PMA is a MAPE-K-based self-adaptive system that proactively adapts microservices at runtime. PMA is a generic, holistic, adaptive, proactive system that aims to manage microservices through horizontal auto-scaling. It offers three forecasting strategies: univariate, multivariate, and MPS. These strategies estimate the usage of performance metrics for a particular microservice at runtime. Based on this estimation, the system decides whether an adaptation is needed. If necessary, the adaptation consists of executing scaling-in/scaling-out operations.
- Self-Adaptive Scaling: PMA continuously monitors your microservices application and dynamically adjusts the number of instances based on workload demand, ensuring optimal performance and resource utilization.
- Proactive Optimization: By analyzing various metrics such as CPU usage, memory consumption, and incoming traffic, PMA anticipates scalability needs and proactively scales resources before performance degradation occurs.
- Customizable Policies: Tailor scaling policies to match your application's unique requirements. Define thresholds and triggers to initiate scaling actions, providing fine-grained control over resource allocation.
- Easy Integration: With a simple setup process and intuitive configuration options, PMA seamlessly integrates into your existing microservices environment, requiring minimal effort for implementation and maintenance.
To install PMA, follow these steps:
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Clone the repository to your local machine:
git clone https://github.com/yourusername/PMA.git -
Navigate to the project directory:
cd PMA -
Install the required dependencies using pip:
pip3 install -r requirements.txt
After installing PMA, you can use it to auto-scale your microservices applications. Follow these steps to set up auto-scaling:
-
Configure PMA to monitor your microservices application by editing the configuration file (
mape-k.py) with relevant metrics thresholds and scaling policies. PMA can also be customized to adapt to your specific microservices application and scaling requirements. You can modify the configuration file (mape-k.yaml) to set custom thresholds and scaling policies tailored to your application's needs. -
Run PMA using the following command:
python3 mape-k.py -
PMA will continuously monitor the metrics of your microservices application and automatically adjust the number of instances based on the defined scaling policies.
Contributions to PMA are welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request on GitHub.
This project is licensed under the Creative Commons License - see the LICENSE file for details.
For any questions or inquiries about PMA, feel free to contact us at wellisonraulm@gmail.com.
Optimize your microservices application with PMA's self-adaptive proactive auto-scaling! 🚀