2012 IEEE International Conference on Cluster Computing最新文献_第8页

Building a TaaS Platform for Web Service Load Testing 构建用于Web服务负载测试的TaaS平台

2012 IEEE International Conference on Cluster Computing Pub Date : 2012-09-24 DOI: 10.1109/CLUSTER.2012.20

Minzhi Yan, Hailong Sun, Xu Wang, Xudong Liu

{"title":"Building a TaaS Platform for Web Service Load Testing","authors":"Minzhi Yan, Hailong Sun, Xu Wang, Xudong Liu","doi":"10.1109/CLUSTER.2012.20","DOIUrl":"https://doi.org/10.1109/CLUSTER.2012.20","url":null,"abstract":"Web services are widely known as the building blocks of typical service oriented applications. The performance of such an application system is mainly dependent on that of component web services. Thus the effective load testing of web services is of great importance to understand and improve the performance of a service oriented system. However, existing Web Service load testing tools ignore the real characteristics of the practical running environment of a web service, which leads to inaccurate test results. In this work, we present WS-TaaS, a load testing platform for web services, which enables load testing process to be as close as possible to the real running scenarios. In this way, we aim at providing testers with more accurate performance testing results than existing tools. WS-TaaS is developed on the basis of our existing Cloud PaaS platform: Service4All. First, we provide detailed analysis of the requirements of Web Service load testing and present the conceptual architecture and design of key components. Then we present the implementation details of WS-TaaS on the basis of Service4All. Finally, we perform a set of experiments based on the testing of real web services, and the experiments illustrate that WS-TaaS can efficiently facilitate the whole process of Web Service load testing.","PeriodicalId":143579,"journal":{"name":"2012 IEEE International Conference on Cluster Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134502439","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: 30

Clover: A Distributed File System of Expandable Metadata Service Derived from HDFS Clover:源自HDFS的可扩展元数据服务的分布式文件系统

2012 IEEE International Conference on Cluster Computing Pub Date : 2012-09-24 DOI: 10.1109/CLUSTER.2012.54

Youwei Wang, Jiang Zhou, Can Ma, Weiping Wang, Dan Meng, Jason Kei

引用次数: 9

Affinity-aware Virtual Cluster Optimization for MapReduce Applications MapReduce应用的亲和性感知虚拟集群优化

2012 IEEE International Conference on Cluster Computing Pub Date : 2012-09-24 DOI: 10.1109/CLUSTER.2012.13

Cairong Yan, Ming Zhu, Xin Yang, Ze Yu, Min Li, Youqun Shi, Xiaolin Li

{"title":"Affinity-aware Virtual Cluster Optimization for MapReduce Applications","authors":"Cairong Yan, Ming Zhu, Xin Yang, Ze Yu, Min Li, Youqun Shi, Xiaolin Li","doi":"10.1109/CLUSTER.2012.13","DOIUrl":"https://doi.org/10.1109/CLUSTER.2012.13","url":null,"abstract":"Infrastructure-as-a-Service clouds are becoming ubiquitous for provisioning virtual machines on demand. Cloud service providers expect to use least resources to deliver best services. As users frequently request virtual machines to build virtual clusters and run MapReduce-like jobs for big data processing, cloud service providers intend to place virtual machines closely to minimize network latency and subsequently reduce data movement cost. In this paper we focus on the virtual machine placement issue for provisioning virtual clusters with minimum network latency in clouds. We define distance as the latency between virtual machines and use it to measure the affinity of virtual clusters. Such metric of distance indicates the considerations of virtual machine placement and topology of physical nodes in clouds. Then we formulate our problem as the classical shortest distance problem and solve it by modeling to integer programming problem. A greedy virtual machine placement algorithm is designed to get a compact virtual cluster. Furthermore, an improved heuristic algorithm is also presented for achieving a global resource optimization. The simulation results verify our algorithms and the experiment results validate the improvement achieved by our approaches.","PeriodicalId":143579,"journal":{"name":"2012 IEEE International Conference on Cluster Computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116990221","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: 12

Community Clustering for Distributed Publish/Subscribe Systems 分布式发布/订阅系统的社区集群

2012 IEEE International Conference on Cluster Computing Pub Date : 2012-09-01 DOI: 10.1109/CLUSTER.2012.67

Wei Li, Songlin Hu, Jintao Li, H. Jacobsen

引用次数: 10

Multicore-Enabled Smart Storage for Clusters 支持集群的多核智能存储

2012 IEEE International Conference on Cluster Computing Pub Date : 2012-09-01 DOI: 10.1109/CLUSTER.2012.70

Zhiyang Ding, Xunfei Jiang, Shu Yin, X. Qin, Kai-Hsiung Chang, X. Ruan, Mohammed I. Alghamdi, Meikang Qiu

