2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing最新文献_第7页

Optimal Footprint Symbiosis in Shared Cache 共享缓存中的最优内存占用共生

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.153

Xiaolin Wang, Yechen Li, Yingwei Luo, Xiameng Hu, Jacob Brock, C. Ding, Zhenlin Wang

引用次数: 15

Cloud-Based OLAP over Big Data: Application Scenarios and Performance Analysis 基于大数据的云OLAP:应用场景及性能分析

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.174

A. Cuzzocrea, Rim Moussa, Guandong Xu, G. Grasso

引用次数: 4

Highly Available Cloud-Based Cluster Management 基于云的高可用集群管理

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.125

Dmitry Duplyakin, Matthew Haney, H. Tufo

引用次数: 2

BigDataDIRAC: Deploying Distributed Big Data Applications BigDataDIRAC:部署分布式大数据应用

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.109

Víctor Fernández, V. Muñoz, T. F. Pena

{"title":"BigDataDIRAC: Deploying Distributed Big Data Applications","authors":"Víctor Fernández, V. Muñoz, T. F. Pena","doi":"10.1109/CCGrid.2015.109","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.109","url":null,"abstract":"The Distributed Infrastructure with Remote Agent Control (DIRAC) software framework allows a user community to manage computing activities in different grid and cloud environments. Many communities from several fields (LHCb, Belle II, Creatis, DIRAC4EGI multiple community portal, etc.) use DIRAC to run jobs in distributed environments. Google created the MapReduce programming model offering an efficient way of performing distributed computation over large data sets. Several enterprises are providing Hadoop cloud based resources to their users, and are trying to simplify the usage of Hadoop in the cloud. Based in these two robust technologies, we have created BigDataDIRAC, a solution which gives users the opportunity to access multiple Big Data resources scattered in different geographical areas, such as access to grid resources. This approach opens the possibility of offering not only grid and cloud to the users, but also Big Data resources from the same DIRAC environment. Proof of concept is shown using three computing centers in two countries, and with four Hadoop clusters. Our results demonstrate the ability of BigDataDIRAC to manage jobs driven by dataset location stored in the Hadoop File System (HDFS) of the Hadoop distributed clusters. DIRAC is used to monitor the execution, collect the necessary statistical data, and upload the results from the remote HDFS to the SandBox Storage machine. The tests produced the equivalent of 5 days continuous processing.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"26 1","pages":"1177-1180"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90259948","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

A Data Placement Strategy for Data-Intensive Scientific Workflows in Cloud 云中数据密集型科学工作流的数据放置策略

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.72

Qing Zhao, Congcong Xiong, Xi Zhao, Ce Yu, Jian Xiao

{"title":"A Data Placement Strategy for Data-Intensive Scientific Workflows in Cloud","authors":"Qing Zhao, Congcong Xiong, Xi Zhao, Ce Yu, Jian Xiao","doi":"10.1109/CCGrid.2015.72","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.72","url":null,"abstract":"With the arrival of cloud computing and Big Data, many scientific applications with large amount of data can be abstracted as scientific workflows and running on a cloud environment. Distributing these datasets intelligently can decrease data transfers efficiently during the workflow's execution. In this paper, we proposed a 2- stage data placement strategy. In the initial stage, we cluster the datasets based on their correlation, and allocate these clusters onto data centers. Compared with existing works, we have incorporated the data size into correlation calculation, and have proposed a new type of data correlation for the intermediate data named \"the first order conduction correlation\". Hence the data transmission cost can be measured more reasonable. In the runtime stage, the re-distribution algorithm can adjust data layout according to the changed factors, and the overhead of re-layout itself has also been measured. Compared with previous work, simulation results show that our proposed strategy can effectively reduce the time consumption of data movements during the workflow execution.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"11 1","pages":"928-934"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84290821","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

Log-Structured Global Array for Efficient Multi-Version Snapshots 高效多版本快照的日志结构全局数组

