Enhancing in-vitro for comprehensive serverless cloud benchmarking
In recent years, Function-as-a-Service (FaaS) systems have been rising in popularity among developers due to their cost-efficiency and ease of use. Many platforms, such as Microsoft Azure, AWS Lambda and Google Cloud offer a ”pay-as-you-go” model, where users are charged only for the duration...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/181147 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | In recent years, Function-as-a-Service (FaaS) systems have been rising in popularity
among developers due to their cost-efficiency and ease of use. Many platforms, such
as Microsoft Azure, AWS Lambda and Google Cloud offer a ”pay-as-you-go” model,
where users are charged only for the duration their functions are running. Furthermore,
workflow enhancements such as Azure Durable Functions and AWS Step functions seek
to integrate stateful workflows for complex task execution. This provides considerable
cost savings compared to traditional hosting models, where users incur infrastructure
costs regardless of actual usage. However, as the inner workings of these systems are
not available to the public, users would seek to compare the performance between them,
leading to the rise of bench marking tools.
While tools to benchmark performance in FaaS systems do exist, many of them focus on the commercial aspect, diving into cost optimisation, rather than research on
performance and latency analysis. Those who do, have yet to implement analysis and
benchmarks on workflows.
In our project, we have successfully introduced workflows in our system, in the form
of a Directed Acyclic Graph. We then measured the overhead of invocations and
compared them by varying different aspects of the DAG. From our experiments, we
derived several insights; DAG Length showed a linear relationship with latency, and
DAG width did not have a clear impact on performance.
Our project serves as an enhancement to an existing system that aims to synthesize
representative workload summaries to evaluate the performance of serverless systems
at diverse load scale factors |
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