9. April, 2022 - Virtual Conference

5th Workshop on Hot Topics in Cloud Computing Performance (HotCloudPerf 2022)

Disclaimer: HotCloudPerf 2022 will be fully virtual. Our experience with a virtual ICPE (and HotCloudperf) 2020 and 2021 was excellent and the participants rated the experience and format very highly. For more information, please contact us at: hotcloudperf2022@easychair.org

Overview

The HotCloudPerf workshop proposes a meeting venue for academics and practitioners, from experts to trainees, in the field of cloud computing performance. The new understanding of cloud computing covers the full computational continuum from data centers to edge resources to IoT sensors and devices. The workshop aims to engage this community and to lead to the development of new methodological aspects for gaining a deeper understanding not only of cloud performance, but also of cloud operation and behavior, through diverse quantitative evaluation tools, including benchmarks, metrics, and workload generators. The workshop focuses on novel cloud properties such as elasticity, performance isolation, dependability, and other non-functional system properties, in addition to classical performance-related metrics such as response time, throughput, scalability, and efficiency.

Acknowledgement

The HotCloudPerf workshop is technically sponsored by the Standard Performance Evaluation Corporation (SPEC)’s Research Group (RG) and is organized annually by the RG Cloud Group. HotCloudPerf has emerged from the series of yearly meetings organized by the RG Cloud Group, since 2013. The RG Cloud Group group is taking a broad approach, relevant for both academia and industry, to cloud benchmarking, quantitative evaluation, and experimental analysis.

Topics

  1. Methodological and practical aspects of software engineering, performance engineering, and computer systems related to hot topics in cloud performance.

  2. Empirical performance studies in cloud computing environments and systems, including observation, measurement, and surveys.

  3. Performance analysis using modeling, simulation, and queueing theory for cloud environments, applications, and systems.

  4. Tuning and auto-tuning of systems operating in cloud environments, e.g., auto-tiering of data or optimized resource deployment.

  5. Software patterns and architectures for engineering cloud performance, e.g., serverless.

  6. End-to-end performance engineering for pipelines and workflows in cloud environments, or of applications with non-trivial SLAs.

  7. Tools for monitoring and studying cloud computing performance.

  8. General and specific methods and methodologies for understanding and engineering cloud performance.

  9. Serverless computing platforms and microservices in cloud datacenters.

  10. Case studies on cloud performance and its interaction with the computational continuum

Important Dates


January 15 January 27, 2022

January 20 January 27, 2022

February 25, 2022

May 4, 2022

April 9, 2022

Abstract due

Papers due

Author Notification

Camera-ready deadline

Workshop day

Keynotes

Lydia Chen: Dependability Management for Datacenters: A Machine Learning Perspective

TU Delft, Netherlands

The practice of collecting big performance data has changed how infrastructure providers model and manage the system in the past decade. There is a methodology shift from domain-knowledge based white-box models, e.g., queueing and simulation, to black-box machine learning models. At the same time, there is an ever-increasing number of applications based on (deep) machine learning models. In this talk, I will first show how such a game change affects the dependability management for major infrastructure providers, e.g., IBM. In the second part of the talk, I will focus on recent techniques, e.g., differential approximation, scheduling, and parallel tuning, that aim to strike an optimal tradeoff between the latency and accuracy for deep learning applications at a minimum cost.

Evgenia Smirni: Serverless Machine Learning Serving for Scalable Workflows

College of William & Mary, Virginia, US

Serverless computing is a new pay-per-use cloud service paradigm that automates resource scaling for stateless functions and can potentially facilitate bursty machine learning serving. Batching is critical for latency performance and cost-effectiveness of machine learning inference, but unfortunately it is not supported by existing serverless platforms due to their stateless design. Initial experiments show that without batching, machine learning serving cannot reap the benefits of serverless computing. In this talk, I will describe BATCH, a framework for supporting efficient machine learning serving on serverless platforms. BATCH uses an optimizer to provide inference tail latency guarantees and cost optimization and to enable adaptive batching support. We prototype BATCH atop of AWS Lambda and popular machine learning inference systems. The evaluation verifies the accuracy of the analytic optimizer illustrates its use for performance and cost advantages over the state-of-the-art.

