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README

Microscaling Engine

Our Microscaling Engine provides automation, resilience and efficiency for microservice architectures. You can use our Microscaling-in-a-Box site to experiment with microscaling. Or visit microscaling.com to find out more about our product and Microscaling Systems.

This project is no longer being developed. As an alternative we recommend you take a look at Keda.

Docker Image

Build

Go 1.6 & 1.7

Microscaling Engine is under development, so we're not making any promises about forward compatibility, and we wouldn't advise running it on production machines yet. But if you're keen to get it into production we'd love to hear from you.

Schedulers

Microscaling Engine will integrate with all the popular container schedulers. Currently we support

• Docker API
• Marathon
• Kubernetes

Support for more schedulers is coming soon. Let us know if there is a particular scheduler you wish us to support.

Metrics

Currently we support scaling a queue to maintain a target length. Support for more metrics is coming soon.

2 queue scaling algorithms are available.

• SimpleQueue - scales containers up or down by one according to whether the queue is too long or too short.
• Queue - uses control theory to prevent oscillation.

Queue Types

• SQS - blog post with more details coming soon.
• NSQ - see this blog post for more details.
• Azure storage queues - this blog post describes using the Azure queue as the metric while running microscaled tasks on DC/OS.

Support for more message queues is coming soon. Let us know if there is a particular queue you wish us to integrate with.

Running

The easiest way to run Microscaling-in-a-box is to follow the instructions. The docker run command pulls the latest image of this code from Docker hub.

Running with label-based config

Get scaling parameters from your image metadata by configuring them with the following labels:

• com.microscaling.is-scalable
• com.microscaling.priority
• com.microscaling.max-delta
• com.microscaling.min-containers
• com.microscaling.max-containers

Download the compose file and add the following environment variable to the environment settings for the microscaling image:

MSS_CONFIG=LABEL

Building from source

If you want to build and run your own version locally:

• Clone this repo
• Build your own version of the Docker image DOCKER_IMAGE=<your-image> make build
• Specify -it <your-image> instead of -it microscaling/microscaling:latest on docker run so that it picks up your version of the image

Licensing

Microscaling Engine is licensed under the Apache License, Version 2.0. See LICENSE for the full license text.

Contributing

We'd love to get contributions from you! Please see CONTRIBUTING.md for more details.

Contact Us

We'd love to hear from you at hello@microscaling.com or on Twitter at @microscaling. And we welcome new issues or pull requests!

Documentation

Overview

Microscaling is a package that monitors demand for resource in a system and then scales and repurposes containers, based on agreed "quality of service" contracts, to best handle that demand within the constraints of your existing VM or physical infrastructure (for v1).

Microscaling is defined to optimize the use of existing physical and VM resources instantly. VMs cannot be scaled in real time (it takes several minutes) and new physical machines take even longer. However, containers can be started or stopped at sub-second speeds, allowing your infrastructure to adapt itself in real time to meet system demands.

Microscaling is aimed at effectively using the resources you have right now - your existing VMs or physical servers - by using them as optimally as possible.

The microscaling approach is analogous to the way that a router dynamically optimises the use of a physical network. A router is limited by the capacity of the lines physically connected to it. Adding additional capacity is a physical process and takes time. Routers therefore make decisions in real time about which packets will be prioritized on a particular line based on the packet's priority (defined by a "quality of service" contract).

For example, at times of high bandwidth usage a router might prioritize VOIP traffic over web browsing in real time.

Containers allow microscaling to make similar "instant" judgements on service prioritisation within your existing infrastructure. Routers make very simplistic judgments because they have limited time and cpu and they act at a per packet level. Microscaling has the capability of making far more sophisticated judgements, although even fairly simple ones will still provide a significant new service.