2012 IEEE Fifth International Conference on Utility and Cloud Computing最新文献_第5页

A Coalitional Game-Based Mechanism for Forming Cloud Federations 基于联盟博弈的云联盟形成机制

2012 IEEE Fifth International Conference on Utility and Cloud Computing Pub Date : 2012-11-05 DOI: 10.1109/UCC.2012.19

Lena Mashayekhy, Daniel Grosu

引用次数: 35

A Session-Based Adaptive Admission Control Approach for Virtualized Application Servers 基于会话的虚拟化应用服务器自适应准入控制方法

2012 IEEE Fifth International Conference on Utility and Cloud Computing Pub Date : 2012-11-05 DOI: 10.1109/UCC.2012.22

A. Ashraf, Benjamin Byholm, Ivan Porres

引用次数: 29

CloudBay: Enabling an Online Resource Market Place for Open Clouds CloudBay:为开放云提供在线资源市场

2012 IEEE Fifth International Conference on Utility and Cloud Computing Pub Date : 2012-11-05 DOI: 10.1109/UCC.2012.40

Han Zhao, Ze Yu, Shiva Tiwari, Xing Mao, Kyungyong Lee, D. Wolinsky, Xiaolin Li, R. Figueiredo

{"title":"CloudBay: Enabling an Online Resource Market Place for Open Clouds","authors":"Han Zhao, Ze Yu, Shiva Tiwari, Xing Mao, Kyungyong Lee, D. Wolinsky, Xiaolin Li, R. Figueiredo","doi":"10.1109/UCC.2012.40","DOIUrl":"https://doi.org/10.1109/UCC.2012.40","url":null,"abstract":"This paper presents Cloud Bay, an online resource trading and leasing platform for multi-party resource sharing. Following a market-oriented design principle, Cloud Bay provides an abstraction of a shared virtual resource space across multiple administration domains, and features enhanced functionalities for scalable and automatic resource management and efficient service provisioning. Cloud Bay distinguishes itself from existing research and contributes in a number of aspects. First, it leverages scalable network virtualization and self-configurable virtual appliances to facilitate resource federation and parallel application deployment. Second, Cloud Bay adopts an eBay-style transaction model that supports differentiated services with different levels of job priorities. For cost-sensitive users, Cloud Bay implements an efficient matchmaking algorithm based on auction theory and enables opportunistic resource access through preemptive service scheduling. The proposed Cloud Bay platform stands between HPC service sellers and buyers, and offers a comprehensive solution for resource advertising and stitching, transaction management, and application-to-infrastructure mapping. In this paper, we present the design details of Cloud Bay, and discuss lessons and challenges encountered in the implementation process. The proof-of-concept prototype of Cloud Bay is justified through experiments across multiple sites and simulations.","PeriodicalId":122639,"journal":{"name":"2012 IEEE Fifth International Conference on Utility and Cloud Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126483259","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: 21

The Impact of Hypervisor Layer on Database Applications Hypervisor层对数据库应用程序的影响

2012 IEEE Fifth International Conference on Utility and Cloud Computing Pub Date : 2012-11-05 DOI: 10.1109/UCC.2012.28

D. Yokoyama, V. Oliveira, H. Kloh, Matheus Bandini, F. Porto, B. Schulze, A. Mury

引用次数: 7

Performance Modeling to Support Multi-tier Application Deployment to Infrastructure-as-a-Service Clouds 支持向基础设施即服务云部署多层应用程序的性能建模

