Companion of the 2018 ACM/SPEC International Conference on Performance Engineering最新文献_第9页

Adaptive Dispatch: A Pattern for Performance-Aware Software Self-Adaptation 自适应调度:一种性能感知软件自适应模式

Companion of the 2018 ACM/SPEC International Conference on Performance Engineering Pub Date : 2018-04-02 DOI: 10.1145/3185768.3186406

Petr Kubát, L. Bulej, T. Bures, Vojtech Horký, P. Tůma

引用次数: 1

Application Speedup Characterization: Modeling Parallelization Overhead and Variations of Problem Size and Number of Cores. 应用加速表征:建模并行化开销和问题大小和核数的变化。

Companion of the 2018 ACM/SPEC International Conference on Performance Engineering Pub Date : 2018-04-02 DOI: 10.1145/3185768.3185770

Victor H. F. Oliveira, Alex F. A. Furtunato, L. Silveira, Kyriakos Georgiou, K. Eder, S. X. D. Souza

{"title":"Application Speedup Characterization: Modeling Parallelization Overhead and Variations of Problem Size and Number of Cores.","authors":"Victor H. F. Oliveira, Alex F. A. Furtunato, L. Silveira, Kyriakos Georgiou, K. Eder, S. X. D. Souza","doi":"10.1145/3185768.3185770","DOIUrl":"https://doi.org/10.1145/3185768.3185770","url":null,"abstract":"To make efficient use of multi-core processors, it is important to understand the performance behavior of parallel applications. Modeling this can enable the use of online approaches to optimize throughput or energy, or even guarantee a minimum QoS. Accurate models would avoid probe different runtime configurations, which causes overhead. Throughout the years, many speedup models were proposed. Most of them based on Amdahl's or Gustafson's laws. However, many of those make considerations such as a fixed parallel fraction, or a parallel fraction that varies linearly with problem size, and inexistent parallelization overhead. Although such models aid in the theoretical understanding, these considerations do not hold in real environments, which makes the modeling unsuitable for accurate characterization of parallel applications. The model proposed estimates the speedup taking into account the variation of its parallel fraction according to problem size, number of cores used and overhead. Using four applications from the PARSEC benchmark suite, the proposed model was able to estimate speedups more accurately than other models in recent literature.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78312116","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

Towards Scalability Guidelines for Semantic Data Container Management 面向语义数据容器管理的可伸缩性指南

Companion of the 2018 ACM/SPEC International Conference on Performance Engineering Pub Date : 2018-04-02 DOI: 10.1145/3185768.3186302

Gunnar Brataas, B. Neumayr, C. G. Schütz, A. Vennesland

引用次数: 1

SPARK Job Performance Analysis and Prediction Tool SPARK工作绩效分析和预测工具

Companion of the 2018 ACM/SPEC International Conference on Performance Engineering Pub Date : 2018-04-02 DOI: 10.1145/3185768.3185772

Rekha Singhal, Chetan Phalak, P. Singh

{"title":"SPARK Job Performance Analysis and Prediction Tool","authors":"Rekha Singhal, Chetan Phalak, P. Singh","doi":"10.1145/3185768.3185772","DOIUrl":"https://doi.org/10.1145/3185768.3185772","url":null,"abstract":"Spark is one of most widely deployed in-memory big data technology for parallel data processing across cluster of machines. The availability of these big data platforms on commodity machines has raised the challenge of assuring performance of applications with increase in data size. We have build a tool to assist application developer and tester to estimate an application execution time for larger data size before deployment. Conversely, the tool may also be used to estimate the competent cluster size for desired application performance in production environment. The tool can be used for detailed profiling of Spark job, post execution, to understand performance bottleneck. This tool incorporates different configurations of Spark cluster to estimate application performance. Therefore, it can also be used with optimization techniques to get tuned value of Spark parameters for an optimal performance. The tool's key innovations are support for different configurations of Spark platform for performance prediction and simulator to estimate Spark stage execution time which includes task execution variability due to HDFS, data skew and cluster nodes heterogeneity. The tool using model [3] has been shown to predict within 20% error bound for Wordcount, Terasort,Kmeans and few SQL workloads.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80676391","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

A Performance Study of Big Data Workloads in Cloud Datacenters with Network Variability 具有网络可变性的云数据中心大数据工作负载性能研究

Companion of the 2018 ACM/SPEC International Conference on Performance Engineering Pub Date : 2018-04-02 DOI: 10.1145/3185768.3186299

