Interactive Serverless Compute.md · GitHub

Overview

Serverless compute is emerging as an attractive cloud computing model that lets developers focus only on the core applications, building them as small, fine-grained workloads without having to worry about building and/or managing the infrastructure they run on.
Cloud providers dynamically provision, deploy, patch, and monitor the infrastructure and its resources (e.g., compute, storage, memory, and network) for these workloads; with tenants only paying for the resources they consume at millisecond increments
They generally put a strict limit on the compute time and resource that can be consumed by a single workload, in order to ensure that they can easily deploy and scale each workload without impacting the availability of other workloads.

To compare the performance of λ−NIC versus existing serverless compute frameworks, we select OpenFaaS as the baseline framework because it is the most favorited open-source serverless framework and closely resembles serverless infrastructure
We evaluate three types of workloads: short, simple, and long
Short workloads with no dependency
Simple server that responds to static contents
Longer workloads involving processing on larger pieces of data (e.g., image or stream processing)
The data required for these workloads is generally larger than a single packet and is stored in the DRAM

This research was supported by The Stanford Platform Lab.
The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official λ−NIC Bare-Metal Container Simple Server
Latency (ms) for each workload
Work completion time (s) for 10,000 requests sent across 56 threads while context switching
Resource usage when running image transformer