Green Software Engineering Principles — Vocabulary
Learn vocabulary for the Green Software Foundation principles: carbon efficiency, energy efficiency, and hardware efficiency.
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What are the three core principles of Green Software Engineering?
Green Software Foundation (greensoftware.foundation) three pillars: Carbon Efficiency (emit the minimum amount of carbon to deliver your function — demand and spatial shifting, renewable energy), Energy Efficiency (use the minimum energy — algorithmic efficiency, resource utilization), Hardware Efficiency (extend hardware lifespan, avoid unnecessary hardware creation — embodied carbon).
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What is 'software carbon intensity' (SCI) in green software vocabulary?
SCI = (E x I) + M per R. E = energy consumed, I = carbon intensity of energy, M = embodied carbon of hardware (amortized), R = functional unit (per API call, per user, per transaction). SCI allows comparing carbon efficiency: 'Our API has an SCI of 5gCO2eq per 1000 requests. After optimization, it dropped to 3gCO2eq per 1000 requests.'
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What is 'right-sizing' in cloud sustainability vocabulary?
Right-sizing: if your application uses 10% of a large instance, switch to a smaller one. Wasted compute capacity = wasted energy = unnecessary carbon emissions. Cloud providers offer tools (AWS Compute Optimizer, Azure Advisor) to identify over-provisioned resources. Right-sizing is usually the highest-impact low-effort green action.
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What is 'embodied carbon' in green IT vocabulary?
Embodied carbon: manufacturing a laptop or server requires significant energy (often from fossil fuels), producing CO2 before the device is ever turned on. For a laptop, embodied carbon may represent 80% of its lifetime carbon footprint. Extending hardware lifespan, buying refurbished equipment, and avoiding unnecessary hardware procurement reduces embodied carbon significantly.
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What is a 'carbon budget' in software project sustainability vocabulary?
Carbon budget for software: 'This new ML feature has a carbon budget of X gCO2eq per day. If the initial design exceeds this, we must optimize before shipping.' Treating carbon as a constrained resource — like memory or cost — makes sustainability a first-class design constraint rather than an afterthought.
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Code Review Comment: 'This function is a bit verbose. Could we consider optimizing it for fewer API calls to reduce the overall network traffic? It seems like a potential area for Green Software Engineering consideration.' – Alex (Lead Engineer). What does 'reducing overall network traffic' primarily relate to in the context of Green Software Engineering?
This question tests understanding of a key aspect – network traffic. Reducing API calls directly addresses the energy consumption associated with data transmission, which is a core component of Green Software Engineering. Options A and C are related to code quality but don't directly address reducing resource usage from network requests. Option D concerns scaling, not minimizing data transfer.
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Slack Message: 'Just ran the new CI pipeline – it's using almost twice as much GPU time than the previous build! Need to investigate.' – Ben (DevOps Engineer). In this scenario, what does 'GPU time' most directly refer to in terms of Green Software Engineering?
This focuses on resource consumption. 'GPU time' directly relates to the power consumed by a GPU during intensive computational processes like compilation and testing – this is a significant factor in Green Software Engineering. Option A refers to code volume, C to pipeline speed, and D to notification frequency.
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PR Description: 'Refactoring the user profile service to utilize a more efficient data serialization format. Aiming for a 30% reduction in payload size during API calls.' – Chloe (Backend Developer). What is the primary goal of reducing the 'payload size' in this context?
This question explores payload size – reducing it directly minimizes data transferred over the network. This reduction in data transfer is a fundamental principle of Green Software Engineering, as less data sent means less energy consumed by servers and networks. Options A, C, and D are irrelevant to this specific PR description.
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Standup Update: 'We're experimenting with serverless functions for our image resizing service. Initial estimates suggest a significant reduction in idle compute time – potentially saving us around 15% on cloud costs.' – David (Software Engineer). What does 'idle compute time' relate to concerning Green Software Engineering?
This scenario highlights idle compute time – this is a crucial concept in Green Software Engineering. Servers continue to consume power even when not actively processing requests. Reducing idle time directly translates to reduced energy consumption and cost savings, aligning with the principles of resource efficiency. Options A, C, and D are related to active processing or throughput.
