Natalie Carl

Tim C. Rese, Nils Japke, Diana Baumann et al.

The rapid growth of spatiotemporal data volumes needs to be handled by database systems capable of efficiently managing and querying such data. Existing systems such as PostGIS, SpaceTime, and MobilityDB offer partial solutions but differ widely in scope and performance. Also, first spatiotemporal benchmarks provide valuable insights but are limited in scope and, to our knowledge, no application-centric benchmarking suite exists. In this paper, we propose GeoBenchr, an open-source, application-centric benchmarking suite for spatiotemporal platforms. GeoBenchr enables comprehensive evaluation across diverse datasets, query types, and workload patterns, reflecting realistic use cases from domains such as cycling, aviation, and maritime tracking. We use our GeoBenchr prototype to evaluate several system aspects including scalability, configuration impact, and cross-platform performance comparison. Our results highlight the importance of application-centric benchmarking in selecting suitable spatiotemporal database systems for real-world scenarios.

Trever Schirmer, Aris Wiegand, Lucca di Benedetto et al.

With the ever-increasing usage of serverless computing in both industry and academia, it is essential to understand the mechanisms that power the underlying platforms. As serverless is more than ten years old, there are different platforms with vastly different approaches. We show that, next to the traditional and popular platforms, a second generation of serverless platform has emerged. While first-generation platforms are based on containerized, centralized execution, the new generation leverages lightweight isolates and edge deployment. This evolution reduces warm request latency from approximately 40 ms to around 10 ms and reduces cold starts to an afterthought, but limits the execution environment. In this paper, we gather and analyze all publicly available information to provide detailed insights into the underlying architecture of seven platforms and then run a microbenchmark-based evaluation totaling more than 38 million function calls to gain a deeper understanding their performance.

Niklas Kowallik, Natalie Carl, Leon Pöllinger et al.

Function-as-a-Service (FaaS) platforms provide scalable and cost-efficient execution but suffer from increased latency and resource overheads in complex applications comprising multiple functions, particularly due to double billing when functions call each other. This paper presents Provuse, a transparent, platform-side optimization that automatically performs function fusion at runtime for independently deployed functions, thereby eliminating redundant function instances. This approach reduces both cost and latency without requiring users to change any code. Provusetargets provider-managed FaaS platforms that retain control over function entry points and deployment artifacts, enabling transparent, runtime execution consolidation without developer intervention. We provide two implementations for this approach using the tinyFaaS platform as well as Kubernetes, demonstrating compatibility with container orchestration frameworks. An evaluation shows consistent improvements, achieving an average end-to-end latency reduction of 26.33% and a mean RAM usage reduction of 53.57%. These results indicate that automatic function fusion is an effective platform-side strategy for reducing latency and RAM consumption in composed FaaS applications, highlighting the potential of transparent infrastructure-level optimizations in serverless systems.