Reducing the Global ICT Footprint via Self-adaptive Large-scale ICT Systems
Lead Research Organisation:
Lancaster University
Department Name: Computing & Communications
Abstract
ICT now consumes approximately 10% of global electricity, with large-scale ICT systems such as Cloud datacentres, IoT, and HPC systems generating a substantial ICT footprint in terms of energy consumption and GHG emissions, and are growing contributors to climate change. Researchers across Computer Science and various engineering disciplines have predominantly tackled this problem via enhancing the energy-efficiency of individual components (software, servers, networking, cooling) via improvements to scheduling, software optimisation, hardware, and cooling.
However, enhancing system component efficiency has still resulted in a growing global ICT footprint - more data, greater compute ability, and more devices. This is due to the rebound effect, whereby technological progress enhances system efficiency, however increases the rate of consumption and end-use demand. This is of increasing concern given the end of Moore's law, growing global digital service consumption, and the rise of Big Data and AI services in society - all when combined result in a rapidly increasing ICT footprint. It is no longer possible to rely on the conventional perception that 'green' large-scale ICT systems can be achieved just by solely improving component energy-efficiency. There needs to focused effort to actually reverse the global ICT footprint.
We believe that this problem is not insurmountable however, yet requires a radical rethink how large-scale ICT systems are designed and operate. A system's ICT footprint is a by-product of its operation; we propose to inverse this dynamic - whereby system operation is instead a by-product of, and directly dictated by, its ICT footprint. What is required isn't greater efficiency, but instead precise control over how ICT systems operate and respond to energy levels and footprint targets; a significant research challenge given the sheer scale and complexity in understanding the relationship between ICT footprint manifestation, component interactions, and the impact of organisational sustainability practises. This challenge is further compounded by potential organisational resistance who may champion commercial profits over environment concerns. However, overcoming this challenge would allow ICT systems operation to be directly matched to energy generated from renewable sources, adhere to a specified GHG emission targets defined at organisational or national level, or dynamically align with an organisation's commercial targets or OpEx restrictions.
This fellowship will design a large-scale ICT system capable of self-adapting its operation in response to energy availability and ICT footprint targets. This specifically entails:
(1) Studying of causes of ICT footprint manifestation within technology organisations, and understand the rationale and impact of enacting sustainability practises.
(2) Determine and model the precise relationship between complex ICT component interactions and resultant ICT footprint.
(3) Design a self-adaptive framework that coordinates ICT energy-efficient decision making holistically.
(4) Create a holistic resource manager underpinned by energy availability and ICT footprint targets.
This fellowship is backed by a consortium of industrial and academic Computer Science and sustainability collaborators in the UK and beyond, and will be underpinned by considerable empirical analysis and experimentation in both production and laboratory CPU/GPU-based datacentre and HPC systems.
Findings from this fellowship are potentially ground breaking towards designing future digital infrastructure in the face of environmental change. Our key outcomes include:
- Reducing ICT system energy use between 25-50% with no software performance penalty.
- Demonstrating the feasibility to reverse global ICT footprint growth via unshackling system operation from the rebound effect.
- Releasing the largest in-depth operational and energy data from real-world ICT systems.
However, enhancing system component efficiency has still resulted in a growing global ICT footprint - more data, greater compute ability, and more devices. This is due to the rebound effect, whereby technological progress enhances system efficiency, however increases the rate of consumption and end-use demand. This is of increasing concern given the end of Moore's law, growing global digital service consumption, and the rise of Big Data and AI services in society - all when combined result in a rapidly increasing ICT footprint. It is no longer possible to rely on the conventional perception that 'green' large-scale ICT systems can be achieved just by solely improving component energy-efficiency. There needs to focused effort to actually reverse the global ICT footprint.
We believe that this problem is not insurmountable however, yet requires a radical rethink how large-scale ICT systems are designed and operate. A system's ICT footprint is a by-product of its operation; we propose to inverse this dynamic - whereby system operation is instead a by-product of, and directly dictated by, its ICT footprint. What is required isn't greater efficiency, but instead precise control over how ICT systems operate and respond to energy levels and footprint targets; a significant research challenge given the sheer scale and complexity in understanding the relationship between ICT footprint manifestation, component interactions, and the impact of organisational sustainability practises. This challenge is further compounded by potential organisational resistance who may champion commercial profits over environment concerns. However, overcoming this challenge would allow ICT systems operation to be directly matched to energy generated from renewable sources, adhere to a specified GHG emission targets defined at organisational or national level, or dynamically align with an organisation's commercial targets or OpEx restrictions.
This fellowship will design a large-scale ICT system capable of self-adapting its operation in response to energy availability and ICT footprint targets. This specifically entails:
(1) Studying of causes of ICT footprint manifestation within technology organisations, and understand the rationale and impact of enacting sustainability practises.
(2) Determine and model the precise relationship between complex ICT component interactions and resultant ICT footprint.
(3) Design a self-adaptive framework that coordinates ICT energy-efficient decision making holistically.
(4) Create a holistic resource manager underpinned by energy availability and ICT footprint targets.
This fellowship is backed by a consortium of industrial and academic Computer Science and sustainability collaborators in the UK and beyond, and will be underpinned by considerable empirical analysis and experimentation in both production and laboratory CPU/GPU-based datacentre and HPC systems.
Findings from this fellowship are potentially ground breaking towards designing future digital infrastructure in the face of environmental change. Our key outcomes include:
- Reducing ICT system energy use between 25-50% with no software performance penalty.
- Demonstrating the feasibility to reverse global ICT footprint growth via unshackling system operation from the rebound effect.
- Releasing the largest in-depth operational and energy data from real-world ICT systems.
People |
ORCID iD |
Peter Garraghan (Principal Investigator / Fellow) |
Publications
Borowiec D
(2023)
DOPpler: Parallel Measurement Infrastructure for Auto-Tuning Deep Learning Tensor Programs
in IEEE Transactions on Parallel and Distributed Systems
Borowiec D
(2022)
Trimmer: Cost-Efficient Deep Learning Auto-tuning for Cloud Datacenters
Borowiec D
(2021)
The environmental consequence of deep learning
in ITNOW
Nabavi S
(2021)
TRACTOR: Traffic-aware and power-efficient virtual machine placement in edge-cloud data centers using artificial bee colony optimization
in International Journal of Communication Systems
Terenius P
(2023)
A material social view on data center waste heat: Novel uses and metrics
in Frontiers in Sustainability
Tuli S
(2022)
HUNTER: AI based holistic resource management for sustainable cloud computing
in Journal of Systems and Software
Yeung G
(2022)
Horus: Interference-Aware and Prediction-Based Scheduling in Deep Learning Systems
in IEEE Transactions on Parallel and Distributed Systems
Description | Reducing the Global ICT Footprint via Self-adaptive Large-scale (DCMS Supplement) |
Amount | £335,000 (GBP) |
Funding ID | EP/V007092/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 12/2022 |
End | 03/2023 |
Description | BT Sustainability Round Table |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | Discussion surrounding organisational challenges in sustainable ICT across different sectors. Included major UK sectors from Defence, Telecommunication, Consultancy, Logistics and Financial Services. |
Year(s) Of Engagement Activity | 2023 |
Description | NVAITC Technical Sharing 2022 - Sustainable AI & Net Zero Deep Learning Systems |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Presented an hour presentation/discussion on Net-zero Deep Learning Systems and sustainable AI to technical experts/scientists/engineers at Nvidia research. |
Year(s) Of Engagement Activity | 2022 |