Hydra: Virtualized Multi-Language Runtime for High-Density Serverless Platforms
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866908460893863936 |
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| author | Ivanenko, Serhii Lanko, Vasyl Horn, Rudi Jovanovic, Vojin Bruno, Rodrigo |
| author_facet | Ivanenko, Serhii Lanko, Vasyl Horn, Rudi Jovanovic, Vojin Bruno, Rodrigo |
| contents | Serverless is an attractive computing model that offers seamless scalability and elasticity; it takes the infrastructure management burden away from users and enables a pay-as-you-use billing model. As a result, serverless is becoming increasingly popular to support highly elastic and bursty workloads. However, existing platforms are supported by bloated virtualization stacks, which, combined with bursty and irregular invocations, lead to high memory and latency overheads.
To reduce the virtualization stack bloat, we propose Hydra, a virtualized multi-language runtime and platform capable of hosting multiple sandboxes running concurrently. To fully leverage Hydra's virtualized runtime, we revisit the existing serverless platform design to make it colocation-aware across owners and functions, and to feature a caching layer of pre-allocated Hydra instances that can be used by different functions written in different languages to reduce cold starts. We also propose a snapshotting mechanism to checkpoint and restore individual sandboxes.
By consolidating multiple serverless function invocations through Hydra, we improve the overall function density (ops/GB-sec) by 2.41x on average compared to OpenWhisk runtimes, the state-of-the-art single-language runtimes used in most serverless platforms, and by 1.43x on average compared to Knative runtimes supporting invocation colocation within the same function. When reproducing the Azure Functions trace, our serverless platform operating Hydra instances reduces the overall memory footprint by 21.3-43.9% compared to operating OpenWhisk instances and by 14.5-30% compared to operating Knative instances. Hydra eliminates cold starts thanks to the pool of pre-warmed runtime instances, reducing p99 latency by 45.3-375.5x compared to OpenWhisk and by 1.9-51.4x compared to Knative. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_10131 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Hydra: Virtualized Multi-Language Runtime for High-Density Serverless Platforms Ivanenko, Serhii Lanko, Vasyl Horn, Rudi Jovanovic, Vojin Bruno, Rodrigo Distributed, Parallel, and Cluster Computing Programming Languages Serverless is an attractive computing model that offers seamless scalability and elasticity; it takes the infrastructure management burden away from users and enables a pay-as-you-use billing model. As a result, serverless is becoming increasingly popular to support highly elastic and bursty workloads. However, existing platforms are supported by bloated virtualization stacks, which, combined with bursty and irregular invocations, lead to high memory and latency overheads. To reduce the virtualization stack bloat, we propose Hydra, a virtualized multi-language runtime and platform capable of hosting multiple sandboxes running concurrently. To fully leverage Hydra's virtualized runtime, we revisit the existing serverless platform design to make it colocation-aware across owners and functions, and to feature a caching layer of pre-allocated Hydra instances that can be used by different functions written in different languages to reduce cold starts. We also propose a snapshotting mechanism to checkpoint and restore individual sandboxes. By consolidating multiple serverless function invocations through Hydra, we improve the overall function density (ops/GB-sec) by 2.41x on average compared to OpenWhisk runtimes, the state-of-the-art single-language runtimes used in most serverless platforms, and by 1.43x on average compared to Knative runtimes supporting invocation colocation within the same function. When reproducing the Azure Functions trace, our serverless platform operating Hydra instances reduces the overall memory footprint by 21.3-43.9% compared to operating OpenWhisk instances and by 14.5-30% compared to operating Knative instances. Hydra eliminates cold starts thanks to the pool of pre-warmed runtime instances, reducing p99 latency by 45.3-375.5x compared to OpenWhisk and by 1.9-51.4x compared to Knative. |
| title | Hydra: Virtualized Multi-Language Runtime for High-Density Serverless Platforms |
| topic | Distributed, Parallel, and Cluster Computing Programming Languages |
| url | https://arxiv.org/abs/2212.10131 |