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Enhancing Observability of Serverless Computing with the Serverless Application Analytics Framework

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Wes LloydAuthors Info & Claims

ICPE '21: Companion of the ACM/SPEC International Conference on Performance Engineering

Pages 161 - 164

https://doi.org/10.1145/3447545.3451173

Published: 19 April 2021 Publication History

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Abstract

To improve the observability of workload performance, resource utilization, and infrastructure underlying serverless Function-as-a-Service (FaaS) platforms, we have developed the Serverless Application Analytics Framework (SAAF). SAAF provides a reusable framework supporting multiple programming languages that developers can leverage to inspect performance, resource utilization, scalability, and infrastructure metrics of function deployments to commercial and open-source FaaS platforms. To automate reproducible FaaS performance experiments, we provide the FaaS Runner as a multithreaded FaaS client. FaaS Runner provides a programmable client that can orchestrate over one thousand concurrent FaaS function calls. The ReportGenerator is then used to aggregate experiment output into CSV files for consumption by popular data analytics tools. SAAF and its supporting tools combined can assess forty-eight distinct metrics to enhance observability of serverless software deployments. In this tutorial paper, we describe SAAF and its supporting tools and provide examples of observability insights that can be derived.

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Cited By

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Lin CMahmoudi NFan CKhazaei H(2023)Fine-Grained Performance and Cost Modeling and Optimization for FaaS ApplicationsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.321478334:1(180-194)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TPDS.2022.3214783
Cordingly RLloyd W(2023)Enabling Serverless Sky Computing2023 IEEE International Conference on Cloud Engineering (IC2E)10.1109/IC2E59103.2023.00038(232-235)Online publication date: 25-Sep-2023
https://doi.org/10.1109/IC2E59103.2023.00038
Cordingly RKaur JDwivedi DLloyd W(2023)Towards Serverless Sky Computing: An Investigation on Global Workload Distribution to Mitigate Carbon Intensity, Network Latency, and Cost2023 IEEE International Conference on Cloud Engineering (IC2E)10.1109/IC2E59103.2023.00015(59-69)Online publication date: 25-Sep-2023
https://doi.org/10.1109/IC2E59103.2023.00015
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Index Terms

Enhancing Observability of Serverless Computing with the Serverless Application Analytics Framework
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing

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Information & Contributors

Information

Published In

ICPE '21: Companion of the ACM/SPEC International Conference on Performance Engineering

April 2021

198 pages

ISBN:9781450383318

DOI:10.1145/3447545

General Chairs:
Johann Bourcier
University of Rennes 1, France
,
Zhen Ming (Jack) Jiang
York University, Canada
,
Program Chairs:
Cor-Paul Bezemer
University of Alberta, Canada
,
Vittorio Cortellessa
University of L'Aquila, Italy

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 April 2021

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ICPE '21: ACM/SPEC International Conference on Performance Engineering

April 19 - 23, 2021

Virtual Event, France

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Overall Acceptance Rate 252 of 851 submissions, 30%

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Cited By

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Lin CMahmoudi NFan CKhazaei H(2023)Fine-Grained Performance and Cost Modeling and Optimization for FaaS ApplicationsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.321478334:1(180-194)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TPDS.2022.3214783
Cordingly RLloyd W(2023)Enabling Serverless Sky Computing2023 IEEE International Conference on Cloud Engineering (IC2E)10.1109/IC2E59103.2023.00038(232-235)Online publication date: 25-Sep-2023
https://doi.org/10.1109/IC2E59103.2023.00038
Cordingly RKaur JDwivedi DLloyd W(2023)Towards Serverless Sky Computing: An Investigation on Global Workload Distribution to Mitigate Carbon Intensity, Network Latency, and Cost2023 IEEE International Conference on Cloud Engineering (IC2E)10.1109/IC2E59103.2023.00015(59-69)Online publication date: 25-Sep-2023
https://doi.org/10.1109/IC2E59103.2023.00015
Cordingly RXu SLloyd W(2022)Function Memory Optimization for Heterogeneous Serverless Platforms with CPU Time Accounting2022 IEEE International Conference on Cloud Engineering (IC2E)10.1109/IC2E55432.2022.00019(104-115)Online publication date: Sep-2022
https://doi.org/10.1109/IC2E55432.2022.00019
Kiener MChadha MGerndt M(2021)Towards Demystifying Intra-Function Parallelism in Serverless ComputingProceedings of the Seventh International Workshop on Serverless Computing (WoSC7) 202110.1145/3493651.3493672(42-49)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.1145/3493651.3493672

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The Serverless Application Analytics Framework: Enabling Design Trade-off Evaluation for Serverless Software

Supporting Multi-Provider Serverless Computing on the Edge

Serverless Computing Revisited: Evolution, State-of-the-Art, and Performance Challenges