Proceedings of the 2017 Symposium on Cloud Computing最新文献_第3页

Early work on modeling computational sprinting 早期对计算冲刺建模的研究

Proceedings of the 2017 Symposium on Cloud Computing Pub Date : 2017-09-24 DOI: 10.1145/3127479.3132691

Nathaniel Morris, Christopher Stewart, R. Birke, L. Chen, Jaimie Kelley

引用次数: 2

Bridging the architectural gap between NOS design principles in software-defined networks 弥合软件定义网络中NOS设计原则之间的架构差距

Proceedings of the 2017 Symposium on Cloud Computing Pub Date : 2017-09-24 DOI: 10.1145/3127479.3132567

Jaehyun Nam, Hyeonseong Jo, Yeonkeun Kim, Phillip A. Porras, V. Yegneswaran, Seungwon Shin

引用次数: 1

Secure data types: a simple abstraction for confidentiality-preserving data analytics 安全数据类型:用于保密性数据分析的简单抽象

Proceedings of the 2017 Symposium on Cloud Computing Pub Date : 2017-09-24 DOI: 10.1145/3127479.3129256

Savvas Savvides, J. Stephen, Masoud Saeida Ardekani, V. Sundaram, P. Eugster

{"title":"Secure data types: a simple abstraction for confidentiality-preserving data analytics","authors":"Savvas Savvides, J. Stephen, Masoud Saeida Ardekani, V. Sundaram, P. Eugster","doi":"10.1145/3127479.3129256","DOIUrl":"https://doi.org/10.1145/3127479.3129256","url":null,"abstract":"Cloud computing offers a cost-efficient data analytics platform. However, due to the sensitive nature of data, many organizations are reluctant to analyze their data in public clouds. Both software-based and hardware-based solutions have been proposed to address the stalemate, yet all have substantial limitations. We observe that a main issue cutting across all solutions is that they attempt to support confidentiality in data queries in a way transparent to queries. We propose the novel abstraction of secure data types with corresponding annotations for programmers to conveniently denote constraints relevant to security. These abstractions are leveraged by novel compilation techniques in our system Cuttlefish to compute data analytics queries in public cloud infrastructures while keeping sensitive data confidential. Cuttlefish encrypts all sensitive data residing in the cloud and employs partially homomorphic encryption schemes to perform operations securely, resorting however to client-side completion, re-encryption, or secure hardware-based re-encryption based on Intel's SGX when available based on a novel planner engine. Our evaluation shows that our prototype can execute all queries in standard benchmarks such as TPC-H and TPC-DS with an average overhead of 2.34× and 1.69× respectively compared to a plaintext execution that reveals all data.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84664636","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

Optimized on-demand data streaming from sensor nodes 从传感器节点优化按需数据流

Proceedings of the 2017 Symposium on Cloud Computing Pub Date : 2017-09-24 DOI: 10.1145/3127479.3131621

J. Traub, S. Breß, T. Rabl, Asterios Katsifodimos, V. Markl

引用次数: 35

Processing Java UDFs in a C++ environment 在c++环境中处理Java udf

Proceedings of the 2017 Symposium on Cloud Computing Pub Date : 2017-09-24 DOI: 10.1145/3127479.3132022

Viktor Rosenfeld, René Müller, Pınar Tözün, Fatma Özcan

{"title":"Processing Java UDFs in a C++ environment","authors":"Viktor Rosenfeld, René Müller, Pınar Tözün, Fatma Özcan","doi":"10.1145/3127479.3132022","DOIUrl":"https://doi.org/10.1145/3127479.3132022","url":null,"abstract":"Many popular big data analytics systems today make liberal use of user-defined functions (UDFs) in their programming interface and are written in languages based on the Java Virtual Machine (JVM). This combination creates a barrier when we want to integrate processing engines written in a language that compiles down to machine code with a JVM-based big data analytics ecosystem. In this paper, we investigate efficient ways of executing UDFs written in Java inside a data processing engine written in C++. While it is possible to call Java code from machine code via the Java Native Interface (JNI), a naive implementation that applies the UDF one row at a time incurs a significant overhead, up to an order of magnitude. Instead, we can significantly reduce the costs of JNI calls and data copies between Java and machine code, if we execute UDFs on batches of rows, and reuse input/output buffers when possible. Our evaluation of these techniques using different scalar UDFs, in a prototype system that combines Spark and a columnar data processing engine written in C++, shows that such a combination does not slow down the execution of SparkSQL queries containing such UDFs. In fact, we find that the execution of Java UDFs inside an embedded JVM in our C++ engine is 1.12X to 1.53X faster than executing in Spark alone. Our analysis also shows that compiling Java UDFs directly into machine code is not always beneficial over strided execution in the JVM.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76177865","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: 11