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.80

H. Fujita, N. Dun, Z. Rubenstein, A. Chien

引用次数: 8

A Parallel Algorithm for Clipping Polygons with Improved Bounds and a Distributed Overlay Processing System Using MPI 改进边界多边形裁剪并行算法及基于MPI的分布式叠加处理系统

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.43

S. Puri, S. Prasad

{"title":"A Parallel Algorithm for Clipping Polygons with Improved Bounds and a Distributed Overlay Processing System Using MPI","authors":"S. Puri, S. Prasad","doi":"10.1109/CCGrid.2015.43","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.43","url":null,"abstract":"Clipping arbitrary polygons is one of the complex operations in computer graphics and computational geometry. It is applied in many fields such as Geographic Information Systems (GIS) and VLSI CAD. We have two significant results to report. Our first result is the effective parallelization of the classic, highly sequential Greiner-Hormann algorithm, which yields the first output-sensitive CREW PRAM algorithm for a pair of simple polygons, and can perform clipping in O(logn) time using O(n+k) processors, where n is the total number of vertices and k is the number of edge intersections. This improves upon our previous clipping algorithm based on the parallelization of Vatti's sweepline algorithm, which requires O(n+k+k') processors to achieve logarithmic time complexity where k' can be O(n2). This also improves upon another O(logn) time algorithm by Karinthi, Srinivas, and Almasi which unlike our algorithm does not handle self-intersecting polygons, is not output-sensitive, and must employ O(n2) processors to achieve O(logn) time. We also study multi-core and many-core implementations of our parallel Greiner-Hormann algorithm. Our second result is a practical, parallel GIS system, namely MPI-GIS, for polygon overlay processing of two GIS layers containing large number of polygons over a cluster of compute nodes. It employs R-tree for efficient indexing and identification of potentially intersecting set of polygons across two input GIS layers. Spatial data files tend to be large in size (in GBs) and the underlying overlay computation is highly irregular and compute intensive. This system achieves 44X speedup on a 32-node NERSC's CARVER cluster while processing about 600K polygons in two GIS layers within 19 seconds which takes over 13 minutes on state-of-art ArcGIS system.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"138 1","pages":"576-585"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86591068","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: 35

A Priority-Based Scheduling Heuristic to Maximize Parallelism of Ready Tasks for DAG Applications 一种基于优先级的启发式调度方法以最大化DAG应用程序的就绪任务并行性

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.97

Wei Zheng, Lu Tang, R. Sakellariou

引用次数: 11

A Deep Learning Prediction Process Accelerator Based FPGA 基于FPGA的深度学习预测过程加速器

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.114

Qi Yu, Chao Wang, Xiang Ma, Xi Li, Xuehai Zhou

引用次数: 54

A Resource Allocation Model for Hybrid Storage Systems 混合存储系统的资源分配模型

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.132

Hui Wang, P. Varman

{"title":"A Resource Allocation Model for Hybrid Storage Systems","authors":"Hui Wang, P. Varman","doi":"10.1109/CCGrid.2015.132","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.132","url":null,"abstract":"Providing QoS guarantees for hybrid storage systems made up of both solid-state drives (SSDs) and hard disks (HDs) is a challenging problem. Since HDs and SSDs have widely different IOPS capacities, it is not sensible to treat the storage system as a monolithic black box, instead a useful QoS model must necessarily differentiate the IOs made to different device types. Traditional storage resource allocation models have largely been designed to provide QoS for a single resource type, and result in poor utilization and fairness when applied to multiple coupled resources. In this paper, we present a new resource allocation model for hybrid storage systems using a multi-resource framework. The model supports reservations and shares for clients sharing the storage system. Reservations specify the minimum throughput (IOPS) that a client must receive, while shares reflect its weight relative to other clients that are bottlenecked on the same device. We present a formal multi-resource allocation model to allocate IOPS to clients, together with an IO scheduling algorithm to maximize system throughput. The model and algorithms are validated with empirical results.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"108 1","pages":"91-100"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73349179","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: 7