Powerline

### Prize category

Best Plugin

### Overview

Given the sporadic nature of CPU utilization, and how current carbon calculations depend heavily on this metric, I propose Powerline, a plugin that uses polynomial regression and other techniques to "even out" CPU utilization statistics. CPU utilization measurements vary by large amounts over short periods of time, and since only snapshots of the data are used for the final calculations, anomalies (more often than not spikes) could result in a false estimation of true utilization. Through smoothing out CPU utilization numbers in a way that more accurately reflects the actual usage without needing to conduct measurements more frequently, I hope that Powerline will allow for more accurate environmental impact reporting.

### Questions to be answered

_No response_

### Have you got a project team yet?

I am working on this project alone

### Project team

_No response_

### Terms of Participation

- [X] I agree to the hackathon [Rules & Terms](https://github.com/Green-Software-Foundation/hack/wiki/Rules-and-Terms) and [Code of Conduct](https://github.com/Green-Software-Foundation/hack/wiki/Code-of-Conduct)

### Project Submission

### Summary
Powerline is a plugin designed to enhance the accuracy of environmental impact reporting by addressing the sporadic nature of CPU utilization measurements. Leveraging polynomial regression and other advanced techniques, Powerline evens out CPU utilization statistics, providing a more accurate reflection of actual usage. By mitigating the risk of false estimations, Powerline aims to improve the reliability of carbon footprint calculations.

### Problem
The accuracy of CPU utilization data collected from server data sensors is important for reliable environmental impact reporting and efficient resource allocation. Traditional methods often overlook the sporadic nature of this data, resulting in inaccuracies that compromise the integrity of sustainability assessments. Fluctuations in CPU utilization over time pose a significant challenge, as they can skew calculations and misrepresent the true environmental footprint of software operations. Addressing this problem is essential for organizations striving to make informed decisions regarding energy consumption, carbon emissions, and overall environmental stewardship. By developing solutions that enhance the accuracy of CPU utilization data, we can ensure more precise and meaningful assessments of software's environmental impact, leading to more effective sustainability strategies and initiatives.

### Application
Powerline functions by enhancing the accuracy of CPU utilization data through a 3-step process. Initially, it moderates large anomalies in the input data to ensure consistency. Next, Powerline interpolates between timestamps to increase data density. The crucial stage involves employing polynomial regression with a curve generated from the original data points to approximate the CPU utilization curve, replacing the interpolated data points with curve values for improved accuracy. These adjustments are reflected in the output markup file, demonstrating how Powerline transforms raw CPU utilization data into more precise representations. By leveraging these techniques, Powerline enables organizations to obtain more reliable metrics for environmental impact reporting, facilitating better-informed decisions regarding resource optimization and sustainability initiatives.

### Prize category
Best Plugin

### Judging Criteria
In terms of Overall Impact, Powerline enhances the accuracy of CPU utilization data, thereby improving the reliability of environmental impact reporting. This has the potential to drive meaningful change in sustainability efforts by providing users of the IF with more precise metrics for decision-making and resource allocation. Secondly, Powerline presents an Opportunity to measure different software ecosystems and environments effectively. Its modular design in the framework allows for seamless integration with various platforms and services, enabling widespread adoption and application across diverse infrastructures. Lastly, Powerline adheres to the Unix philosophy and micro-model architecture by focusing on one specific task—enhancing CPU utilization data—while maintaining compatibility with other plugins. This modularity ensures that Powerline plays well with existing frameworks and contributes to the overall efficiency and effectiveness of environmental impact assessments.

### Video
https://youtu.be/Pa4nv86B5Fg

### Artefacts
https://github.com/szm100/powerline

### Process
The development of Powerline involves a systematic approach focused on addressing the challenges associated with fluctuating CPU utilization data. I began by thoroughly analyzing the existing methods of CPU utilization measurement and identifying the limitations that led to inaccuracies in environmental impact reporting. I then designed and implemented a three-step process within Powerline to clean and enhance the input data. This process included anomaly moderation, timestamp interpolation, and polynomial regression for curve approximation. Throughout the development phase, I prioritized modularity and compatibility with existing frameworks, ensuring that Powerline could seamlessly integrate into various software ecosystems. Additionally, testing and validation procedures were conducted to verify the accuracy and reliability of the solution.

### Inspiration
The inspiration behind developing Powerline stemmed from recognizing the critical role of accurate data in driving informed decision-making, particularly in the context of environmental impact assessment. Data accuracy has a huge impact on sustainability efforts. Witnessing the discrepancies and uncertainties caused by fluctuating CPU utilization data highlighted the need for a solution that could enhance data accuracy and reliability. By harnessing the power of data science techniques such as polynomial regression, I aimed to empower organizations with more dependable metrics for environmental reporting and resource optimization.

### Challenges
As the sole developer and a student with limited prior experience in JavaScript, I encountered several challenges during the development of Powerline. Initially, navigating the intricacies of JavaScript posed difficulties, particularly in understanding and implementing module imports effectively. Overcoming these challenges required extensive self-learning and experimentation to grasp fundamental concepts and techniques. Despite these obstacles, the learning process was invaluable, as it not only enhanced my technical skills but also reinforced my determination to overcome challenges and deliver a working solution.

### Accomplishments
I am most proud of my perseverance and resilience throughout the development process of Powerline, especially in the face of numerous setbacks and failures. Despite encountering multiple challenges, including technical hurdles and initial failures of the plugin, I remained steadfast in my commitment to finding solutions and pushing forward. Each setback served as a valuable learning opportunity, fueling my determination to iterate and improve the plugin until it met the desired standards of accuracy and reliability.

### Learnings
Through the hacking process, I gained invaluable insights into the complexities of software development and the challenges inherent in delivering effective solutions. I learned firsthand the importance of thorough planning and preparation before diving into implementation, as well as the need for adaptability and resilience when faced with unexpected obstacles. Additionally, I learned that initial ideas and intentions may not always translate seamlessly into practical solutions, highlighting the importance of iteration and continuous improvement. Overall, the hacking experience underscored the importance of a systematic approach to problem-solving, emphasizing the value of both strategic foresight and hands-on experimentation in achieving success.

### What's next?
Moving forward, I hope my solution, Powerline, can contribute long-term to the Impact Framework ecosystem by serving as a inspiration for further innovation in leveraging data science for sustainability initiatives. By demonstrating the effectiveness of data-driven approaches in improving the reliability of environmental data, Powerline can inspire others to explore similar techniques and develop innovative solutions for green software development. Additionally, I aim to use Powerline as a springboard for future projects in this area, building upon the knowledge and experience gained during its development to tackle broader sustainability challenges and drive positive environmental impact. Ultimately, I believe that Powerline has the potential to foster a culture of data-driven decision-making and innovation within the Impact Framework ecosystem, paving the way for more effective and sustainable practices in software development and beyond.