Companion of the 2023 ACM/SPEC International Conference on Performance Engineering最新文献_第5页

How Do ML Jobs Fail in Datacenters? Analysis of a Long-Term Dataset from an HPC Cluster 数据中心的机器学习作业是如何失败的?高性能计算集群长期数据集分析

Companion of the 2023 ACM/SPEC International Conference on Performance Engineering Pub Date : 2023-04-15 DOI: 10.1145/3578245.3584726

Xiaoyu Chu, Sacheendra Talluri, L. Versluis, A. Iosup

引用次数: 0

GraalVM Compiler Benchmark Results Dataset (Data Artifact) GraalVM编译器基准结果数据集(数据工件)

Companion of the 2023 ACM/SPEC International Conference on Performance Engineering Pub Date : 2023-04-15 DOI: 10.1145/3578245.3585025

L. Bulej, Vojtech Horký, Michele Tucci, P. Tůma, François Farquet, David Leopoldseder, Aleksandar Prokopec

引用次数: 0

Transparent Trace Annotation for Performance Debugging in Microservice-oriented Systems (Work In Progress Paper) 面向微服务系统性能调试的透明跟踪注释(工作进展文件)

Companion of the 2023 ACM/SPEC International Conference on Performance Engineering Pub Date : 2023-04-15 DOI: 10.1145/3578245.3585030

Adel Belkhiri, Ahmad Shahnejat Bushehri, Felipe Gohring de Magalhaes, G. Nicolescu

{"title":"Transparent Trace Annotation for Performance Debugging in Microservice-oriented Systems (Work In Progress Paper)","authors":"Adel Belkhiri, Ahmad Shahnejat Bushehri, Felipe Gohring de Magalhaes, G. Nicolescu","doi":"10.1145/3578245.3585030","DOIUrl":"https://doi.org/10.1145/3578245.3585030","url":null,"abstract":"Microservices is a cloud-native architecture in which a single application is implemented as a collection of small, independent, and loosely-coupled services. This architecture is gaining popularity in the industry as it promises to make applications more scalable and easier to develop and deploy. Nonetheless, adopting this architecture in practice has raised many concerns, particularly regarding the difficulty of diagnosing performance bugs and explaining abnormal software behaviour. Fortunately, many tools based on distributed tracing were proposed to achieve observability in microservice-oriented systems and address these concerns (e.g., Jaeger). Distributed tracing is a method for tracking user requests as they flow between services. While these tools can identify slow services and detect latency-related problems, they mostly fail to pinpoint the root causes of these issues. This paper presents a new approach for enacting cross-layer tracing of microservice-based applications. It also proposes a framework for annotating traces generated by most distributed tracing tools with relevant tracing data and metrics collected from the kernel. The information added to the traces aims at helping the practitioner get a clear insight into the operations of the application executing user requests. The framework we present is notably efficient in diagnosing the causes of long tail latencies. Unlike other solutions, our approach for annotating traces is completely transparent as it does not require the modification of the application, the tracer, or the operating system. Furthermore, our evaluation shows that this approach incurs low overhead costs.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122936556","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: 0

ICPE'23 AIPerf Workshop Chairs' Welcome ICPE第23届AIPerf研讨会主席欢迎

Companion of the 2023 ACM/SPEC International Conference on Performance Engineering Pub Date : 2023-04-15 DOI: 10.1145/3578245.3584940

Emilio Incerto, Marin Litoiu, Riccardo Pinciroli

{"title":"ICPE'23 AIPerf Workshop Chairs' Welcome","authors":"Emilio Incerto, Marin Litoiu, Riccardo Pinciroli","doi":"10.1145/3578245.3584940","DOIUrl":"https://doi.org/10.1145/3578245.3584940","url":null,"abstract":"We are pleased to welcome you to the 2023 ACM Workshop on Artificial Intelligence for Performance Modeling, Prediction, and Control - AIPerf'23. In its first edition, AIPerf intends to foster the usage of AI (such as probabilistic methods, machine learning, and deep learning) to control, model, and predict the performance of computer systems. The relevance of these topics reflects current and future trends toward exploiting AI-based approaches to deal with complex, large, and interconnected systems. Despite AI and ML being widely adopted techniques to investigate several mainstream domains, their usage for performance modeling and evaluation is still limited, and their benefit to the performance engineering field remains unclear. AIPerf proposes a meeting venue to promote the dissemination of research works that use or study AI techniques for quantitative analysis of modern ICT systems and to engage academics and practitioners of this field. The workshop focuses on presenting experiences and results of applying AI/ML-based techniques to performance-related problems, as well as sharing performance datasets and benchmarks with the community to facilitate the development of new and more accurate learning procedures. Putting together AIPerf'23 was a team effort. We first thank the authors for providing the content of the program. We are grateful to the program committee and the senior program committee, who worked very hard to review papers and provide authors' feedback. Finally, We thank the ICPE23 organizers for sponsoring AIPerf in its community. We hope that you will find this program interesting and thought-provoking and that the symposium will provide you with a valuable opportunity to share ideas with other researchers and practitioners from institutions around the world.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"426 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116716835","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: 0

Large-scale Graph Processing and Simulation with Serverless Workflows in Federated FaaS 联邦FaaS中无服务器工作流的大规模图形处理与仿真

Companion of the 2023 ACM/SPEC International Conference on Performance Engineering Pub Date : 2023-04-15 DOI: 10.1145/3578245.3585333

