Professor Andy Hopper and Dr Andrew Rice of the University of Cambridge Computer Laboratory, examine the role of computing in the quest to reduce our impact on the environment and improve our lives.
Computers, communications and their applications needn't have a negative effect on the environment. In fact, if harnessed properly computing could make a major contribution to ensuring a sustainable future for society and the planet.
At the University of Cambridge Computer Laboratory, we are examining the ways computing and digital technology can be developed to help reduce the ecological footprint of society and improve the way we live.
This research ranges from looking at the benefits of co-locating data centres with wind farms to the use of sensors to help optimise our transport network.
The research has four main themes:
- providing an optimal digital infrastructure that makes the best use of the energy it consumes during manufacture, operation and end-of-life processes;
- developing a global data collection network to sense and optimise our consumption of resources and our impact on the environment;
- predicting and reacting to future events in natural systems by developing dependable and trustworthy implementations of the complex models provided by scientists; and
- finding digital alternatives to our physical activities, building on the success of e-billing, downloadable music and online shopping.
Optimising the Digital Infrastructure
Data centres and server farms play an important role in the modern information infrastructure. They provide highly available websites for end users, support day-to-day business processes, and execute offline jobs such as indexing and backup. They incorporate power and cooling systems designed with high levels of redundancy which provide continued service even in the event of a fault. Ever-increasing amounts of energy are consumed to keep them running.
The complexity of these support systems has led to a situation where much of the digital infrastructure exists only to cope with faults if they occur. To reduce this overhead we need to run closer to the wire and act to mitigate faults when they occur. If uninterrupted service is required then this can be provided with software redundancy techniques. And low-priority services or batch jobs could simply be terminated pending resumption of service.
Constructing data centres and server farms close to large-scale renewable energy sources, such as wind turbines, also has potential benefits. These generation sites are commonly in remote areas (or even offshore) and providing high-capacity power connections is expensive. A data centre could consume this energy onsite whilst only requiring a high bandwidth data connection. A wired data connection requires much less cable than power transmission and wireless connections require no cable at all!
Computing has the potential for great flexibility in its use of energy. Rescheduling batch jobs, using power management techniques and turning machines off can produce rapid changes in its power demands. In the case of wind power this means that previously wasted peaks in power generation can be absorbed by increasing computation rates. Similarly, troughs in production can be mitigated by scaling back again. Adaptive data centres allow computers to contribute as a 'virtual battery' by selectively varying power consumption in response to the availability of generation capacity.
Sensing and Optimising the World
We aim to create a detailed model of the planet that is kept up-to-date with information collected from millions of sensors around the globe. This would be invaluable in discovering the impact human activities have on the environment, as well as for optimising energy consumption and other natural resources.
The potential benefits to be gained from such a vast amount of real-time, accurate data are immense. However, such a system must be engineered appropriately. We must ensure that our sensors themselves do not consume more resources than they empower us to conserve.
In this respect, a promising source of information is to simply take advantage of observations made by humans to accumulate qualitative, as well as quantitative data. For example, eye witness reports could reveal logging activities in the Brazilian rainforest or villagers in developing countries could provide information on access and quality of water supplies. Quantitative information could also be collected from people carrying digital cameras or GPS units. Autonomously compiling data and discovering which reports are consistent and which are not is a key challenge.
It's a huge task. In order to work on the scale of the UK alone, data needs to be collected from 25 million homes, 33 million registered vehicles and the use of 45GW of electricity and 1 million litres of water per second monitored. But once data has been compiled and stored, we envisage the creation of a real-time data map that will enable everyone to observe different layers. This might include a transportation layer that shows congestion on roads; an energy layer that shows the state of the electricity grid; a water layer that shows flows and leaks in the distribution system; or a layer that use infrared sensors to show wasted heat through the roofs of our homes.
Wide-scale sensing and data collection highlights the dilemmas of providing functionality whilst preserving privacy: constructing a privacy-preserving system with no centralised data storage is technically feasible but adds operating overheads and energy costs. At the other end of the spectrum, a centralised system can deliver efficient operation at the cost of exposing huge amounts of private data to administrators (or hackers). A suitable and safe compromise must be found.
Predicting and Reacting Based on a World Model
The study of global warming is a prominent example of the scientific community's efforts to produce accurate forecasts of the behaviour of natural systems. Ever more sophisticated algorithms running on powerful computers are being brought to bear on the problem. But more fundamental is the question of how can we be sure that the implementation of these models is correct?
Therefore, the third goal of the Computing for the Future of the Planet is to develop techniques and tools for building models which we can depend upon. This is by no means a simple task: even our most commonly used computing applications require frequent updates to fix bugs and security holes.
Many techniques from Computer Science might prove applicable to this goal. High-level programming languages strive to leave the programmer free to canonically express his intent. This is beneficial both in reducing the number of bugs in the code and also in providing freedom to the compiler as to the specific strategy for execution. The programmer need not worry about the huge complexity of modern hardware; and if a new type of machine becomes available the program only requires recompilation rather than being rewritten.
Another use of high level information occurs in languages which can now ensure, before a program is executed, that the programmer has never attempted to add a distance measurement to an area or to multiply a value in metres by a value in feet, for example.
Models are becoming increasingly important as their predictions drive not only scientific understanding but also policy makers. In many fields the output of a model is relevant in the short term and so must be computed in a timely manner. There is no use predicting the spread of a disease epidemic if the prediction arrives too late.
Our interests focus on providing high-performance models by making the best use of the chosen machine architecture and ensuring that intermediate values in a model are not computed to a needlessly high level of accuracy. This must be achieved whilst allowing us to reason about the correctness of our implementation and the trustworthiness of the results.
Digital Alternatives to Physical Activities
It is estimated that the ecological footprint of Western Europe is more than double its biocapacity and over two and a half times the globally sustainable average footprint. Computing has the potential to free us from these constraints. The ephemeral nature of information and data suggests that there is huge potential for growth and expansion if we can shift our physical activities to digital alternatives. Some digital alternatives existing today include: reading the news online, downloading music as opposed to buying CDs, or opting for e-billing as opposed to the paper alternative.
Many of us already conduct many aspects of our lives in cyberspace. This happens in virtual worlds such as Second Life, on social networking sites and through the use of email and instant messaging. In the developing world the explosive growth of the mobile phone market is provoking a similar trend through unprecedented communication ability and access to information. Much of this activity occurs without regulatory incentives or environmental legislation-these activities are compelling in their own right. If this occurs with minimal environmental impact the possibilities are unbounded.
However, the huge environmental impact of manufacturing computers and the energy costs of provisioning our infrastructure mean that there are significant costs to our digital alternatives. For example, researchers have found that there is only a minor energy reduction when reading the news online as opposed to buying a newspaper. Intelligent choices about which activities we move to a digital world, compounded with our optimal digital infrastructure and other improvements in technology, should tilt the balance more strongly in the favour of computing.
Given the immeasurable changes undergone by computing, and caused by computing in the last 60 years, we ask what changes we might see over the next 60 years. 'Computing for the Future of the Planet' aims to tackle problems throughout all aspects of our lives and our environment and to ensure that computing has a positive impact on the world around us.
Professor Andy Hopper and Dr Andrew Rice of the University of Cambridge Computer Laboratory