Indy: a software system for the dense cloud Indy:一个用于密集云的软件系统

Proceedings of the 2017 Symposium on Cloud Computing Pub Date : 2017-09-24 DOI: 10.1145/3127479.3134429

Chenggang Wu, Jose M. Faleiro, Yihan Lin, J. Hellerstein

引用次数: 0

Revisiting performance in big data systems: an resource decoupling approach 重新审视大数据系统中的性能:一种资源解耦方法

Proceedings of the 2017 Symposium on Cloud Computing Pub Date : 2017-09-24 DOI: 10.1145/3127479.3132685

Chen Yang, Qi Guo, Xiaofeng Meng, Rihui Xin, Chunkai Wang

引用次数: 0

A scalable distributed spatial index for the internet-of-things 物联网的可扩展分布式空间索引

Proceedings of the 2017 Symposium on Cloud Computing Pub Date : 2017-09-24 DOI: 10.1145/3127479.3132254

A. Iyer, I. Stoica

{"title":"A scalable distributed spatial index for the internet-of-things","authors":"A. Iyer, I. Stoica","doi":"10.1145/3127479.3132254","DOIUrl":"https://doi.org/10.1145/3127479.3132254","url":null,"abstract":"The increasing interest in the Internet-of-Things (IoT) suggests that a new source of big data is imminent---the machines and sensors in the IoT ecosystem. The fundamental characteristic of the data produced by these sources is that they are inherently geospatial in nature. In addition, they exhibit unprecedented and unpredictable skews. Thus, big data systems designed for IoT applications must be able to efficiently ingest, index and query spatial data having heavy and unpredictable skews. Spatial indexing is well explored area of research in literature, but little attention has been given to the topic of efficient distributed spatial indexing. In this paper, we propose Sift, a distributed spatial index and its implementation. Unlike systems that depend on load balancing mechanisms that kick-in post ingestion, Sift tries to distribute the incoming data along the distributed structure at indexing time and thus incurs minimal rebalancing overhead. Sift depends only on an underlying key-value store, hence is implementable in many existing big data stores. Our evaluations of Sift on a popular open source data store show promising results---Sift achieves up to 8× reduction in indexing overhead while simultaneously reducing the query latency and index size by over 2× and 3× respectively, in a distributed environment compared to the state-of-the-art.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86142934","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: 12

Janus: supporting heterogeneous power management in virtualized environments Janus:支持虚拟化环境中的异构电源管理

Proceedings of the 2017 Symposium on Cloud Computing Pub Date : 2017-09-24 DOI: 10.1145/3127479.3132566

Daehoon Kim, Mohammad Alian, Jaehyuk Huh, N. Kim

引用次数: 0

Practical whole-system provenance capture 实用的全系统来源捕获

Proceedings of the 2017 Symposium on Cloud Computing Pub Date : 2017-09-24 DOI: 10.1145/3127479.3129249

Thomas Pasquier, Xueyuan Han, Mark Goldstein, Thomas Moyer, D. Eyers, M. Seltzer, J. Bacon

{"title":"Practical whole-system provenance capture","authors":"Thomas Pasquier, Xueyuan Han, Mark Goldstein, Thomas Moyer, D. Eyers, M. Seltzer, J. Bacon","doi":"10.1145/3127479.3129249","DOIUrl":"https://doi.org/10.1145/3127479.3129249","url":null,"abstract":"Data provenance describes how data came to be in its present form. It includes data sources and the transformations that have been applied to them. Data provenance has many uses, from forensics and security to aiding the reproducibility of scientific experiments. We present CamFlow, a whole-system provenance capture mechanism that integrates easily into a PaaS offering. While there have been several prior whole-system provenance systems that captured a comprehensive, systemic and ubiquitous record of a system's behavior, none have been widely adopted. They either A) impose too much overhead, B) are designed for long-outdated kernel releases and are hard to port to current systems, C) generate too much data, or D) are designed for a single system. CamFlow addresses these shortcoming by: 1) leveraging the latest kernel design advances to achieve efficiency; 2) using a self-contained, easily maintainable implementation relying on a Linux Security Module, NetFilter, and other existing kernel facilities; 3) providing a mechanism to tailor the captured provenance data to the needs of the application; and 4) making it easy to integrate provenance across distributed systems. The provenance we capture is streamed and consumed by tenant-built auditor applications. We illustrate the usability of our implementation by describing three such applications: demonstrating compliance with data regulations; performing fault/intrusion detection; and implementing data loss prevention. We also show how CamFlow can be leveraged to capture meaningful provenance without modifying existing applications.","PeriodicalId":20679,"journal":{"name":"Proceedings of the 2017 Symposium on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77536678","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: 114