Serverless applications are composed of functions triggered by events. Data stores are a common source of event triggers in the cloud, even beyond serverless, such as in Kubernetes. We find trigger latency, the time from event generation to function invocation, to take up to 62% of execution time for common serverless applications. Even though event triggers play a crucial role in serverless performance, the mechanisms driving these triggers are ill-understood. In this paper, we analyze data store trigger mechanisms, define the features that make up these mechanisms, and characterize their performance with TriggerPerf, a benchmarking tool for data store triggers. We implement TriggerPerf on three AWS data stores with built-in trigger support: S3, DynamoDB, and AuroraDB. With TriggerPerf, we demonstrate significant latency, scalability, and elasticity bottlenecks across these data stores. We observe that the trigger latency of AWS data stores is up to$100 \times$higher compared to a reference etcd data store. Moreover, the median tail latency of S3 and AuroraDB is 10x higher when under high load, unlike DynamoDB. The observed variability in performance patterns significantly impacts the reliability of serverless and distributed systems that depend on them, highlighting the critical need for further research into the underlying mechanisms. The tool is open-sourced and is available at https://github.com/atlarge-research/trigger-perf.

Resource managers such as Kubernetes are rapidly evolving to support low-latency and scalable computing paradigms such as serverless and granular computing. As a result, Kubernetes supports dozens of workload deployment models and exposes roughly 1,600 configuration parameters. Previous work has shown that parameter tuning can significantly improve Kubernetes' performance, but identifying which parameters impact performance and should be tuned remains challenging. To help users optimize their Kubernetes deployments, we present Columbo, an offline reasoning framework to detect and resolve performance bottlenecks using configuration parameters. We study Kubernetes and define its workload deployment pipeline of 6 stages and 26 steps. To detect bottlenecks, Columbo uses an analytical model to predict the best-case deployment time of a workload per pipeline stage and compares it to empirical data from a novel benchmark suite. Columbo then uses a rule-based methodology to recommend parameter updates based on the detected bottleneck, deployed workload, and mapping of configurations to pipeline stages. We demonstrate that Columbo reduces workload deployment time across its benchmark suite by 28% on average and 79% at most. We report a total execution time decrease of 17% for data processing with Spark and up to 20% for serverless workflows with OpenWhisk. Columbo is open-source and available at https://github.com/atlarge-research/continuum/tree/columbo.

We live in a data-centric world where many fundamental shifts in our daily lives are powered by Big Data. To meet the performance, cost, and energy demands of modern Big Data systems, there have been significant technological and engineering breakthroughs in the field of storage systems with novel hardware innovations and software architectures. Nevertheless, a typical computer science student still associates data storage solely with the technology of hard disk drives (HDD), which was invented six decades ago. One key reason for this association is the lack of courses on modern storage systems in computer science education curricula. In this paper, we make a concerted effort to summarize the state of storage systems education across universities, popular textbooks, and policies (ACM/IEEE). We make a case that the storage systems should have its own home course in educational curricula. We report on our experience of designing and offering one such course at the Vrije Universiteit, Amsterdam over the past four years. We further contribute to the educational material in this direction by making the course lectures, video recordings, assignments, and grading framework freely and openly accessible at https://atlarge-research.com/courses/storage-systems-vu.

As the next generation of diverse workloads like autonomous driving and augmented/virtual reality evolves, computation is shifting from cloud-based services to the edge, leading to the emergence of a cloud-edge compute continuum. This continuum promises a wide spectrum of deployment opportunities for workloads that can leverage the strengths of cloud (scalable infrastructure, high reliability) and edge (energy efficient, low latencies). Despite its promises, the continuum has only been studied in silos of various computing models, thus lacking strong end-to-end theoretical and engineering foundations for computing and resource management across the continuum. Consequently, devel-opers resort to ad hoc approaches to reason about performance and resource utilization of workloads in the continuum. In this work, we conduct a first-of-its-kind systematic study of various computing models, identify salient properties, and make a case to unify them under a compute continuum reference architecture. This architecture provides an end-to-end analysis framework for developers to reason about resource management, workload distribution, and performance analysis. We demonstrate the utility of the reference architecture by analyzing two popular continuum workloads, deep learning and industrial IoT. We have developed an accompanying deployment and benchmarking framework and first-order analytical model for quantitative reasoning of continuum workloads. The framework is open-sourced and available at https://github.com/atlarge-research/continuum.