Power Aware Container Placement in Cloud Computing with Affinity and Cubic Power Model

What this project is about

Optimizing container placement in cloud data centers by balancing:

• Power efficiency (using a realistic cubic CPU power model)
• Application–machine affinity & anti-affinity constraints
• Multi-resource awareness (CPU, memory, I/O, network)

Why it matters

Modern data centers spend ~60% of their operational cost on electricity. This project shows how smart scheduling can save power, improve performance, and make cloud computing more sustainable.

Key Highlights

First approach to integrate power, affinity/anti-affinity, and multiple resources in one framework Four-phase solution: preprocessing → greedy heuristics → combined optimization → Genetic Algorithm refinement Achieved up to:

• 26% lower total system cost
• 37% higher affinity payoff ratio
• 4% better affinity satisfaction vs. state-of-the-art methods

Tech & Tools

• Implementation in C++ & Python
• Evaluated on Google Cluster Dataset + synthetic workloads
• Compared against GCCS and HOP-CAPM

Python Libraries Used

• Numpy
• Matplotlib
• Pandas
• Mosek
• Make sure to install the above libraries before running the code using following command:

pip install numpy matplotlib mosek pandas

How to run an IPYNB file

• Inside Jupyter Notebook, you can run each cell of the IPYNB file by clicking on it and pressing Shift + Enter or by using the Run button in the toolbar.
• Alternatively, you can run the whole notebook in a single go by using the Run All option in the toolbar.

Link to the preprint version

https://arxiv.org/abs/2408.01176

Link to the paper

https://link.springer.com/article/10.1007/s00607-025-01566-0

Please cite our work as :

Sarkar, S., Sharma, N., Mittal, A. et al. Power aware container placement in cloud computing with affinity and cubic power model. Computing 107, 221 (2025). https://doi.org/10.1007/s00607-025-01566-0