Reducing the Global ICT Footprint via Self-adaptive Large-scale ICT Systems

35931f12-9a12-451b-b5c4-0bb8e150810d

Active

EPSRC
EPSRC
EPSRC
EPSRC
EPSRC

£5,835,220

May 31, 2021

May 31, 2025

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.