Proceedings of the 4th annual Symposium on Cloud Computing最新文献

Small is better: avoiding latency traps in virtualized data centers 越小越好:避免虚拟化数据中心中的延迟陷阱

Proceedings of the 4th annual Symposium on Cloud Computing Pub Date : 2013-10-01 DOI: 10.1145/2523616.2523620

Yunjing Xu, Michael Bailey, Brian D. Noble, F. Jahanian

引用次数: 51

Hierarchical scheduling for diverse datacenter workloads 针对不同数据中心工作负载的分层调度

Proceedings of the 4th annual Symposium on Cloud Computing Pub Date : 2013-10-01 DOI: 10.1145/2523616.2523637

A. Bhattacharya, D. Culler, E. Friedman, A. Ghodsi, S. Shenker, I. Stoica

引用次数: 109

VMShadow: optimizing the performance of virtual desktops in distributed clouds VMShadow:优化分布式云环境下虚拟桌面的性能

Proceedings of the 4th annual Symposium on Cloud Computing Pub Date : 2013-10-01 DOI: 10.1145/2523616.2525950

Tian Guo, V. Gopalakrishnan, K. Ramakrishnan, P. Shenoy, A. Venkataramani, Seungjoon Lee

引用次数: 6

Proceedings of the 4th annual Symposium on Cloud Computing 第四届云计算年会论文集

Proceedings of the 4th annual Symposium on Cloud Computing Pub Date : 2013-10-01 DOI: 10.1145/2523616

G. Lohman

引用次数: 5

Introducing service-level awareness in the cloud 在云中引入服务级感知

Proceedings of the 4th annual Symposium on Cloud Computing Pub Date : 2013-10-01 DOI: 10.1145/2523616.2525936

C. Klein, M. Maggio, Karl-Erik Årzén, F. Hernández-Rodriguez

引用次数: 5

Comparing SSD-placement strategies to scale a database-in-the-cloud 比较ssd放置策略以扩展云中的数据库

Proceedings of the 4th annual Symposium on Cloud Computing Pub Date : 2013-10-01 DOI: 10.1145/2523616.2525949

Yingyi Bu, Hongrae Lee, J. Madhavan

引用次数: 3

When the network crumbles: an empirical study of cloud network failures and their impact on services 当网络崩溃:云网络故障及其对服务影响的实证研究

Proceedings of the 4th annual Symposium on Cloud Computing Pub Date : 2013-10-01 DOI: 10.1145/2523616.2523638

Rahul Potharaju, Navendu Jain

引用次数: 91

Scalable lineage capture for debugging DISC analytics 用于调试DISC分析的可扩展沿袭捕获

Proceedings of the 4th annual Symposium on Cloud Computing Pub Date : 2013-10-01 DOI: 10.1145/2523616.2523619

Dionysios Logothetis, Soumyarupa De, K. Yocum

{"title":"Scalable lineage capture for debugging DISC analytics","authors":"Dionysios Logothetis, Soumyarupa De, K. Yocum","doi":"10.1145/2523616.2523619","DOIUrl":"https://doi.org/10.1145/2523616.2523619","url":null,"abstract":"A fundamental challenge for big-data analytics is how to efficiently tune and debug multi-step dataflows. This paper presents Newt, a scalable architecture for capturing and using record-level data lineage to discover and resolve errors in analytics. Newt's flexible instrumentation allows system developers to collect this fine-grain lineage from a range of data intensive scalable computing (DISC) architectures, actively recording the flow of data through multi-step, user-defined transformations. Newt pairs this API with a scale-out, fault-tolerant lineage store and query engine. We find that while active collection can be expensive, it incurs modest runtime overheads for real-world analytics (<36%) and enables novel lineage-based debugging techniques. For instance, Newt can efficiently recreate errors (crashes or bad outputs) or remove input data from the dataflow to enable data cleaning strategies. Additionally, Newt's active lineage collection allows retro-spective analyses of a dataflow's behavior, such as identifying anomalous processing steps. As case studies, we instrument two DISC systems, Hadoop and Hyracks, with less than 105 lines of additional code for each. Finally, we use Newt to systematically clean input data to a Hadoop-based de novo genome assembler, improving the quality of the output assembly.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130901575","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

Scale-up vs scale-out for Hadoop: time to rethink? Hadoop的Scale-up vs . scale-out:是时候重新思考了?

