Proceedings of the Seventh ACM Symposium on Cloud Computing最新文献_第3页

Grandet: A Unified, Economical Object Store for Web Applications 用于Web应用程序的统一的、经济的对象存储

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987562

Yang Tang, Gang Hu, Xinhao Yuan, Lingmei Weng, Junfeng Yang

引用次数: 6

PerfOrator: eloquent performance models for Resource Optimization 穿孔器:资源优化的雄辩性能模型

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987566

K. Rajan, Dharmesh Kakadia, C. Curino, Subru Krishnan

{"title":"PerfOrator: eloquent performance models for Resource Optimization","authors":"K. Rajan, Dharmesh Kakadia, C. Curino, Subru Krishnan","doi":"10.1145/2987550.2987566","DOIUrl":"https://doi.org/10.1145/2987550.2987566","url":null,"abstract":"Query Optimization focuses on finding the best query execution plan, given fixed hardware resources. In BigData settings, both pay-as-you-go clouds and on-prem shared clusters, a complementary challenge emerges: Resource Optimization: find the best hardware resources, given an execution plan. In this world, provisioning is almost instantaneous and time-varying resources can be acquired on a per-query basis. This allows us to optimize allocations for completion time, resource usage, dollar cost, etc. These optimizations have a huge impact on performance and cost, and pivot around a core challenge: faithful resource-to-performance models for arbitrary BigData queries. This task is challenging for users and tools alike due to lack of good statistics (high-velocity, unstructured data), frequent use of UDFs, impact on performance of different hardware types and a lack of understanding of parallel execution at such a scale. We address this with PerfOrator, a novel approach to resource-to-performance modeling. PerfOrator employs nonlinear regression on profile runs to model arbitrary UDFs, calibration queries to generalize across hardware platforms, and analytical framework models to account for parallelism. The resulting estimates are orders of magnitude more accurate than existing approaches (e.g, Hive's optimizer), and have been successfully employed in two resource optimization scenarios: 1) optimize provisioning of clusters in cloud settings---with decisions within 1% of optimal, 2) reserve skyline of resources for SLA jobs---with accuracies over 10x better than human experts.","PeriodicalId":362207,"journal":{"name":"Proceedings of the Seventh ACM Symposium on Cloud Computing","volume":"8 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":"121071728","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

Towards Weakly Consistent Local Storage Systems 弱一致性本地存储系统

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987579

Ji-Yong Shin, M. Balakrishnan, Tudor Marian, Jakub Szefer, Hakim Weatherspoon

{"title":"Towards Weakly Consistent Local Storage Systems","authors":"Ji-Yong Shin, M. Balakrishnan, Tudor Marian, Jakub Szefer, Hakim Weatherspoon","doi":"10.1145/2987550.2987579","DOIUrl":"https://doi.org/10.1145/2987550.2987579","url":null,"abstract":"Heterogeneity is a fact of life for modern storage servers. For example, a server may spread terabytes of data across many different storage media, ranging from magnetic disks, DRAM, NAND-based solid state drives (SSDs), as well as hybrid drives that package various combinations of these technologies. It follows that access latencies to data can vary hugely depending on which media the data resides on. At the same time, modern storage systems naturally retain older versions of data due to the prevalence of log-structured designs and caches in software and hardware layers. In a sense, a contemporary storage system is very similar to a small-scale distributed system, opening the door to consistency/performance trade-offs. In this paper, we propose a class of local storage systems called StaleStores that support relaxed consistency, returning stale data for better performance. We describe several examples of StaleStores, and show via emulations that serving stale data can improve access latency by between 35% and 20X. We describe a particular StaleStore called Yogurt, a weakly consistent local block storage system. Depending on the application's consistency requirements (e.g. bounded staleness, mono-tonic reads, read-my-writes, etc.), Yogurt queries the access costs for different versions of data within tolerable staleness bounds and returns the fastest version. We show that a distributed key-value store running on top of Yogurt obtains a 6X speed-up for access latency by trading off consistency and performance within individual storage servers.","PeriodicalId":362207,"journal":{"name":"Proceedings of the Seventh ACM Symposium on Cloud Computing","volume":"66 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":"115668791","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

