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Track Your Tasks, Track Your Impact: A Carbon-Conscious Task Management Approach #144

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nikethanrai opened this issue Apr 6, 2024 · 0 comments
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contribution-project registered A project which has been registered with the GSF submitted The project team has submitted their solution.

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@nikethanrai
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nikethanrai commented Apr 6, 2024

Prize category

Best Plugin

Overview

A Carbon-Conscious Task Management Approach:

This tool tackles the issue head-on by:

  1. Monitoring Local Applications: It tracks applications running on your device, gathering data on:

    • Total Uptime: How long each app has been running

    • Memory Consumption: The amount of system resources being used

  2. Carbon Footprint Estimation: Based on the collected data and established industry models, the tool estimates the carbon footprint associated with each application's resource usage.

  3. Transparency Through Teams Integration: The top 10 applications with the highest estimated carbon emissions are displayed on Microsoft Teams, providing clear insights into your digital footprint.

Benefits Beyond Awareness:

  • Actionable Insights: By seeing the ranked list, users can identify apps they might be leaving open unnecessarily and make informed decisions about closing them.

  • Data-Driven Optimization: Understanding the carbon impact of different applications helps users optimize their resource usage and potentially improve overall system performance.

Questions to be answered

Is there something similar already being done?

Have you got a project team yet?

Yes and we aren't recruiting

Submission

Summary

This tool monitors applications running on your device, collecting data on their total uptime, memory consumption,
and estimating their carbon footprint. It provides transparency by integrating with Microsoft Teams to display the
top 10 applications with the highest estimated carbon emissions, offering insights into your digital footprint.
We have used two IF provided plugins

  1. E-Mem: To calculate memory/energy using memory utilisation and memory capacity
  2. SCI-O: To calculate carbon-operational emission using the memory/energy released from the processes.

image

Problems

The tool addresses several problems related to application monitoring and environmental impact:

  1. Lack of Awareness: Users often run numerous applications simultaneously on their devices without fully understanding their resource consumption and environmental impact.
    This tool provides insights into the resource usage and carbon footprint of each application, raising awareness among users.

  2. Resource Optimization: Many applications consume excessive system resources, leading to performance issues and
    unnecessary energy consumption. By tracking memory consumption and uptime, the tool helps users identify resource-intensive applications
    and optimize their usage to improve device performance and reduce energy consumption.

  3. Environmental Impact: The carbon footprint estimation feature highlights the environmental impact of running various applications.
    It enables users to make more environmentally conscious choices by identifying and minimizing the usage of high-emission applications.

Application

The solution is a monitoring tool that tracks applications on a user's device, recording their total uptime and memory consumption.
It estimates the carbon footprint associated with each application's resource usage. This estimation is based on industry models and data collected by the tool. The Impact Framework comes into the picture and helps us to calculate carbon emission using the SCI-O plugin. So this plugin takes memory/energy as its input and returns the carbon in gCO2 per equivalent. Additionally, it integrates with Microsoft Teams to display the top 10 applications with the highest estimated carbon emissions, providing transparency and insights into the user's digital footprint. Overall, the solution helps users understand their application usage, optimize resource consumption, and make more environmentally conscious choices.

Prize Category

Best Contribution

Judging Criteria

The solution falls under the category of "Best Contribution" It addresses issues related to understanding and mitigating the environmental impact of digital activities, specifically focusing on monitoring application resource usage and estimating carbon emissions. By providing insights into the carbon footprint of individual applications and integrating with collaboration platforms like Microsoft Teams for transparency, the solution encourages users to adopt more sustainable digital practices.

Video

https://www.youtube.com/watch?v=UEVs3_mA_ME

Artefacts

https://github.com/nikethanrai/CarbonHack.git

Inspiration

The inspiration behind the development of this monitoring tool stems from a collective recognition of the increasingly significant environmental impact of digital technologies. As society becomes more reliant on digital devices and applications, there's a growing need to raise awareness about the carbon footprint associated with our digital activities and empower individuals to make more sustainable choices. We were inspired by the potential for technology to not only track and analyze application usage but also to actively contribute to reducing environmental harm. By providing users with clear insights into the carbon emissions of their digital activities, the tool seeks to inspire individuals to take action towards minimizing their environmental footprint. Ultimately, the goal is to foster a culture of digital responsibility and inspire positive change towards a more sustainable future.

Challenges

Major challenges were finding the relevant plugins and mapping them with the available data. We also faced some challenged while integrating ms teams.

Accomplishments

What we are most proud of we could build something that can be used with just one click and if setup as a scheduler, not even that is required. The carbon emissions we calculated were an eye opener in terms of how much carbon is generated unknowingly and we could build something to nudge the user to reduce them.

Whats next?

We would love to make the data more accurate and eventually add input metrics like CPU consumption as well.

Project team

@abhibajaj121831

Terms of Participation

@nikethanrai nikethanrai added the draft This project is in draft mode and has not been submitted label Apr 6, 2024
@russelltrow russelltrow added plug-in-project registered A project which has been registered with the GSF submitted The project team has submitted their solution. contribution-project and removed draft This project is in draft mode and has not been submitted plug-in-project labels Apr 8, 2024
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