S. Ristov, Reza Farahani, R.-C. Prodan

{"title":"Large-scale Graph Processing and Simulation with Serverless Workflows in Federated FaaS","authors":"S. Ristov, Reza Farahani, R.-C. Prodan","doi":"10.1145/3578245.3585333","DOIUrl":"https://doi.org/10.1145/3578245.3585333","url":null,"abstract":"Serverless computing offers an affordable and easy way to code lightweight functions that can be invoked based on some events to perform simple tasks. For more complicated processing, multiple serverless functions can be orchestrated as a directed acyclic graph to form a serverless workflow, so-called function choreography (FC). Although most famous cloud providers offer FC management systems such as AWS Step Functions, and there are also several open-source FC management systems (e.g., Apache OpenWhisk), their primary focus is on describing the control flow and data flow between serverless functions in the FC. Moreover, the existing FC management systems rarely consider the processed data, which is commonly represented in a graph format. In this paper, we review the capabilities of the existing FC management systems in supporting graph processing applications. We also raise two key research questions related to large-scale graph processing using serverless computing in federated Function-as-a-Service (FaaS). As part of the Graph-Massivizer project, funded by the Horizon Europe research and innovation program, we will research and develop (prototype) solutions that will address these challenges.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126985225","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: 0

A Pattern-based Function and Workflow Visual Environment for FaaS Development across the Continuum 跨连续体的FaaS开发的基于模式的功能和工作流可视化环境

Companion of the 2023 ACM/SPEC International Conference on Performance Engineering Pub Date : 2023-04-15 DOI: 10.1145/3578245.3584934

George Kousiouris, Szymon Ambroziak, Blazej Zarzycki, D. Costantino, Stylianos Tsarsitalidis, Vasileios Katevas, Alessandro Mamelli, Teta Stamati

{"title":"A Pattern-based Function and Workflow Visual Environment for FaaS Development across the Continuum","authors":"George Kousiouris, Szymon Ambroziak, Blazej Zarzycki, D. Costantino, Stylianos Tsarsitalidis, Vasileios Katevas, Alessandro Mamelli, Teta Stamati","doi":"10.1145/3578245.3584934","DOIUrl":"https://doi.org/10.1145/3578245.3584934","url":null,"abstract":"The ability to split applications across different locations in the continuum (edge/cloud) creates needs for application break down into smaller and more distributed chunks. In this realm the Function as a Service approach appears as a significant enabler in this process. The paper presents a visual function and workflow development environment for complex FaaS (Apache OpenwhisK) applications. The environment offers a library of pattern based and reusable nodes and flows while mitigating function orchestration limitations in the domain. Generation of the deployable artefacts, i.e. the functions, is performed through embedded DevOps pipelines. A range of annotations are available for dictating diverse options including QoS needs, function or data locality requirements, function affinity considerations etc. These are propagated to the deployment and operation stacks for supporting the cloud/edge interplay. The mechanism is evaluated functionally through creating, registering and executing functions and orchestrating workflows, adapting typical parallelization patterns and an edge data collection process.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134408574","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

AI, What Does the Future Hold for Us? Automating Strategic Foresight 人工智能，未来会给我们带来什么?自动化战略预见

Companion of the 2023 ACM/SPEC International Conference on Performance Engineering Pub Date : 2023-04-15 DOI: 10.1145/3578245.3585336

Jože M. Rožanec, P. Nemec, Gregor Leban, M. Grobelnik

引用次数: 0

Identification and Classification of JMH Microbenchmark States using Time Series Analysis JMH微基准状态的时间序列识别与分类

Companion of the 2023 ACM/SPEC International Conference on Performance Engineering Pub Date : 2023-04-15 DOI: 10.1145/3578245.3584694

T. Wallace, B. Ombuki-Berman, Naser Ezzati-Jivan

引用次数: 0

Automatic FAIR Provenance Collection and Visualization for Time Series 时间序列的自动公平来源收集和可视化

Companion of the 2023 ACM/SPEC International Conference on Performance Engineering Pub Date : 2023-04-15 DOI: 10.1145/3578245.3585026

Fadoua Rafii, H. González-Vélez, Adriana E. Chis

{"title":"Automatic FAIR Provenance Collection and Visualization for Time Series","authors":"Fadoua Rafii, H. González-Vélez, Adriana E. Chis","doi":"10.1145/3578245.3585026","DOIUrl":"https://doi.org/10.1145/3578245.3585026","url":null,"abstract":"Provenance provides data lineage and history of different transformations applied to a dataset. A complete trace of data provenance can enable the reanalysis, reproducibility, and reusability of features, which are essential for validating results and extending them in many projects. Open time series datasets are readily accessible and discoverable, but their full reproducibility and reusability require clear metadata provenance. This paper introduces an assessment of provenance variables using an algorithm for collecting FAIR (Findable, Accessible, Interoperable, Reusable) characteristics in open time series and generating an associated provenance graph. We have evaluated the FAIRness of provenance traces by automatically mapping their properties to a provenance data model graph for a case study employing open time series from weather stations. Our approach arguably enables researchers to analyse time series datasets with similar characteristics, prompting new research questions, insights, and investigations. As a result, this approach has the potential to promote reusability and reproducibility, which are critical factors in scientific research.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129529972","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: 0

Views From the Trenches: Current Trends in Performance Engineering 来自战壕的观点:性能工程的当前趋势

Companion of the 2023 ACM/SPEC International Conference on Performance Engineering Pub Date : 2023-04-15 DOI: 10.1145/3578245.3584355

A. Podelko

引用次数: 0