The growth of serverless computing has led to a widespread reexamination of the cloud software upon which it is based. In parallel, the flattening of single core performance has led to a resurgence of interest in manycore systems, trading absolute performance for system throughput, an appropriate match for the serverless paradigm. However, the combination of deep cloud system software stacks and slow hardware simulation techniques has limited the exploration of serverless-native CPUs. We argue that the RISC-V ecosystem offers an opportunity to tackle the intersection of these topics. We present an exploratory comparison of several RISC-V SoC configurations and commercial products running serverless workloads. We find that the RISC-V cores offer reasonable performance, but more importantly provide researchers the ability to run more realistic software workloads. This allows for meaningful exploration of the interactions between system software, serverless workloads, and specialized hardware.

Function as a Service (FaaS) and the associated serverless computing paradigm alleviates users from resource management and allows cloud platforms to optimize system infrastructure under the hood. Despite significant advances, FaaS infrastructure still leaves much room to improve performance and resource efficiency. We argue that both higher performance and resource efficiency are possible - while maintaining secure isolation - if we are willing to revisit the FaaS programming model and system software design. We propose Dandelion, a clean-slate FaaS system that rethinks the programming model by treating serverless functions as pure functions, thereby explicitly separating computation and I/O. This new programming model enables a lightweight yet secure function execution system. It also makes functions more amenable to hardware acceleration and enables dataflow-aware function orchestration. Our initial prototype of Dandelion achieves 45x lower tail latency for cold starts compared to Firecracker. For 95% hot function invocations, Dandelion achieves 5x higher peak throughput.

Serverless computing offers a number of advantages over conventional, Virtual Machine (VM) based deployments on the cloud, e.g., greater elasticity, simplicity of use and management, finer granularity billing, and rapid deployment and start up times. Naturally, there is a growing interest in exploring how to run applications in this new environment and data analytics is not an exception. Unfortunately, current serverless platforms are limited along several dimensions, which makes things quite difficult from the perspective of data analytics. In this paper we explore what serverless has to offer today, what is missing, and what can be done to make serverless a better computing platform in general and for data analytics in particular.

Function as a Service (FaaS) and the associated serverless computing paradigm alleviates users from resource management and allows cloud platforms to optimize system infrastructure under the hood. Despite significant advances, FaaS infrastructure still leaves much room to improve performance and resource efficiency. We argue that both higher performance and resource efficiency are possible --- while maintaining secure isolation --- if we are willing to revisit the FaaS programming model and system software design. We propose Dandelion, a clean-slate FaaS system that rethinks the programming model by treating serverless functions as pure functions, thereby explicitly separating computation and I/O. This new programming model enables a lightweight yet secure function execution system. It also makes functions more amenable to hardware acceleration and enables dataflow-aware function orchestration. Our initial prototype of Dandelion achieves 45× lower tail latency for cold starts compared to Firecracker. For 95% hot function invocations, Dandelion achieves 5× higher peak throughput.