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Code Review Comment: 'I noticed you're using a synchronous database call here. Consider switching to an asynchronous approach to avoid blocking the main thread and potentially improve responsiveness – this can lead to more efficient resource utilization.' – Emily (Senior Developer). What does 'blocking the main thread' relate to within the context of Green Software Engineering?
This question tackles asynchronous programming and its impact on resource utilization. Blocking a thread means that the program must wait for an operation to finish before continuing, preventing other tasks from running concurrently and leading to inefficient use of CPU cycles and energy. Asynchronous operations allow for non-blocking execution, improving efficiency.
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Code Review Comment: 'This function is a bit verbose. Could we consider optimizing it for fewer API calls to reduce the overall network traffic? It seems like a potential area for Green Software Engineering consideration.' – Alex (Lead Engineer). What does 'reducing overall network traffic' primarily relate to in the context of Green Software Engineering?
This question tests understanding of a key aspect – network traffic. Reducing API calls directly addresses the energy consumption associated with data transmission, which is a core component of Green Software Engineering. Options A and C are related to code quality but don't directly address reducing resource usage from network requests. Option D concerns scaling, not minimizing data transfer.
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Slack Message: 'Just ran the new CI pipeline – it's using almost twice as much GPU time than the previous build! Need to investigate.' – Ben (DevOps Engineer). In this scenario, what does 'GPU time' most directly refer to in terms of Green Software Engineering?
This focuses on resource consumption. 'GPU time' directly relates to the power consumed by a GPU during intensive computational processes like compilation and testing – this is a significant factor in Green Software Engineering. Option A refers to code volume, C to pipeline speed, and D to notification frequency.
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PR Description: 'Refactoring the user profile service to utilize a more efficient data serialization format. Aiming for a 30% reduction in payload size during API calls.' – Chloe (Backend Developer). What is the primary goal of reducing the 'payload size' in this context?
This question explores payload size – reducing it directly minimizes data transferred over the network. This reduction in data transfer is a fundamental principle of Green Software Engineering, as less data sent means less energy consumed by servers and networks. Options A, C, and D are irrelevant to this specific PR description.
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Standup Update: 'We're experimenting with serverless functions for our image resizing service. Initial estimates suggest a significant reduction in idle compute time – potentially saving us around 15% on cloud costs.' – David (Software Engineer). What does 'idle compute time' relate to concerning Green Software Engineering?
This scenario highlights idle compute time – this is a crucial concept in Green Software Engineering. Servers continue to consume power even when not actively processing requests. Reducing idle time directly translates to reduced energy consumption and cost savings, aligning with the principles of resource efficiency. Options A, C, and D are related to active processing or throughput.
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Code Review Comment: 'I noticed you're using a synchronous database call here. Consider switching to an asynchronous approach to avoid blocking the main thread and potentially improve responsiveness – this can lead to more efficient resource utilization.' – Emily (Senior Developer). What does 'blocking the main thread' relate to within the context of Green Software Engineering?
This question tackles asynchronous programming and its impact on resource utilization. Blocking a thread means that the program must wait for an operation to finish before continuing, preventing other tasks from running concurrently and leading to inefficient use of CPU cycles and energy. Asynchronous operations allow for non-blocking execution, improving efficiency.
What will I practise in "Green Software Engineering Principles — Vocabulary"?
Learn vocabulary for the Green Software Foundation principles: carbon efficiency, energy efficiency, and hardware efficiency.
How many exercises are in this module?
This module has 15 multiple-choice exercises, each with instant feedback and a full explanation of the correct answer.
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What happens if I choose the wrong answer?
You'll immediately see which answer was correct, plus a full explanation covering the vocabulary and reasoning behind it — mistakes are where most of the learning happens.
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No. Progress is tracked only for your current visit; reloading or leaving the page resets the counter. This keeps the exercise simple and account-free.
Where can I find more Green IT exercises?
Browse the full Green IT hub for related drills, or check the "Next up" link below to continue with a connected topic.
How is this different from reading an article on the same topic?
Articles explain vocabulary and concepts in prose; this exercise tests and reinforces that vocabulary through active recall with immediate feedback — the two work best together.
Who writes these exercises?
Every exercise is written by the CoderSlingo team, drawing on real workplace English used in IT roles, then reviewed for accuracy and clarity.