Paul Brebner: Scaling Open Source Big Data Cloud Applications is Easy/Hard

Instaclustr, California, US

In the last decade, the development of modern horizontally scalable open-source Big Data technologies such as Apache Cassandra (for data storage), and Apache Kafka (for data streaming) enabled cost-effective, highly scalable, reliable, low-latency applications, and made these technologies increasingly ubiquitous. To enable reliable horizontal scalability, both Cassandra and Kafka utilize partitioning (for concurrency) and replication (for reliability and availability) across clustered servers. But building scalable applications isn’t as easy as just throwing more servers at the clusters, and unexpected speed humps are common. Consequently, you also need to understand the performance impact of partitions, replication, and clusters; monitor the correct metrics to have an end-to-end view of applications and clusters; conduct careful benchmarking, and scale and tune iteratively to take into account performance insights and optimizations. In this presentation, I will explore some of the performance goals, challenges, solutions, and results I discovered over the last 5 years building multiple realistic demonstration applications. The examples will include trade-offs with elastic Cassandra auto-scaling, scaling a Cassandra and Kafka anomaly detection application to 19 Billion checks per day, and building low-latency streaming data pipelines using Kafka Connect for multiple heterogeneous source and sink systems.

Submission Types


  1. Full-papers (8 pages including references)

  2. Short-papers (4 pages including references)

  3. Talk only (1-2 pages, not included in the proceedings).

Format

The format of the submissions is single-blind and should follow the ACM format of the companion conference, ICPE.

All presented papers will have a good amount of time allocated for Q&A plus feedback. In addition, the presentation session will be wrapped up by a 10-15 min discussion.

Submission Site

Articles and talk-only contributions are required to be submitted via EasyChair.

Call for Papers

You can find the full Call for Papers (CfP) here: CfP

Organizing Committee

Cristina L. Abad, Escuela Superior Politécnica del Litoral, Ecuador, (cabadr@espol.edu.ec)

Simon Eismann, University of Würzburg, Germany, (simon.eismann@uni-wuerzburg.de)

Alexandru Iosup, VU Amsterdam, the Netherlands (a.iosup@vu.nl)


To contact the chairs, you can email: hotcloudperf2022@easychair.org

Program Committee

Cristina Abad, Escuela Superior Politecnica del Litoral

Ahmed Ali-Eldin, Chalmers | University of Gothenburg

Marta Beltran, Universidad Rey Juan Carlos

Andre Bondi, Software Performance and Scalability Consulting LLC

Marc Brooker, Amazon Web Services

Lucy Cherkasova, ARM Research

Wilhelm Hasselbring, University of Kiel

Nikolas Herbst, University of Würzburg

Riccardo Pinciroli, Gran Sasso Science Institute

Alexandru Iosup, Vrije Universiteit Amsterdam

Alessandro Papadopoulos, Mälardalen University

Joel Scheuner, Chalmers | University of Gothenburg

Petr Tůma, Charles University

Alexandru Uta, Leiden University

Erwin van Eyk, Vrije Universiteit Amsterdam

André van Hoorn, University of Hamburg

Chen Wang, IBM


Cristina Abad, Escuela Superior Politecnica del Litoral

Ahmed Ali-Eldin, Chalmers | University of Gothenburg

Marta Beltran, Universidad Rey Juan Carlos

Andre Bondi, Software Performance and Scalability Consulting LLC

Marc Brooker, Amazon Web Services

Lucy Cherkasova, ARM Research

Wilhelm Hasselbring, University of Kiel

Nikolas Herbst, University of Würzburg

Riccardo Pinciroli, Gran Sasso Science Institute

Alexandru Iosup, Vrije Universiteit Amsterdam

Alessandro Papadopoulos, Mälardalen University

Joel Scheuner, Chalmers | University of Gothenburg

Petr Tůma, Charles University

Alexandru Uta, Leiden University

Erwin van Eyk, Vrije Universiteit Amsterdam

André van Hoorn, University of Hamburg

Chen Wang, IBM