Deep Online Cognition in Modular Software

Have you ever wondered how much energy it takes to visit the BBC news website? Or send an email? Or watch iPlayer? All these modern everyday services consume an increasing amount of energy on a global scale through sites called "data centres" - large computer facilities which your computer, phone or tablet talks to when it uses these services. By 2020, these data centres are expected to represent up to 20% of total worldwide energy consumption.

This project aims to reduce energy consumption by enabling data centres to service more users in a shorter time. This would allow us to use smaller, more efficient, data centres thus reducing the amount of energy that they consume. We will achieve this by developing a new way for the software that runs inside data centres -- the software that sends web pages or video content to your computer -- to analyse itself in real-time, to locate areas of inefficiency, and to automatically correct these inefficiencies when they are observed.

This real-time analysis is a continual process. The kinds of user requests that are seen by a data centre change over time, meaning that the data centre software needs to continually optimise itself against new types of access and workload patterns generated by the current population of users. The optimal software that is needed to respond to requests for many small HTML and image files may, for example, be completely different to the optimal software that is needed to respond to requests for a few large popular media streams. Existing evidence shows data centre software is very difficult to optimise for a single user workload pattern, never mind the fluctuating patterns to which real data centres are subjected over time.

The approach we are taking in this research has yet to be tried in any prior work -- it is uniquely enabled by a new way of building software that has been developed by the lead investigator of this project. For the first time this approach involves fully automated learning and understanding by a piece of software about the capabilities and performance levels of each sub-element (or 'component') that makes up that software. Over time the software will build up an understanding of how each of its components performs against various user workload patterns to which it is subjected, and how the performance of those components relates to the current performance level of the overall software system. When the overall performance level drops, for example due to a shift in the user workload pattern, the individual software component or components responsible for this performance drop will be automatically identified and exchanged for other components that are known to perform better in the current set of operating conditions.

In essence this approach will ultimately lead to a kind of "assisted" model of software development, where software itself acts as an active member of its own development team to identify and correct inefficiencies while the software is running. When uncorrectable inefficiencies are detected, due to the lack of any high-performing alternative components, the software will notify its development team of this situation and will ask them to design new alternative components to suit a particular set of operating conditions.

We are confident that the novel approach taken by this research will be successful in achieving previously untapped performance improvements and has the potential not only to save energy at data centres but to create a whole new model of software development -- a model in which software itself is able to reason about its own design and about its efficacy for a particular purpose.

The research will contribute to the UK's international standing in research excellence, tackling two major challenges in modern computer science -- the automated optimisation of complex software and the increasing energy consumption of data centres. Beyond this, our work will have two major avenues for broader impact.

The first of these results from our focus on reducing the energy consumption of data centres, an activity that is impactful to a large section of the digital economy, including data centre operators, web hosting companies, major application-level service providers such as Google and the BBC, and a large range of smaller companies that rely on data centre services to support their day to day operations. By offering solutions that reduce energy costs at data centres, and correspondingly which directly reduce operating costs, we enable organisations to spend more time and money on innovation rather than on operations. As one of Europe's leading data centre operators, the UK has a key interest in this sector. In this project we have direct access to industry stakeholders at two of the above levels: firstly via direct collaboration with the BBC as a major application-level service provider; and secondly with the host institution's KBC business incubation unit which houses a variety of smaller companies that rely on web services. In detail, the BBC represents a major data centre user to support its extensive web site (one of the top 100 most popular sites in the world), the iPlayer, and a wide range of internal systems. As a publicly funded organisation the BBC has serious constraints in terms of both scale and operational costs and so represents an excellent partner to whom we can disseminate our results, including the potential deployment of experimental prototype software. Additionally, because the BBC outsources many of its data centre operations to third-party companies, we will also have access to these connections to further disseminate our research work. The host institution's KBC, meanwhile, represents the other end of the spectrum with a collection of very small companies. This may lead to commercialisation opportunities for some elements of the research in terms of energy-saving data centre technology.

The second major impact avenue is in our endeavour to bring a higher level of reasoning to software systems such that they are able to learn and understand their own capabilities in real-time. This has the potential to be a highly disruptive innovation which will fundamentally change the way in which software is developed. Instead of large teams working to design every detail of a software system, those teams will focus on higher-level aspects of system composition and will expect the software itself to assist in the design of its own fine-grained details. In the further future, this will see software take an increasingly prominent role as a member of its own "development team", resulting in software development lifecycles that are cheaper, faster, and which produce more reliable results. Our research helps to place the UK at the leading edge of this vision -- an immediate impact being the training of an RA and a PhD student in the key technology that supports the vision.