fifth International Conference on Cloud Computing Pub Date : 2012-06-24 DOI: 10.1145/2479942.2479946

Lei Yang, Jiannong Cao, Shaojie Tang, Tao Li, A. Chan

{"title":"A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing","authors":"Lei Yang, Jiannong Cao, Shaojie Tang, Tao Li, A. Chan","doi":"10.1145/2479942.2479946","DOIUrl":"https://doi.org/10.1145/2479942.2479946","url":null,"abstract":"The advances in technologies of cloud computing and mobile computing enable the newly emerging mobile cloud computing paradigm. Three approaches have been proposed for mobile cloud applications: 1) extending the access to cloud services to mobile devices; 2) enabling mobile devices to work collaboratively as cloud resource providers; 3) augmenting the execution of mobile applications on portable devices using cloud resources. In this paper, we focus on the third approach in supporting mobile data stream applications. More specifically, we study the computation partitioning, which aims at optimizing the partition of a data stream application between mobile and cloud such that the application has maximum speed/throughput in processing the streaming data. To the best of our knowledge, it is the first work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the make span of executions in other applications. We first propose a framework to provide runtime support for the dynamic partitioning and execution of the application. Different from existing works, the framework not only allows the dynamic partitioning for a single user but also supports the sharing of computation instances among multiple users in the cloud to achieve efficient utilization of the underlying cloud resources. Meanwhile, the framework has better scalability because it is designed on the elastic cloud fabrics. Based on the framework, we design a genetic algorithm to perform the optimal partition. We have conducted extensive simulations. The results show that our method can achieve more than 2X better performance over the execution without partitioning.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114380202","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: 429

Center-of-Gravity Reduce Task Scheduling to Lower MapReduce Network Traffic 重心减少任务调度，降低MapReduce网络流量

2012 IEEE Fifth International Conference on Cloud Computing Pub Date : 2012-06-24 DOI: 10.1109/CLOUD.2012.92

Mohammad Hammoud, M. S. Rehman, M. Sakr

{"title":"Center-of-Gravity Reduce Task Scheduling to Lower MapReduce Network Traffic","authors":"Mohammad Hammoud, M. S. Rehman, M. Sakr","doi":"10.1109/CLOUD.2012.92","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.92","url":null,"abstract":"MapReduce is by far one of the most successful realizations of large-scale data-intensive cloud computing platforms. MapReduce automatically parallelizes computation by running multiple map and/or reduce tasks over distributed data across multiple machines. Hadoop is an open source implementation of MapReduce. When Hadoop schedules reduce tasks, it neither exploits data locality nor addresses partitioning skew present in some MapReduce applications. This might lead to increased cluster network traffic. In this paper we investigate the problems of data locality and partitioning skew in Hadoop. We propose Center-of-Gravity Reduce Scheduler (CoGRS), a locality-aware skew-aware reduce task scheduler for saving MapReduce network traffic. In an attempt to exploit data locality, CoGRS schedules each reduce task at its center-of-gravity node, which is computed after considering partitioning skew as well. We implemented CoGRS in Hadoop-0.20.2 and tested it on a private cloud as well as on Amazon EC2. As compared to native Hadoop, our results show that CoGRS minimizes off-rack network traffic by averages of 9.6% and 38.6% on our private cloud and on an Amazon EC2 cluster, respectively. This reflects on job execution times and provides an improvement of up to 23.8%.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114688975","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: 99

MedBook: A Cloud-Based Healthcare Billing and Record Management System MedBook:基于云的医疗保健计费和记录管理系统

2012 IEEE Fifth International Conference on Cloud Computing Pub Date : 2012-06-24 DOI: 10.1109/CLOUD.2012.133

M. Rodríguez-Martínez, H. Valdivia, Jose Rivera, J. Seguel, Melvin Greer

引用次数: 19

Analysis of SaaS Business Platform Workloads for Sizing and Collocation SaaS业务平台工作负载的大小和配置分析

2012 IEEE Fifth International Conference on Cloud Computing Pub Date : 2012-06-24 DOI: 10.1109/CLOUD.2012.73

R. Ganesan, S. Sarkar, Akshay Narayan

{"title":"Analysis of SaaS Business Platform Workloads for Sizing and Collocation","authors":"R. Ganesan, S. Sarkar, Akshay Narayan","doi":"10.1109/CLOUD.2012.73","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.73","url":null,"abstract":"Sharing of physical infrastructure using virtualization presents an opportunity to improve the overall resource utilization. It is extremely important for a Software as a Service (SaaS) provider to understand the characteristics of the business application workload in order to size and place the virtual machine (VM) containing the application. A typical business application has a multi-tier architecture and the application workload is often predictable. Using the knowledge of the application architecture and statistical analysis of the workload, one can obtain an appropriate capacity and a good placement strategy for the corresponding VM. In this paper we propose a tool iCirrus-WoP that determines VM capacity and VM collocation possibilities for a given set of application workloads. We perform an empirical analysis of the approach on a set of business application workloads obtained from geographically distributed data centers. The iCirrus-WoP tool determines the fixed reserved capacity and a shared capacity of a VM which it can share with another collocated VM. Based on the workload variation, the tool determines if the VM should be statically allocated or needs a dynamic placement. To determine the collocation possibility, iCirrus-WoP performs a peak utilization analysis of the workloads. The empirical analysis reveals the possibility of collocating applications running in different time-zones. The VM capacity that the tool recommends, show a possibility of improving the overall utilization of the infrastructure by more than 70% if they are appropriately collocated.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"63 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126116907","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

