Pagurus

Introduction

  Pagurus is a container management system for serverless computing. It targets on reusing idle containers to help other functions alleviate the system-level cold startups. In Pagurus, contaienrs that created by it owner function is called Executor Container. Once the executor containers are identified as idle (e.g., not serving queries for 5 minutes), Pagurus will replace it with a Zygote Container. The Zygote Container is created from an image that installs the shared packages of all the to-be-helped functions. Once the to-be-helped function would experience the cold startup, Pagurus will fork a zygote container for it and avoid the time-consuming container creation, software environment setup, and dependencies preparation. The forked container in Pagurus is called Helper container.

Getting Started

  We provide a quick setup script to deploy Pagurus in your machine. To deploy Pagurus and prepare the related software dependicies:

git clone https://github.com/lzjzx1122/Pagurus.git
./install.sh

  This shell script will install all dependencies and prepare the run-time environment. Then start Pagurus by the following command

• Run the inter_controller:

python3 inter_controller/inter_controller.py bench

, where the bench can be aws or azure.

• Run the intra_controller:

python3 intra_controller/intra_controller.py net_port max_lender_num default_zygote_time option bench 

For example, python3 intra_controller/intra_controller.py 5001 1 60 pagurus aws.

  Now Pagurus is successfully started and ready for serving function queries.

Run Aws and Azure Benchmark.

You can enter aws or azure directory to run the experiments in our paper:

python3 run_experiment.py <test_number>

This script will help you open the intra_controller and inter_controller, and send some function invocations to inter_controller. Therefore, you don't need to open intra_controller and inter_controller with this script.

Applications and Functions

  We already provide several function benchmarks in Pagurus, such as FunctionBench, FaaSProfiler, simple version of aws-example-applications, and Azure trace mapping.

  Each function should be define with a entry point with "def main(param):" . The packages needed for the function should also assigned in a requirements.txt along with the main.py or .zip file. These assigned packages and versions are maintained here in the json format like:

"cer_lambda": {
        "requests": "2.25.1",
        "xlsxwriter": "1.4.0",
        "pandas": "0.24.2",
        "numpy": "1.16.6"
    },

  Besides the functions, Pagurus also support the invocation of an application. The application contains several functions and follow a sequence or parallel logic to construt a complex business. It should be noticed that the functions and applications must be created and defined before being invoked in Pagurus.

Detailed Concepts and Related Components

  Pagurus contains three main components as shown in the figure: intra-function container manager, inter-function container scheduler, and sharing-aware function balancer. Serverless functions are running in Docker containers. The communication between them are thruogh REST APIs.

• Proxy in Docker Container
• Inter-Function Container Scheduler
• Intra-Function Container Manager
• Sharing-aware Function Balancer
• CouchDB Server

  Besides these components of Pagurrus, we also provide two additional prewarm-based policy in Pagurus: OpenWhisk-based Prewarm and SOCK-based Prewarm. You can find their prewarm manager here.