There are many hardware environments which the Web will be expected to penetrate, yet where engineering assumptions that apply
to large-scale, more-or-less fixed dedicated computing machines don’t necessarily apply. Obvious examples include mobile computing, ubiqui- tous (or pervasive) computing where interoperability becomes an issue, P2P systems and grid computing. Mobile computing makes all sorts of engineering demands; the computing power available isn’t vast and users must be assumed to be constantly on the move with variable bandwidth and access. Furthermore, presenting information to the user requires different paradigms from the PC, for example to allow the user to receive enough information on the small screen to make brows- ing compelling [20, 193]. Mobile access to the Web may become the dominant mode in many nations, particularly developing ones, thanks to relatively low prices and reliability of wireless connections and bat- tery power [222]. Research in this area is important for the equitable distribution of Web resources.
Ubiquitous computing, P2P and grid computing share many seri- ous research issues, most notably the coordination of behaviour in large scale distributed systems. Ubiquitous computing envisages small, rel- atively low-powered computing devices embedded in the environment interacting pervasively with people. There are various imaginative pos- sibilities, such as smart threads which can be woven into clothing. But without second-guessing the trends it is clear that smaller devices will need wireless connections to network architectures allowing automatic
ad hoc configuration, and there are a number of engineering difficulties associated with that issue (cf. [244, 176]).
For instance, service discovery in the pervasive paradigm must take place without a human in the loop. Services must be able to adver- tise themselves to facilitate discovery. Standards for publishing services would be required to ensure security and privacy, trust of the service’s reliability, the compensation for the service provider, and exactly how the service would be composed with other invoked services to achieve some goal or solve the problem at hand [179].
This is just one example of a currently evolving computing environ- ment that is likely to grow in importance. In the context of Web Science and the search for and description of the invariants of the Web experi- ence, it is essential that the assumptions we make about environments, and the technologies that live in them, are minimised.
P2P networks, characterised by autonomy from central servers, intermittent connectivity and the opportunistic use of resources [220], are another intriguing environment for the next generation Web. In such networks (including file-sharing networks such as Napster, com- munication networks such as Skype, and computation networks such as SETI@home), the computer becomes a component in a distributed system, and might be doing all sorts of things: backing up others’ files, storing encrypted fragments of files, doing processing for large- scale endeavours in the background, and so on. There are clearly many potential applications for both structured and unstructured P2P net- works in the Web context. The question for Web scientists is what essential functions for the Web experience can be preserved in loosely coupled autonomous systems. Given the unusual characteristics of P2P, including the potentially great number and heterogeneity of P2P nodes, traditional engineering methods such as online experimentation (which would require unfeasibly large numbers of users to sign up to an archi- tecture and allow their transactions to be monitored) or large-scale sim- ulation (the scale is simply too large) will be inappropriate. The scale licensed by the Web, which we will continue to see in P2P networks, makes network modelling theory essential (cf. e.g. [249, 189]), but we must expect radical experimentation, innovation and entrepreneurial- ism to lead the way in this field.
The temptation to exploit radically decentralised environments such as P2P networks in the next generation of the Web is strong; decentral- isation is a key aspect of the Web’s success. So, for example, one could imagine P2P networks being used to locate cached pages for backups in the event of failure or error leading to missing pages or dangling links. It needs to be established whether the ability of a P2P network to do that (which itself is currently unproven) would undermine the domain name system or support it.
Whereas P2P systems exploit large scale distribution to achieve lots of small ends, grid computing [102] is often a distributed approach to large scale problems using substantial computing power to analyse enor- mous quantities of data. The problem is to coordinate the behaviour of a large number of computers, exploiting unused resources oppor- tunistically like P2P; again like P2P, and unlike traditional distributed
computing, grid computing is meant to be neutral about administrative or platform boundaries. Open standards are therefore needed, and the Grid requires abstract descriptions of computing resources.
By analogy to the Semantic Web, the Grid has spawned the Seman- tic Grid, where information and computing resources are annotated with metadata (and as with the SW RDF is the language of choice), allowing the exploitation of machine-readable specifications for the automatic coordination of resources to solve particular large-scale prob- lems [72]. The application of the Grid and the Semantic Grid to large scale problems shows enormous promise – indeed as data from CERN’s Large Hadron Collider come on stream at a gigabyte/sec, the Grid is indispensable.
The Grid and Semantic Grid raise a number of old questions in a new guise. Given that one’s computing resources are given over to outsiders, trust and security will require reconsideration [23]. Socially, an interesting issue is understanding whether the Grid will actually change science, or merely allow the processing of more and more data [207].
In general, all these new computing paradigms raise the question of how lots of relatively autonomous individuals can work together to produce mutually beneficial results (either results beneficial to each individual, or to society as a whole). Coordination problems such as these have always loomed large in many disciplines, and we shouldn’t be surprised to find them at the centre of Web Science.