2012 IEEE Fifth International Conference on Utility and Cloud Computing Pub Date : 2012-11-05 DOI: 10.1109/UCC.2012.20

W. Lloyd, S. Pallickara, O. David, J. Lyon, M. Arabi, K. Rojas

{"title":"Performance Modeling to Support Multi-tier Application Deployment to Infrastructure-as-a-Service Clouds","authors":"W. Lloyd, S. Pallickara, O. David, J. Lyon, M. Arabi, K. Rojas","doi":"10.1109/UCC.2012.20","DOIUrl":"https://doi.org/10.1109/UCC.2012.20","url":null,"abstract":"Infrastructure-as-a-service (IaaS) clouds support migration of multi-tier applications through virtualization of diverse application stack(s) of components which may require various operating systems and environments. To maximize performance of applications deployed to IaaS clouds while minimizing deployment costs, it is necessary to create virtual machine images to host application components with consideration for component dependencies that may affect load balancing of physical resources of VM hosts including CPU time, disk and network bandwidth. This paper presents results of an investigation utilizing physical machine (PM) and virtual machine (VM) resource utilization statistics to build performance models to predict application performance and rank performance of application component deployment configurations deployed across VMs. Our objective was to predict which component compositions provide best performance while requiring the fewest number of VMs. Eighteen individual resource utilization statistics were investigated for use as independent variables to predict service execution time using four different modeling approaches. Overall CPU time was the strongest predictor of execution time. The strength of individual predictors varied with respect to the resource utilization profiles of the applications. CPU statistics including idle time and number of context switches were good predictors when the test application was more disk I/O bound, while disk I/O statistics were better predictors when the application was more CPU bound. All performance models built were effective at determining the best performing service composition deployments validating the utility of our approach.","PeriodicalId":122639,"journal":{"name":"2012 IEEE Fifth International Conference on Utility and Cloud Computing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124607649","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

Cells: A Self-Hosting Virtual Infrastructure Service 单元:一种自托管虚拟基础设施服务

2012 IEEE Fifth International Conference on Utility and Cloud Computing Pub Date : 2012-11-05 DOI: 10.1109/UCC.2012.17

A. Coles, E. Deliot, Aled Edwards, A. Fischer, P. Goldsack, Julio Guijarro, R. Hawkes, Johannes Kirschnick, S. Loughran, Paul Murray, Lawrence Wilcock

引用次数: 4

MAPCloud: Mobile Applications on an Elastic and Scalable 2-Tier Cloud Architecture MAPCloud:基于弹性和可扩展的2层云架构的移动应用

2012 IEEE Fifth International Conference on Utility and Cloud Computing Pub Date : 2012-11-05 DOI: 10.1109/UCC.2012.25

M. Rahimi, N. Venkatasubramanian, S. Mehrotra, A. Vasilakos

{"title":"MAPCloud: Mobile Applications on an Elastic and Scalable 2-Tier Cloud Architecture","authors":"M. Rahimi, N. Venkatasubramanian, S. Mehrotra, A. Vasilakos","doi":"10.1109/UCC.2012.25","DOIUrl":"https://doi.org/10.1109/UCC.2012.25","url":null,"abstract":"The rise in popularity of mobile applications creates a growing demand to deliver richer functionality to users executing on mobile devices with limited resources. The availability of cloud computing platforms has made available unlimited and scalable resource pools of computation and storage that can be used to enhance service quality for mobile applications. This paper exploits the observation that using local resources in close proximity to the user, i.e. local clouds, can increase the quality and performance of mobile applications. In contrast, public cloud offerings (e.g. Amazon Web Services) offer scalability at the cost of higher delays, higher power consumption and higher price on the mobile device. In this paper we introduce MAP Cloud, a hybrid, tiered cloud architecture consisting of local and public clouds and show how it can be leveraged to increase both performance and scalability of mobile applications. We model the mobile application as a workflow of tasks and aim to optimally decompose the set of tasks to execute on the mobile client and 2-tier cloud architecture considering multiple QoS factors such as power, price, and delay. Such an optimization is shown to be NP-Hard, we propose an efficient simulated annealing based heuristic, called CRAM that is able to achieve about84% of optimal solutions when the number of users is high. We evaluate CRAM and the 2-tier approach via implementation(on Android G2 devices and Amazon EC2, S3 and Cloud Front)and extensive simulation using two rich mobile applications(Video-Content Augmented Reality and Image processing). Our results indicate that MAP Cloud provides improved scalability as compared to local clouds, improved efficiency (power/delay)(about 32% lower delays and power) and about 40% decrease in price in comparison to only using public cloud.","PeriodicalId":122639,"journal":{"name":"2012 IEEE Fifth International Conference on Utility and Cloud Computing","volume":"117 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131943053","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: 182