Proceedings of the 4th annual Symposium on Cloud Computing Pub Date : 2013-10-01 DOI: 10.1145/2523616.2523629

Raja Appuswamy, C. Gkantsidis, D. Narayanan, O. Hodson, A. Rowstron

{"title":"Scale-up vs scale-out for Hadoop: time to rethink?","authors":"Raja Appuswamy, C. Gkantsidis, D. Narayanan, O. Hodson, A. Rowstron","doi":"10.1145/2523616.2523629","DOIUrl":"https://doi.org/10.1145/2523616.2523629","url":null,"abstract":"In the last decade we have seen a huge deployment of cheap clusters to run data analytics workloads. The conventional wisdom in industry and academia is that scaling out using a cluster of commodity machines is better for these workloads than scaling up by adding more resources to a single server. Popular analytics infrastructures such as Hadoop are aimed at such a cluster scale-out environment. Is this the right approach? Our measurements as well as other recent work shows that the majority of real-world analytic jobs process less than 100 GB of input, but popular infrastructures such as Hadoop/MapReduce were originally designed for petascale processing. We claim that a single \"scale-up\" server can process each of these jobs and do as well or better than a cluster in terms of performance, cost, power, and server density. We present an evaluation across 11 representative Hadoop jobs that shows scale-up to be competitive in all cases and significantly better in some cases, than scale-out. To achieve that performance, we describe several modifications to the Hadoop runtime that target scale-up configuration. These changes are transparent, do not require any changes to application code, and do not compromise scale-out performance; at the same time our evaluation shows that they do significantly improve Hadoop's scale-up performance.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133702376","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: 178

PoWER: prediction of workload for energy efficient relocation of virtual machines PoWER:预测工作负载以实现高效的虚拟机重新定位

Proceedings of the 4th annual Symposium on Cloud Computing Pub Date : 2013-10-01 DOI: 10.1145/2523616.2525938

Kashifuddin Qazi, Y. Li, A. Sohn

{"title":"PoWER: prediction of workload for energy efficient relocation of virtual machines","authors":"Kashifuddin Qazi, Y. Li, A. Sohn","doi":"10.1145/2523616.2525938","DOIUrl":"https://doi.org/10.1145/2523616.2525938","url":null,"abstract":"Virtual Machines (VM) offer data center owners the option to lease computational resources like CPU cycles, Memory, Disk space and Network bandwidth to end-users. An important consideration in this scenario is the optimal usage of the resources (CPU cycles, Memory, Block I/O and Network Bandwidth) of the physical machines that make up the cloud or 'machine-farms'. At any given time, the machines should not be overloaded (to ensure certain QoS requirements are met) and at the same time a minimum number of machines should be running (to conserve energy). The loads on individual VMs residing on these machines is, in fact, not absolutely random. Certain patterns can be found that can help the data center owners arrange the VMs on the physical machines such that both of the above conditions are met (minimum number of machines running without any being overloaded). In this work we propose a framework, PoWER that tries to intelligently predict the behavior of the cluster based on its history and then accordingly distributes VMs in the cluster and turns off unused Physical Machines, thus saving energy. Central to our framework are concepts of Chaos Theory that make our framework indifferent to the type of loads and inherent cycles in them as opposed to other current prediction algorithms. We also test this framework on our testbed cluster and analyze its performance. We demonstrate that PoWER performs better than another FFT-based time series method in predicting VM loads and freeing resources on Physical Machines for our test loads.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115818806","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