Alexandru Uta, Harry Obaseki

{"title":"A Performance Study of Big Data Workloads in Cloud Datacenters with Network Variability","authors":"Alexandru Uta, Harry Obaseki","doi":"10.1145/3185768.3186299","DOIUrl":"https://doi.org/10.1145/3185768.3186299","url":null,"abstract":"Public cloud computing platforms are a cost-effective solution for individuals and organizations to deploy various types of workloads, ranging from scientific applications, business-critical workloads, e-governance to big data applications. Co-locating all such different types of workloads in a single datacenter leads not only to performance degradation, but also to large degrees of performance variability, which is the result of virtualization, resource sharing and congestion. Many studies have already assessed and characterized the degree of resource variability in public clouds. However, we are missing a clear picture on how resource variability impacts big data workloads. In this work, we take a step towards characterizing the behavior of big data workloads under network bandwidth variability. Emulating real-world clouds» bandwidth distribution, we characterize the performance achieved by running real-world big data applications. We find that most big data workloads are slowed down under network variability scenarios, even those that are not network-bound. Moreover, the maximum average slowdown for the cloud setup with highest variability is 1.48 for CPU-bound workloads, and 1.79 for network-bound workloads.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74942011","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

Combining Energy Saving Techniques in Data Centres using Model-Based Analysis 结合基于模型分析的数据中心节能技术

Companion of the 2018 ACM/SPEC International Conference on Performance Engineering Pub Date : 2018-04-02 DOI: 10.1145/3185768.3186310

Björn F. Postema, T. V. Damme, C. D. Persis, P. Tesi, B. Haverkort

引用次数: 1

Exploratory Analysis of Spark Structured Streaming Spark结构化流的探索性分析

Companion of the 2018 ACM/SPEC International Conference on Performance Engineering Pub Date : 2018-04-02 DOI: 10.1145/3185768.3186360

Todor Ivanov, Jason Taafe

引用次数: 7

A Workload-Dependent Performance Analysis of an In-Memory Database in a Multi-Tenant Configuration 多租户配置下内存数据库的工作负载相关性能分析

Companion of the 2018 ACM/SPEC International Conference on Performance Engineering Pub Date : 2018-04-02 DOI: 10.1145/3185768.3186290

Dominik Paluch, Harald Kienegger, H. Krcmar

{"title":"A Workload-Dependent Performance Analysis of an In-Memory Database in a Multi-Tenant Configuration","authors":"Dominik Paluch, Harald Kienegger, H. Krcmar","doi":"10.1145/3185768.3186290","DOIUrl":"https://doi.org/10.1145/3185768.3186290","url":null,"abstract":"Modern in-memory database systems begin to provide multi-tenancy features. In contrast to the traditional operation of one large database appliance per system, the utilization of the multi-tenancy features allows for multiple database containers running on one system. Consequently, the database tenants share the same system resources, which has an influence on their performance. Understanding the performance of database tenants in different setups with varying workloads is a challenging task. However, knowledge of the performance behavior is crucial in order to benefit from multi-tenancy. In this paper, we provide fine-grained performance insights of the in-memory database SAP HANA in a multi-tenant configuration. We perform multiple benchmark runs utilizing an online analytical processing benchmark in order to retrieve information about the performance behavior of the multi-tenant database containers. Furthermore, we provide an analysis of the collected results and show a more efficient usage of threads in an environment with less active tenants under specific workload conditions.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"239 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89756283","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

Performance Prediction for Families of Data-Intensive Software Applications 数据密集型软件应用家族的性能预测

Companion of the 2018 ACM/SPEC International Conference on Performance Engineering Pub Date : 2018-04-02 DOI: 10.1145/3185768.3186405

J. Verriet, R. Dankers, L. Somers

引用次数: 0

An Analysis of Workflow Formalisms for Workflows with Complex Non-Functional Requirements 复杂非功能需求工作流的工作流形式化分析

Companion of the 2018 ACM/SPEC International Conference on Performance Engineering Pub Date : 2018-04-02 DOI: 10.1145/3185768.3186297

L. Versluis, Erwin Van Eyk, A. Iosup

{"title":"An Analysis of Workflow Formalisms for Workflows with Complex Non-Functional Requirements","authors":"L. Versluis, Erwin Van Eyk, A. Iosup","doi":"10.1145/3185768.3186297","DOIUrl":"https://doi.org/10.1145/3185768.3186297","url":null,"abstract":"Cloud and datacenter operators offer progressively more sophisticated service level agreements to customers. The Quality-of-Service guarantees by these operators have started to entail non-functional requirements customers have regarding their applications. At the same time, expressing applications as workflows in datacenters is increasingly more common. Currently, non-functional requirements (NFRs) can only be defined on entire workflows and cannot be changed at runtime, possibly wasting valuable resources. To move towards modifiable NFRs at the task level, there is a need for a formalism capable of expressing this. Existing formalisms do not support this level of granularity or are restricted to a subset of NFRs. In this work, we investigate the current support for NFRs in existing formalisms. Using a library containing workflows with and without NFRs, we inspect the capability of existing formalisms to express these requirements. Additionally, we create and evaluate five metrics to qualitatively and quantitatively compare each formalism. Our main findings are that although current formalisms do not support arbitrary NFRs per-task, the Directed Acyclic Graphs (DAGs) formalism is the most suitable to extend.","PeriodicalId":10596,"journal":{"name":"Companion of the 2018 ACM/SPEC International Conference on Performance Engineering","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75523326","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