2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing最新文献_第2页

DELMA: Dynamically ELastic MapReduce Framework for CPU-Intensive Applications DELMA: cpu密集型应用的动态弹性MapReduce框架

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.71

Zacharia Fadika, M. Govindaraju

{"title":"DELMA: Dynamically ELastic MapReduce Framework for CPU-Intensive Applications","authors":"Zacharia Fadika, M. Govindaraju","doi":"10.1109/CCGrid.2011.71","DOIUrl":"https://doi.org/10.1109/CCGrid.2011.71","url":null,"abstract":"Since its introduction, MapReduce implementations have been primarily focused towards static compute cluster sizes. In this paper, we introduce the concept of dynamic elasticity to MapReduce. We present the design decisions and implementation tradeoffs for DELMA, (Dynamically Elastic MapReduce), a framework that follows the MapReduce paradigm, just like Hadoop MapReduce, but that is capable of growing and shrinking its cluster size, as jobs are underway. In our study, we test DELMA in diverse performance scenarios, ranging from diverse node additions to node additions at various points in the application run-time with various dataset sizes. The applicability of the MapReduce paradigm extends far beyond its use with large-scale data intensive applications, and can also be brought to bear in processing long running distributed applications executing on small-sized clusters. In this work, we focus both on the performance of processing hierarchical data in distributed scientific applications, as well as the processing of smaller but demanding input sizes primarily used in small clusters. We run experiments for datasets that require CPU intensive processing, ranging in size from Millions of input data elements to process, up to over half a billion elements, and observe the positive scalability patterns exhibited by the system. We show that for such sizes, performance increases accordingly with data and cluster size increases. We conclude on the benefits of providing MapReduce with the capability of dynamically growing and shrinking its cluster configuration by adding and removing nodes during jobs, and explain the possibilities presented by this model.","PeriodicalId":376385,"journal":{"name":"2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125598257","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: 45

A Trustworthiness Fusion Model for Service Cloud Platform Based on D-S Evidence Theory 基于D-S证据理论的服务云平台可信度融合模型

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.31

Rong Hu, Jianxun Liu, Xiaoqing Frank Liu

引用次数: 7

Evaluating and Optimizing Indexing Schemes for a Cloud-Based Elastic Key-Value Store 基于云的弹性键值存储索引方案的评价与优化

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.29

David Chiu, Apeksha Shetty, G. Agrawal

{"title":"Evaluating and Optimizing Indexing Schemes for a Cloud-Based Elastic Key-Value Store","authors":"David Chiu, Apeksha Shetty, G. Agrawal","doi":"10.1109/CCGrid.2011.29","DOIUrl":"https://doi.org/10.1109/CCGrid.2011.29","url":null,"abstract":"Cloud computing has emerged to provide virtual, pay-as-you-go computing and storage services over the Internet, where the usage cost directly depends on consumption. One compelling feature in Clouds is elasticity, where a user can demand, and be immediately given access to, more (or less) resources based on requirements. However, this feature introduces new challenges in developing application and services. In this paper, we focus on the challenges in data management in Cloud environments, in view of elasticity. Particularly, we consider an elastic key-value store, which is used to cache intermediate results in a service-oriented system, and accelerate future queries by reusing the stored values. Such a key-value store can clearly benefit from the elasticity offered by Clouds, by expanding the cache during query-intensive periods. However, supporting an elastic key-value store involves many challenges, including selecting an appropriate indexing scheme, data migration upon elastic resource provisioning, and optimizations to remove certain overheads in the Cloud. This paper focuses on the design of an elastic key-value store. We consider three ubiquitous methods for indexing: B+-Trees, Extendible Hashing, and Bloom Filters, and we show how these schemes can be modified to exploit elasticity in Clouds. We also evaluate various performance aspects associated with the use of these indexing schemes. Furthermore, we have developed a heuristic to request elastic compute resources for expanding the cache such that instance startup overheads are minimized in our scheme. Our evaluation studies show that the index selection depends on various application and system level parameters that we have identified. And while we confirm that B+-Trees, which pervade many of today's key-value systems, would scale well, we showcases when Extendible Hashing would outperform B+-Trees.","PeriodicalId":376385,"journal":{"name":"2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129900039","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

