Proceedings of the Seventh ACM Symposium on Cloud Computing最新文献_第2页

Why Does the Cloud Stop Computing?: Lessons from Hundreds of Service Outages 为什么云会停止计算?:数百次服务中断的经验教训

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987583

Haryadi S. Gunawi, M. Hao, Riza O. Suminto, Agung Laksono, A. Satria, J. Adityatama, Kurnia J. Eliazar

引用次数: 184

TR-Spark: Transient Computing for Big Data Analytics TR-Spark:大数据分析的瞬态计算

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987576

Ying Yan, Yanjie Gao, Yang Chen, Zhongxin Guo, Bole Chen, T. Moscibroda

{"title":"TR-Spark: Transient Computing for Big Data Analytics","authors":"Ying Yan, Yanjie Gao, Yang Chen, Zhongxin Guo, Bole Chen, T. Moscibroda","doi":"10.1145/2987550.2987576","DOIUrl":"https://doi.org/10.1145/2987550.2987576","url":null,"abstract":"Large-scale public cloud providers invest billions of dollars into their cloud infrastructure and operate hundreds of thousands of servers across the globe. For various reasons, much of this provisioned server capacity runs at low average utilization, and there is tremendous competitive pressure to increase utilization. Conceptually, the way to increase utilization is clear: Run time-insensitive batch-job workloads as secondary background tasks whenever server capacity is underutilized; and evict these workloads when the server's primary task requires more resources. Big data analytic tasks would seem to be an ideal fit to run opportunistically on such transient resources in the cloud. In reality, however, modern distributed data processing systems such as MapReduce or Spark are designed to run as the primary task on dedicated hardware, and they perform badly on transiently available resources because of the excessive cost of cascading re-computations in case of evictions. In this paper, we propose a new framework for big data analytics on transient resources. Specifically, we design and implement TR-Spark, a version of Spark that can run highly efficiently as a secondary background task on transient (evictable) resources. The design of TR-Spark is based on two principles: resource stability and data size reduction-aware scheduling and lineage-aware checkpointing. The combination of these principles allows TR-Spark to naturally adapt to the stability characteristics of the underlying compute infrastructure. Evaluation results show that while regular Spark effectively fails to finish a job in clusters of even moderate instability, TR-Spark performs nearly as well as Spark running on stable resources.","PeriodicalId":362207,"journal":{"name":"Proceedings of the Seventh ACM Symposium on Cloud Computing","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124707180","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: 94

Optasia: A Relational Platform for Efficient Large-Scale Video Analytics Optasia:高效大规模视频分析的关系平台

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987564

Yao Lu, Aakanksha Chowdhery, Srikanth Kandula

引用次数: 80

CQSTR: Securing Cross-Tenant Applications with Cloud Containers CQSTR:使用云容器保护跨租户应用程序

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987558

Yan Zhai, Lichao Yin, J. Chase, T. Ristenpart, M. Swift

{"title":"CQSTR: Securing Cross-Tenant Applications with Cloud Containers","authors":"Yan Zhai, Lichao Yin, J. Chase, T. Ristenpart, M. Swift","doi":"10.1145/2987550.2987558","DOIUrl":"https://doi.org/10.1145/2987550.2987558","url":null,"abstract":"Cloud providers are in a position to greatly improve the trust clients have in network services: IaaS platforms can isolate services so they cannot leak data, and can help verify that they are securely deployed. We describe a new system called CQSTR that allows clients to verify a service's security properties. CQSTR provides a new cloud container abstraction similar to Linux containers but for VM clusters within IaaS clouds. Cloud containers enforce constraints on what software can run, and control where and how much data can be communicated across service boundaries. With CQSTR, IaaS providers can make assertions about the security properties of a service running in the cloud. We investigate implementations of CQSTR on both Amazon AWS and OpenStack. With AWS, we build on virtual private clouds to limit network access and on authorization mechanisms to limit storage access. However, with AWS certain security properties can be checked only by monitoring audit logs for violations after the fact. We modified OpenStack to implement the full CQSTR model with only modest code changes. We show how to use CQSTR to build more secure deployments of the data analytics frameworks PredictionIO, PacketPig, and SpamAssassin. In experiments on CloudLab we found that the performance impact of CQSTR on applications is near zero.","PeriodicalId":362207,"journal":{"name":"Proceedings of the Seventh ACM Symposium on Cloud Computing","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131966492","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: 20

RAMinate: Hypervisor-based Virtualization for Hybrid Main Memory Systems RAMinate:混合主存系统的基于管理程序的虚拟化

