arXiv subjects

Rudi Horn

Publications and source records attributed to Rudi Horn.

3 recordsLinked to original sources

arXiv2025

Hydra: Virtualized Multi-Language Runtime for High-Density Serverless Platforms

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.

cs.DC↗

arXiv2020

Language-Integrated Updatable Views (Extended version)

Relational lenses are a modern approach to the view update problem in relational databases. As introduced by Bohannon et al. (2006), relational lenses allow the definition of updatable views by the composition of lenses performing individual transformations. Horn et al. (2018) provided the first implementation of incremental relational lenses, which demonstrated that relational lenses can be implemented efficiently by propagating changes to the database rather than replacing the entire database state. However, neither approach proposes a concrete language design; consequently, it is unclear how to integrate lenses into a general-purpose programming language, or how to check that lenses satisfy the well-formedness conditions needed for predictable behaviour. In this paper, we propose the first full account of relational lenses in a functional programming language, by extending the Links web programming language. We provide support for higher-order predicates, and provide the first account of typechecking relational lenses which is amenable to implementation. We prove the soundness of our typing rules, and illustrate our approach by implementing a curation interface for a scientific database application.

cs.PL↗

arXiv2018

Incremental Relational Lenses

Lenses are a popular approach to bidirectional transformations, a generalisation of the view update problem in databases, in which we wish to make changes to source tables to effect a desired change on a view. However, perhaps surprisingly, lenses have seldom actually been used to implement updatable views in databases. Bohannon, Pierce and Vaughan proposed an approach to updatable views called relational lenses, but to the best of our knowledge this proposal has not been implemented or evaluated to date. We propose incremental relational lenses, that equip relational lenses with change-propagating semantics that map small changes to the view to (potentially) small changes to the source tables. We also present a language-integrated implementation of relational lenses and a detailed experimental evaluation, showing orders of magnitude improvement over the non-incremental approach. Our work shows that relational lenses can be used to support expressive and efficient view updates at the language level, without relying on updatable view support from the underlying database.

cs.PL↗