Principled workflow-centric tracing of distributed systems 分布式系统有原则的以工作流为中心的跟踪

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987568

Raja R. Sambasivan, Ilari Shafer, Jonathan Mace, B. Sigelman, Rodrigo Fonseca, G. Ganger

引用次数: 59

A Case for Virtualizing Persistent Memory 虚拟化持久内存的案例

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987551

Liang Liang, Rong Chen, Haibo Chen, Yubin Xia, KwanJong Park, B. Zang, Haibing Guan

{"title":"A Case for Virtualizing Persistent Memory","authors":"Liang Liang, Rong Chen, Haibo Chen, Yubin Xia, KwanJong Park, B. Zang, Haibing Guan","doi":"10.1145/2987550.2987551","DOIUrl":"https://doi.org/10.1145/2987550.2987551","url":null,"abstract":"With the proliferation of software and hardware support for persistent memory (PM) like PCM and NV-DIMM, we envision that PM will soon become a standard component of commodity cloud, especially for those applications demanding high performance and low latency. Yet, current virtualization software lacks support to efficiently virtualize and manage PM to improve cost-effectiveness, performance, and endurance. In this paper, we make the first case study on extending commodity hypervisors to virtualize PM. We explore design spaces to abstract PM, including load/store accessible guest-physical memory and a block device. We design and implement a system, namely VPM, which provides both full-virtualization as well as a para-virtualization interface that provide persistence hints to the hypervisor. By leveraging the fact that PM has similar characteristics with DRAM except for persistence, VPM supports transparent data migration by leveraging the two-dimensional paging (e.g., EPT) to adjust the mapping between guest PM to host physical memory (DRAM or PM). Finally, VPM provides efficient crash recovery by properly bookkeeping guest PM's states as well as key hypervisor-based states into PM in an epoch-based consistency approach. Experimental results with VPM implemented on KVM and Linux using simulated PCM and NVDIMM show that VPM achieves a proportional consolidation of PM with graceful degradation of performance. Our para-virtualized interface further improves the consolidation ratio with less overhead for some workloads.","PeriodicalId":362207,"journal":{"name":"Proceedings of the Seventh ACM Symposium on Cloud Computing","volume":"13 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":"129676118","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: 5

Privacy Preserving Collaboration in Bring-Your-Own-Apps 在自带应用程序中保护隐私

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987587

Sangmin Lee, Deepak Goel, Edmund L. Wong, Asim Kadav, M. Dahlin

{"title":"Privacy Preserving Collaboration in Bring-Your-Own-Apps","authors":"Sangmin Lee, Deepak Goel, Edmund L. Wong, Asim Kadav, M. Dahlin","doi":"10.1145/2987550.2987587","DOIUrl":"https://doi.org/10.1145/2987550.2987587","url":null,"abstract":"Enterprise environments limit personal device usage for corporate data within a small set of enterprise provided apps or by using a whitelist of third-party apps. Both these options provide employees with limited app features, and a whitelist can be cumbersome to manage. In this paper we present CleanRoom, a new app platform designed to protect confidentiality in a brave \"Bring Your Own Apps\" (BYOA) world where employees use their own untrusted third-party apps to create, edit, and share corporate data. CleanRoom's core guarantee is privacy-preserving collaboration: CleanRoom enables employees to work together on shared data while ensuring that the owners of the data---not the app accessing the data---control who can access and collaborate using this data. CleanRoom provides fine-grained data object sandboxes and uses platform level access control to preserve privacy. We show that CleanRoom prevents a faulty or malicious app from leaking any data to unauthorized users or the app's publisher. CleanRoom accommodates a broad range of apps, preserves the confidentiality of the data that these apps access, and incurs low overhead. Furthermore, CleanRoom supports a novel privacy-preserving error reporting through a combination of differential privacy and static program analysis.","PeriodicalId":362207,"journal":{"name":"Proceedings of the Seventh ACM Symposium on Cloud Computing","volume":"1 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":"129059732","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: 4

