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How can computer scientists design energy-efficient algorithms and architectures that reduce the carbon footprint of datacenters, give the urgent need to mitigate climate change?
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3 answers
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Michael’s Answer
There are two aspects to this concern, and both can be answered with being of greater efficiency.
Whenever we store an entry to a database, we spend X energy to save the item to the data center. Whenever we retrieve an item from a database, we spend X energy to find and return all the values attached to that entry. If we for example design our tables so that we only pull exactly what we need in the shortest amount of time possible, the energy cost of such will be lessened.
Excessive Joining of SQL tables, Tables with 200+ columns, Tables with millions of entries that will never realistically be read anytime soon. All of these help make every call more expensive, both in time and power.
The other half of this issue are the endpoints that call on these data centers. If we need to compare two Car objects stored in the database, we should only pull those two entries without including the various connected tables. If we are performing an action on all items in a list. Pull and update all entries in a single statement instead of doing single gets/updates in a foreach. Do we need to perform a stored procedure every 15 minutes, or can we find a way to only perform the procedure in response to another apps action?
Such adjustments will make for both more efficient code, and make a step to lessening the environmental impact such data centers have.
Whenever we store an entry to a database, we spend X energy to save the item to the data center. Whenever we retrieve an item from a database, we spend X energy to find and return all the values attached to that entry. If we for example design our tables so that we only pull exactly what we need in the shortest amount of time possible, the energy cost of such will be lessened.
Excessive Joining of SQL tables, Tables with 200+ columns, Tables with millions of entries that will never realistically be read anytime soon. All of these help make every call more expensive, both in time and power.
The other half of this issue are the endpoints that call on these data centers. If we need to compare two Car objects stored in the database, we should only pull those two entries without including the various connected tables. If we are performing an action on all items in a list. Pull and update all entries in a single statement instead of doing single gets/updates in a foreach. Do we need to perform a stored procedure every 15 minutes, or can we find a way to only perform the procedure in response to another apps action?
Such adjustments will make for both more efficient code, and make a step to lessening the environmental impact such data centers have.
Updated
Teklemuz Ayenew’s Answer
Reducing the carbon footprint of data centers is a crucial step in addressing climate change, and everyone can play a part in this mission. By designing smarter algorithms and using efficient hardware like ASICs, FPGAs, and edge computing, we can cut down on power use. Adding renewable energy sources and using techniques like virtualization and serverless computing helps to save energy and reduce waste. These efforts make sure we use computing resources wisely and sustainably.
Cloud services that use renewable energy and adjust resources based on demand make a big difference. Real-time monitoring and AI-driven optimization help us spot and fix energy inefficiencies quickly. Another way to lower energy use is by managing data smartly, like automatically archiving or deleting data we don't use anymore, which reduces storage and network demands. Working together across different fields and teaching developers and users about energy-saving practices can make sustainability a key focus in technology. By embracing these strategies, we can greatly lessen the environmental impact of data centers and make a real difference in fighting climate change.
Cloud services that use renewable energy and adjust resources based on demand make a big difference. Real-time monitoring and AI-driven optimization help us spot and fix energy inefficiencies quickly. Another way to lower energy use is by managing data smartly, like automatically archiving or deleting data we don't use anymore, which reduces storage and network demands. Working together across different fields and teaching developers and users about energy-saving practices can make sustainability a key focus in technology. By embracing these strategies, we can greatly lessen the environmental impact of data centers and make a real difference in fighting climate change.
Updated
Billy’s Answer
Hi Owen, Great question! I am glad that you are incorporating Climate change impacts into your planning for your future. Two thoughts come to mind readily on what can Computer Engineers do:
1. Asset utilization: By accelerating the moves of Enterprise and Sovereign IT workloads to the Cloud (VMs/Containers) we increase HW asset utilization vis-a-vis standalone on-prem Hardware. Using an SDN approach capacity allocated could be dynamically scaled up or down based on need. This helps save power and cooling needed overall by increasing efficiency of use of Compute resources.
2. Advanced low power GPU/CPU design: Densely packed cores (2nm and better) shall allow us to do more with less. This increases capability of processors (FLOPS) increasing our compute yield without linearly scaling power and cooling needs.
Hope this stimulates some thoughts for you.
Best
Billy
1. Asset utilization: By accelerating the moves of Enterprise and Sovereign IT workloads to the Cloud (VMs/Containers) we increase HW asset utilization vis-a-vis standalone on-prem Hardware. Using an SDN approach capacity allocated could be dynamically scaled up or down based on need. This helps save power and cooling needed overall by increasing efficiency of use of Compute resources.
2. Advanced low power GPU/CPU design: Densely packed cores (2nm and better) shall allow us to do more with less. This increases capability of processors (FLOPS) increasing our compute yield without linearly scaling power and cooling needs.
Hope this stimulates some thoughts for you.
Best
Billy