Techniques for Fine-Grained, Multi-site Computation Offloading 细粒度、多站点计算卸载技术

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.69

Kanad Sinha, Milind Kulkarni

{"title":"Techniques for Fine-Grained, Multi-site Computation Offloading","authors":"Kanad Sinha, Milind Kulkarni","doi":"10.1109/CCGrid.2011.69","DOIUrl":"https://doi.org/10.1109/CCGrid.2011.69","url":null,"abstract":"Increasingly, mobile devices are becoming the preferred platform for computation for many users. Unfortunately, the resource limitations, in battery life, computation power and storage, restricts the richness of applications that can be run on such devices. To alleviate these concerns, a popular approach that has gained currency in recent years is {em computation offloading}, where a portion of an application is run off-site, leveraging the far greater resources of the cloud. Prior work in this area has focused on a constrained form of the problem: a single mobile device offloading computation to a single server. However, with the increased popularity of cloud computing and storage, it is more common for the data that an application accesses to be distributed among several servers. This paper describes algorithmic approaches for performing fine-grained, multi-site offloading. This allows portions of an application to be offloaded in a data-centric manner, even if that data exists at multiple sites. Our approach is based on a novel partitioning algorithm, and a novel data representation. We demonstrate that our partitioning algorithm outperforms existing multi-site offloading algorithms, and that our data representation provides for more efficient, fine-grained offloading than prior approaches.","PeriodicalId":376385,"journal":{"name":"2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129113233","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: 64

Classification and Composition of QoS Attributes in Distributed, Heterogeneous Systems 分布式异构系统中QoS属性的分类和组成

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.53

E. Vinek, P. Beran, E. Schikuta

引用次数: 24

Cheetah: A Framework for Scalable Hierarchical Collective Operations Cheetah:一个可扩展的分层集体操作框架

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.42

R. Graham, Manjunath Gorentla Venkata, Joshua Ladd, Pavel Shamis, Ishai Rabinovitz, Vasily Filipov, G. Shainer

{"title":"Cheetah: A Framework for Scalable Hierarchical Collective Operations","authors":"R. Graham, Manjunath Gorentla Venkata, Joshua Ladd, Pavel Shamis, Ishai Rabinovitz, Vasily Filipov, G. Shainer","doi":"10.1109/CCGrid.2011.42","DOIUrl":"https://doi.org/10.1109/CCGrid.2011.42","url":null,"abstract":"Collective communication operations, used by many scientific applications, tend to limit overall parallel application performance and scalability. Computer systems are becoming more heterogeneous with increasing node and core-per-node counts. Also, a growing number of data-access mechanisms, of varying characteristics, are supported within a single computer system. We describe a new hierarchical collective communication framework that takes advantage of hardware-specific data-access mechanisms. It is flexible, with run-time hierarchy specification, and sharing of collective communication primitives between collective algorithms. Data buffers are shared between levels in the hierarchy reducing collective communication management overhead. We have implemented several versions of the Message Passing Interface (MPI) collective operations, MPI Barrier() and MPI Bcast(), and run experiments using up to 49, 152 processes on a Cray XT5, and a small InfiniBand based cluster. At 49, 152 processes our barrier implementation outperforms the optimized native implementation by 75%. 32 Byte and one Mega-Byte broadcasts outperform it by 62% and 11%, respectively, with better scalability characteristics. Improvements relative to the default Open MPI implementation are much larger.","PeriodicalId":376385,"journal":{"name":"2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134333357","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

Addressing Resource Fragmentation in Grids through Network-Aware Meta-scheduling in Advance 基于网络感知的元调度方法解决网格资源碎片问题

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.75

Luis Tomás, C. Carrión, María Blanca Caminero, A. Caminero

引用次数: 1

Towards Reliable, Performant Workflows for Streaming-Applications on Cloud Platforms 为云平台上的流应用程序提供可靠、高性能的工作流