Quantifying Manageability of Cloud Platforms 量化云平台的可管理性

2012 IEEE Fifth International Conference on Cloud Computing Pub Date : 2012-06-24 DOI: 10.1109/CLOUD.2012.111

Madhavi Maiya, Sai Dasari, Ravi Yadav, S. Shivaprasad, D. Milojicic

{"title":"Quantifying Manageability of Cloud Platforms","authors":"Madhavi Maiya, Sai Dasari, Ravi Yadav, S. Shivaprasad, D. Milojicic","doi":"10.1109/CLOUD.2012.111","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.111","url":null,"abstract":"Cloud computing has changed the way companies are managing their IT infrastructure and application development and/or deployment. While there are a number of Cloud platforms available, it is a challenge to identify a cloud platform that best suits the business needs. As a consequence Cloud service providers may end up selecting a platform that is either too low- or too high-level to manage, or does not offer the paradigms needed. In this paper, we introduce the Cloud manageability metrics and an approach to quantify the manageability of cloud platforms using the introduced metrics. We then use the metrics and approach to compare manageability of different cloud IaaS and PaaS platforms for specific use case scenarios. The values for the metrics are derived by executing the use cases in each platform using test environments. Based on the results of the comparison, we recommend a cloud platform that is best suited for different organizations needs with respect to cloud management. We expect that the proposed approach will help organizations to evaluate various cloud platforms and choose the platform that best matches their needs.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128369539","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: 8

Software Renting in the Era of Cloud Computing 云计算时代的软件租赁

2012 IEEE Fifth International Conference on Cloud Computing Pub Date : 2012-06-24 DOI: 10.1109/CLOUD.2012.71

Arto Ojala

引用次数: 14

XenPump: A New Method to Mitigate Timing Channel in Cloud Computing XenPump:一种缓解云计算中时序通道的新方法

2012 IEEE Fifth International Conference on Cloud Computing Pub Date : 2012-06-24 DOI: 10.1109/CLOUD.2012.28

Jingzheng Wu, Liping Ding, Yuqi Lin, N. Min-Allah, Yongji Wang

引用次数: 59

Facilitating Business-Oriented Cloud Transformation Decision with Cloud Transformation Advisor 使用云转换顾问促进面向业务的云转换决策

2012 IEEE Fifth International Conference on Cloud Computing Pub Date : 2012-06-24 DOI: 10.1109/CLOUD.2012.142

F. Meng, Jian Wang, Changhua Sun, Dongxu Duan, Yi-Min Chee

引用次数: 2

Application-Level CPU Consumption Estimation: Towards Performance Isolation of Multi-tenancy Web Applications 应用程序级CPU消耗估计:实现多租户Web应用程序的性能隔离

2012 IEEE Fifth International Conference on Cloud Computing Pub Date : 2012-06-24 DOI: 10.1109/CLOUD.2012.81

Wei Wang, Xiang Huang, Xiulei Qin, Wen-bo Zhang, Jun Wei, Hua Zhong

{"title":"Application-Level CPU Consumption Estimation: Towards Performance Isolation of Multi-tenancy Web Applications","authors":"Wei Wang, Xiang Huang, Xiulei Qin, Wen-bo Zhang, Jun Wei, Hua Zhong","doi":"10.1109/CLOUD.2012.81","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.81","url":null,"abstract":"Performance isolation is a key requirement for application-level multi-tenant sharing hosting environments. It requires knowledge of the resource consumption of the various tenants. It is of great importance not only to be aware of the resource consumption of a tenant's given kind of transaction mix, but also to be able to be aware of the resource consumption of a given transaction type. However, direct measurement of CPU resource consumption requires instrumentation and incurs overhead. Recently, regression analysis has been applied to indirectly approximate resource consumption, but challenges still remain for cases with non-determinism and multicollinearity. In this work, we adapts Kalman filter to estimate CPU consumptions from easily observed data. We also propose techniques to deal with the non-determinism and the multicollinearity issues. Experimental results show that estimation results are in agreement with the corresponding measurements with acceptable estimation errors, especially with appropriately tuned filter settings taken into account. Experiments also demonstrate the utility of the approach in avoiding performance interference and CPU overloading.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"411 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122822542","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: 71