Interactive Serverless Compute.md · GitHub
- 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