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.74

Daniel Zinn, Q. Hart, T. McPhillips, Bertram Ludäscher, Yogesh L. Simmhan, Michail Giakkoupis, V. Prasanna

引用次数: 31

Adaptive QoS Decomposition and Control for Storage Cache Management in Multi-server Environments 多服务器环境下存储缓存管理的自适应QoS分解与控制

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.37

R. Prabhakar, Shekhar Srikantaiah, R. Garg, M. Kandemir

{"title":"Adaptive QoS Decomposition and Control for Storage Cache Management in Multi-server Environments","authors":"R. Prabhakar, Shekhar Srikantaiah, R. Garg, M. Kandemir","doi":"10.1109/CCGrid.2011.37","DOIUrl":"https://doi.org/10.1109/CCGrid.2011.37","url":null,"abstract":"Poor I/O performance can prevent an application from scaling to a large number of nodes even if the computation is parallelized appropriately. Therefore, improving I/O performance of large-scale parallel applications is very important. Caching recently and frequently accessed I/O blocks in memory is a widely used technique for improving I/O performance of these applications on high-end machines. However, simultaneous storage cache accesses of multiple applications may lead to unacceptable degradations in application performance due to interferences at the storage cache layer. As a result, efficient management of storage cache space across multiple I/O servers among competing applications is critical in order to ensure performance quality of service (QoS) to individual applications. In this paper, we propose a novel two-step approach to the management of the storage caches to provide predictable performance in multi-server storage architectures: (1)An adaptive QoS decomposition and optimization step uses max-flow algorithm to determine the best decomposition of application-level QoS to sub-QoSs such that the application performance is optimized, and (2) A storage cache allocation step uses feedback control theory to allocates hared storage cache space such that the specified QoSs are satisfied throughout the execution. Our experimental evaluation indicates that, on an average, our approach improves the I/Othroughput of applications by 48.6%, 29.2%, and 20.7%, respectively, over the uncontrolled partitioning, fair share and uniform decomposition schemes. We also observed 31.4%, 20.2%, and 44.7% improvements by our approach, in our global metric, called the fair speedup metric, against the fair share, uncontrolled partitioning and uniform decomposition schemes, respectively.","PeriodicalId":376385,"journal":{"name":"2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133838021","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

SLA-Based Resource Allocation for Software as a Service Provider (SaaS) in Cloud Computing Environments 云计算环境下基于sla的SaaS资源分配

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.51

Linlin Wu, S. Garg, R. Buyya

{"title":"SLA-Based Resource Allocation for Software as a Service Provider (SaaS) in Cloud Computing Environments","authors":"Linlin Wu, S. Garg, R. Buyya","doi":"10.1109/CCGrid.2011.51","DOIUrl":"https://doi.org/10.1109/CCGrid.2011.51","url":null,"abstract":"Cloud computing has been considered as a solution for solving enterprise application distribution and configuration challenges in the traditional software sales model. Migrating from traditional software to Cloud enables on-going revenue for software providers. However, in order to deliver hosted services to customers, SaaS companies have to either maintain their own hardware or rent it from infrastructure providers. This requirement means that SaaS providers will incur extra costs. In order to minimize the cost of resources, it is also important to satisfy a minimum service level to customers. Therefore, this paper proposes resource allocation algorithms for SaaS providers who want to minimize infrastructure cost and SLA violations. Our proposed algorithms are designed in a way to ensure that Saas providers are able to manage the dynamic change of customers, mapping customer requests to infrastructure level parameters and handling heterogeneity of Virtual Machines. We take into account the customers' Quality of Service parameters such as response time, and infrastructure level parameters such as service initiation time. This paper also presents an extensive evaluation study to analyze and demonstrate that our proposed algorithms minimize the SaaS provider's cost and the number of SLA violations in a dynamic resource sharing Cloud environment.","PeriodicalId":376385,"journal":{"name":"2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"368 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115277439","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: 393