Context-Aware Job Scheduling for Cloud Computing Environments 面向云计算环境的上下文感知作业调度

2012 IEEE Fifth International Conference on Utility and Cloud Computing Pub Date : 2012-11-05 DOI: 10.1109/UCC.2012.33

M. Assunção, M. Netto, F. Koch, S. Bianchi

引用次数: 30

A Hybrid Scheduling Algorithm for Data Intensive Workloads in a MapReduce Environment MapReduce环境下数据密集型工作负载的混合调度算法

2012 IEEE Fifth International Conference on Utility and Cloud Computing Pub Date : 2012-11-01 DOI: 10.1109/UCC.2012.32

P. Nguyen, T. Simon, M. Halem, David Chapman, Quang-Trai Le

{"title":"A Hybrid Scheduling Algorithm for Data Intensive Workloads in a MapReduce Environment","authors":"P. Nguyen, T. Simon, M. Halem, David Chapman, Quang-Trai Le","doi":"10.1109/UCC.2012.32","DOIUrl":"https://doi.org/10.1109/UCC.2012.32","url":null,"abstract":"The specific choice of workload task schedulers for Hadoop MapReduce applications can have a dramatic effect on job workload latency. The Hadoop Fair Scheduler (FairS) assigns resources to jobs such that all jobs get, on average, an equal share of resources over time. Thus, it addresses the problem with a FIFO scheduler when short jobs have to wait for long running jobs to complete. We show that even for the FairS, jobs are still forced to wait significantly when the MapReduce system assigns equal sharing of resources due to dependencies between Map, Shuffle, Sort, Reduce phases. We propose a Hybrid Scheduler (HybS) algorithm based on dynamic priority in order to reduce the latency for variable length concurrent jobs, while maintaining data locality. The dynamic priorities can accommodate multiple task lengths, job sizes, and job waiting times by applying a greedy fractional knapsack algorithm for job task processor assignment. The estimated runtime of Map and Reduce tasks are provided to the HybS dynamic priorities from the historical Hadoop log files. In addition to dynamic priority, we implement a reordering of task processor assignment to account for data availability to automatically maintain the benefits of data locality in this environment. We evaluate our approach by running concurrent workloads consisting of the Word-count and Terasort benchmarks, and a satellite scientific data processing workload and developing a simulator. Our evaluation shows the HybS system improves the average response time for the workloads approximately 2.1x faster over the Hadoop FairS with a standard deviation of 1.4x.","PeriodicalId":122639,"journal":{"name":"2012 IEEE Fifth International Conference on Utility and Cloud Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124082639","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: 43

Biometric Evaluation on the Cloud: A Case Study with HumanID Gait Challenge 云上的生物识别评估:与人形步态挑战的案例研究

2012 IEEE Fifth International Conference on Utility and Cloud Computing Pub Date : 2012-11-01 DOI: 10.1109/UCC.2012.49

Ravi Panchumarthy, R. Subramanian, Sudeep Sarkar

{"title":"Biometric Evaluation on the Cloud: A Case Study with HumanID Gait Challenge","authors":"Ravi Panchumarthy, R. Subramanian, Sudeep Sarkar","doi":"10.1109/UCC.2012.49","DOIUrl":"https://doi.org/10.1109/UCC.2012.49","url":null,"abstract":"Biometric datasets are growing in size with time. Procurement of such datasets and resources for development and evaluation of biometric algorithms is expensive, time consuming and often requires expertise in systems software. Our goal in this project is to build a cloud-based evaluation system, which can host a common dataset and allow the submission of algorithms either as source code or Linux x-86 executable, to enforce a standard experimental protocol, and to provide results in a standard format. This facilitates comparing algorithms with each other and benchmarking progress. In order to efficiently service these algorithms, we need expensive computers with lot of storage and processing power. Having such systems eliminates the need for procurement of datasets and resources for experimentation, thus lowering the barrier for engaging in biometrics research. This style of cloud-based online evaluation system will encourage other biometric and research communities to build similar systems. The preferred solution to deploy this web-app is using Amazon Web Services, which provides computing power as well as storage capacity. In this paper, we share our experience with transitioning the HumanID Gait Challenge from traditional data+code type structure to a cloud based solution. It is available at http://marathon.csee.usf.edu/GaitBaseline/gaitcloud.","PeriodicalId":122639,"journal":{"name":"2012 IEEE Fifth International Conference on Utility and Cloud Computing","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123428127","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: 14