PipeGen

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987567

Brandon Haynes, Alvin Cheung, M. Balazinska

引用次数: 48

HIL: Designing an Exokernel for the Data Center HIL:为数据中心设计exokkernel

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987588

Jason Hennessey, Sahil Tikale, Ata Turk, E. Kaynar, Chris Hill, Peter Desnoyers, O. Krieger

引用次数: 10

Randomized Algorithms for Dynamic Storage Load-Balancing 动态存储负载均衡的随机化算法

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987572

Liang Liu, L. Fortnow, Jin Li, Yating Wang, Jun Xu

{"title":"Randomized Algorithms for Dynamic Storage Load-Balancing","authors":"Liang Liu, L. Fortnow, Jin Li, Yating Wang, Jun Xu","doi":"10.1145/2987550.2987572","DOIUrl":"https://doi.org/10.1145/2987550.2987572","url":null,"abstract":"In this work, we study a challenging research problem that arises in minimizing the cost of storing customer data online for reliable access in a cloud. It is how to near-perfectly balance the remaining capacities of all disks across the cloud system while adding new file blocks so that the inevitable event of capacity expansion can be postponed as much as possible. The challenges of solving this problem are twofold. First, new file blocks are added to the cloud concurrently by many dispatchers (computing servers) that have no communication or coordination among themselves. Though each dispatcher is updated with information on disk occupancies, the update is infrequent and not synchronized. Second, for fault-tolerance purposes, a combinatorial constraint has to be satisfied in distributing the blocks of each new file across the cloud system. We propose a randomized algorithm, in which each dispatcher independently samples a blocks-to-disks assignment according to a probability distribution on a set of assignments conforming to the aforementioned combinatorial requirement. We show that this algorithm allows a cloud system to near-perfectly balance the remaining disk capacities as rapidly as theoretically possible, when starting from any unbalanced state that is correctable mathematically.","PeriodicalId":362207,"journal":{"name":"Proceedings of the Seventh ACM Symposium on Cloud Computing","volume":"27 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":"125608539","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

ReStream: Accelerating Backtesting and Stream Replay with Serial-Equivalent Parallel Processing ReStream:加速回溯测试和流回放与串行等效并行处理

Proceedings of the Seventh ACM Symposium on Cloud Computing Pub Date : 2016-10-05 DOI: 10.1145/2987550.2987573

Johann Schleier-Smith, Erik T. Krogen, J. Hellerstein

{"title":"ReStream: Accelerating Backtesting and Stream Replay with Serial-Equivalent Parallel Processing","authors":"Johann Schleier-Smith, Erik T. Krogen, J. Hellerstein","doi":"10.1145/2987550.2987573","DOIUrl":"https://doi.org/10.1145/2987550.2987573","url":null,"abstract":"Real-time predictive applications can demand continuous and agile development, with new models constantly being trained, tested, and then deployed. Training and testing are done by replaying stored event logs, running new models in the context of historical data in a form of backtesting or \"what if?\" analysis. To replay weeks or months of logs while developers wait, we need systems that can stream event logs through prediction logic many times faster than the real-time rate. A challenge with high-speed replay is preserving sequential semantics while harnessing parallel processing power. The crux of the problem lies with causal dependencies inherent in the sequential semantics of log replay. We introduce an execution engine that produces serial-equivalent output while accelerating throughput with pipelining and distributed parallelism. This is made possible by optimizing for high throughput rather than the traditional stream processing goal of low latency, and by aggressive sharing of versioned state, a technique we term Multi-Versioned Parallel Streaming (MVPS). In experiments we see that this engine, which we call ReStream, performs as well as batch processing and more than an order of magnitude better than a single-threaded implementation.","PeriodicalId":362207,"journal":{"name":"Proceedings of the Seventh ACM Symposium on Cloud Computing","volume":"1 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":"131105797","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: 4