{"title":"Multicore-Enabled Smart Storage for Clusters","authors":"Zhiyang Ding, Xunfei Jiang, Shu Yin, X. Qin, Kai-Hsiung Chang, X. Ruan, Mohammed I. Alghamdi, Meikang Qiu","doi":"10.1109/CLUSTER.2012.70","DOIUrl":"https://doi.org/10.1109/CLUSTER.2012.70","url":null,"abstract":"We present a multicore-enabled smart storage for clusters in general and MapReduce clusters in particular. The goal of this research is to improve performance of data-intensive parallel applications on clusters by offloading data processing to multicore processors in storage nodes. Compared with traditional storage devices, next-generation disks will have computing capability to reduce computational load of host processors or CPUs. With the advance of processor and memory technologies, smart storage systems are promising devices to perform complex on-disk operations. The proposed smart storage system can avoid moving a huge amount of data back and forth between storage nodes and computing nodes in a cluster. To enhance the performance of data-intensive applications, we have designed a smart storage system called Multicore-enabled Smart Storage (McSD), in which a multicore processor is integrated in storage nodes. We have implemented a programming framework for data-intensive applications running on a computing system coupled with McSD. The programming framework aims at balancing load between computing nodes and multicore-enabled smart storage nodes. To fully utilize multicore processors in smart storage nodes, we have implemented the MapReduce model for McSDs to handle parallel computing on a cluster. A prototype of McSD has been implemented in a cluster connected by Gigabit Ethernet. Experimental results show that McSD can significantly reduce the execution times of three real-world applications - word count, string matching, and matrix multiplication. We demonstrate that the integration of multicore-enabled smart storage with MapReduce clusters is a promising approach to improving overall performance of data-intensive applications on clusters.","PeriodicalId":143579,"journal":{"name":"2012 IEEE International Conference on Cluster Computing","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132924913","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: 3

SDM: A Stripe-Based Data Migration Scheme to Improve the Scalability of RAID-6 SDM:基于分条的数据迁移方案，提高RAID-6的可扩展性

2012 IEEE International Conference on Cluster Computing Pub Date : 2012-09-01 DOI: 10.1109/CLUSTER.2012.24

Chentao Wu, Xubin He, Jizhong Han, Huailiang Tan, C. Xie

{"title":"SDM: A Stripe-Based Data Migration Scheme to Improve the Scalability of RAID-6","authors":"Chentao Wu, Xubin He, Jizhong Han, Huailiang Tan, C. Xie","doi":"10.1109/CLUSTER.2012.24","DOIUrl":"https://doi.org/10.1109/CLUSTER.2012.24","url":null,"abstract":"In large scale data storage systems, RAID-6 has received more attention due to its capability to tolerate concurrent failures of any two disks, providing a higher level of reliability. However, a challenging issue is its scalability, or how to efficiently expand the disks. The main reason causing this problem is the typical fault tolerant scheme of most RAID-6 systems known as Maximum Distance Separable (MDS) codes, which offer data protection against disk failures with optimal storage efficiency but they are difficult to scale. To address this issue, we propose a novel Stripe-based Data Migration (SDM) scheme for large scale storage systems based on RAID-6 to achieve higher scalability. SDM is a stripe-level scheme, and the basic idea of SDM is optimizing data movements according to the future parity layout, which minimizes the overhead of data migration and parity modification. SDM scheme also provides uniform data distribution, fast data addressing and migration. We have conducted extensive mathematical analysis of applying SDM to various popular RAID-6 coding methods such as RDP, P-Code, H-Code, HDP, X-Code, and EVENODD. The results show that, compared to existing scaling approaches, SDM decreases more than 72.7% migration I/O operations and saves the migration time by up to 96.9%, which speeds up the scaling process by a factor of up to 32.","PeriodicalId":143579,"journal":{"name":"2012 IEEE International Conference on Cluster Computing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133877504","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: 32

A GPU-accelerated Branch-and-Bound Algorithm for the Flow-Shop Scheduling Problem 流车间调度问题的gpu加速分支定界算法

2012 IEEE International Conference on Cluster Computing Pub Date : 2012-08-20 DOI: 10.1109/CLUSTER.2012.18

N. Melab, Imen Chakroun, M. Mezmaz, D. Tuyttens

引用次数: 33