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987570

Takahiro Hirofuchi, Ryousei Takano

{"title":"RAMinate: Hypervisor-based Virtualization for Hybrid Main Memory Systems","authors":"Takahiro Hirofuchi, Ryousei Takano","doi":"10.1145/2987550.2987570","DOIUrl":"https://doi.org/10.1145/2987550.2987570","url":null,"abstract":"In the future, STT-MRAM will achieve larger capacity and comparable read/write performance, but incur orders of magnitude greater write energy than DRAM. To achieve large capacity as well as energy-efficiency, it is necessary to use both DRAM and STT-MRAM for the main memory of a computer. In this paper, we propose a hypervisor-based hybrid memory mechanism (RAMinate) that reduces write traffic to STT-MRAM by optimizing page locations between DRAM and STT-MRAM. In contrast to past studies, our mechanism works at the hypervisor level, not at the hardware or operating system level. It does not require any special program at the operating system level nor any design changes of the current memory controller at the hardware level. We developed a prototype of the proposed system by extending Qemu/KVM and conducted experiments with application benchmarks. We confirmed that our page replacement mechanism successfully worked for unmodified operating systems and dynamically diverted memory write traffic to DRAM. Our experiments also confirmed that our system successfully reduced write traffic to STT-MRAM by approximately 70% for tested workloads, which results in a 50% reduction in energy consumption in comparison to a DRAM-only system.","PeriodicalId":362207,"journal":{"name":"Proceedings of the Seventh ACM Symposium on Cloud Computing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133364966","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

Trading Timeliness and Accuracy in Geo-Distributed Streaming Analytics 地理分布流分析中的交易时效性和准确性

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987580

Benjamin Heintz, A. Chandra, R. Sitaraman

{"title":"Trading Timeliness and Accuracy in Geo-Distributed Streaming Analytics","authors":"Benjamin Heintz, A. Chandra, R. Sitaraman","doi":"10.1145/2987550.2987580","DOIUrl":"https://doi.org/10.1145/2987550.2987580","url":null,"abstract":"Many applications must ingest rapid data streams and produce analytics results in near-real-time. It is increasingly common for inputs to such applications to originate from geographically distributed sources. The typical infrastructure for processing such geo-distributed streams follows a hub-and-spoke model, where several edge servers perform partial computation before forwarding results over a wide-area network (WAN) to a central location for final processing. Due to limited WAN bandwidth, it is not always possible to produce exact results. In such cases, applications must either sacrifice timeliness by allowing delayed---i.e., stale---results, or sacrifice accuracy by allowing some error in final results. In this paper, we focus on windowed grouped aggregation, an important and widely used primitive in streaming analytics, and we study the tradeoff between staleness and error. We present optimal offline algorithms for minimizing staleness under an error constraint and for minimizing error under a staleness constraint. Using these offline algorithms as references, we present practical online algorithms for effectively trading off timeliness and accuracy under bandwidth limitations. Using a workload derived from an analytics service offered by a large commercial CDN, we demonstrate the effectiveness of our techniques through both trace-driven simulation as well as experiments on an Apache Storm-based implementation deployed on PlanetLab. Our experiments show that our proposed algorithms reduce staleness by 81.8% to 96.6%, and error by 83.4% to 99.1% compared to a practical random sampling/batching-based aggregation algorithm across a diverse set of aggregation functions.","PeriodicalId":362207,"journal":{"name":"Proceedings of the Seventh ACM Symposium on Cloud Computing","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126530238","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: 52

Addressing the straggler problem for iterative convergent parallel ML 求解迭代收敛并行机器学习中的离散问题

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987554

A. Harlap, Henggang Cui, Wei Dai, Jinliang Wei, G. Ganger, Phillip B. Gibbons, Garth A. Gibson, E. Xing

引用次数: 114

VNToR: Network Virtualization at the Top-of-Rack Switch vtor:机架顶交换机的网络虚拟化

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987582

J. Fietz, S. Whitlock, G. Ioannidis, K. Argyraki, Edouard Bugnion

引用次数: 2

Adaptive Caching in Big SQL using the HDFS Cache 大SQL中使用HDFS Cache的自适应缓存

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987553

A. Floratou, N. Megiddo, Navneet Potti, Fatma Özcan, Uday Kale, Jan Schmitz-Hermes

引用次数: 29

The Freeze-Frame File System 定格文件系统

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987578

Weijia Song, Theo Gkountouvas, K. Birman, Qi Chen, Zhen Xiao

引用次数: 20