Examples of distributed systems
Examples of Distributed Systems
The goal of this section is to provide motivational examples of contemporary distributed systems and the great diversity of the associated applications.
As mentioned in the introduction, networks are everywhere and underpin many everyday services that we now take for granted: the Internet and he associated World Wide Web, web search, online gaming, email, social networks, eCommerce, etc. To illustrate this point further, consider Figure1.1, which describes a selected range of key commercial or social application sectors highlighting some of the associated established or emerging uses of distributed systems technology.
As can be seen, distributed systems encompass many of the most significant technological developments of recent years and hence an understanding of the underlying technology is absolutely central to a knowledge of modern computing. The figure also provides an initial insight into the wide range of applications in use today, from relatively localized systems (as found, for example, in a car or aircraft) toglobalscale systems involving millions of nodes, from data-centric services to processor-intensive tasks, from systems built from very small and relatively primitive sensors to those incorporating powerful computational elements, from embedded systems to ones that support a sophisticated interactive user experience, and so on. We now look at more specific examples of distributed systems to further illustrate the diversity and indeed complexity of distributed systems provision today.
Web search has emerged as a major growth industry in the last decade, with recent figures indicating that the global number of searches has risen to over 10 billion per calendar month. The task of a web search engine is to index the entire contents of the World Wide Web, encompassing a wide range of information styles including web pages, multimedia sources and (scanned) books. This is a very complex task, as current estimates state that the Web consists of over 63 billion pages and one trillion unique web
Finance and commerce - The growth of eCommerce as exemplified by companies such as Amazon and eBay, and underlying payments technologies such as PayPal; the associated emergence of online banking and trading and also complex information dissemination systems for financial markets.
The information society - The growth of the World Wide Web as a repository of information and knowledge; the development of web search engines such as Google and Yahoo to search this vast repository; the emergence of digital libraries and the large-scale digitization of legacy information sources such as books (for example, Google Books); the increasing significance of user-generated content through sites such as YouTube, Wikipedia and Flickr; the emergence of social networking through services such as Facebook and MySpace.
Creative industries and entertainment - The emergence of online gaming as a novel and highly interactive form of entertainment; the availability of music and film in the home through networked media centres and more widely in the Internet via downloadable or streaming content; the role of user-generated content (as mentioned above) as a new form of creativity, for example via services such as YouTube; the creation of new forms of art and entertainment enabled by emergent (including networked) technologies.
Healthcare - The growth of health informatics as a discipline with its emphasis on online electronic patient records and related issues of privacy; the increasing role of telemedicine in supporting remote diagnosis or more advanced services such as remote surgery (including collaborative working between healthcare teams); the increasing application of networking and embedded systems technology in assisted living, for example for monitoring the elderly in their own homes.
Education - The emergence of e-learning through for example web-based tools such as virtual learning environments; associated support for distance learning; support for collaborative or community-based learning.
Transport and logistics - The use of location technologies such as GPS in route finding systems and more general traffic management systems; the modern car itself as an example of a complex distributed system (also applies to other forms of transport such as aircraft); the development of web-based map services such as MapQuest, Google Maps and Google Earth.
Science - The emergence - of the Grid as a fundamental technology for science including the use of complex networks of computers to support the storage, analysis, and processing of (often very large quantities of) scientific data; the associated use of the Grid as an enabling technology for worldwide collaboration between groups of scientists.
Environmental management The use of (networked) sensor technology to both monitor and manage Environmental management example to provide early warning of natural disasters such as earthquakes, floods or tsunamis and to co ordinate emergency response; the collation and analysis of global environmental parameters to better understand complex natural phenomena such as climate change addresses. Given that most search engines analyze the entire web content and then carry out sophisticated processing on this enormous database, this task itself represents a major challenge for distributed systems design.
Google, the market leader in web search technology, has put significant effort into the design of a sophisticated distributed system infrastructure to support search (and indeed other Google applications and services such as Google Earth). This represents one of the largest and most complex distributed systems installations in the history of computing and hence demands close examination. Highlights of this infrastructure include:
• an underlying physical infrastructure consisting of very large numbers of networked computers located at data centers all around the world.
• a distributed file system designed to support very large files and heavily optimized for the style of usage required by search and other Google applications (especially reading from files at high and sustained rates).
• an associated structured distributed storage system that offers fast access to very large datasets.
• a lock service that offers distributed system functions such as distributed locking and agreement.
• a programming model that supports the management of very large parallel and distributed computations across the underlying physical infrastructure.
Further details on Google’s distributed systems services and underlying communications support can be found in Chapter 21, a compelling case study of a modern distributed system in action.
Massively multiplayer online games (MMOGs)
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Massively multiplayer online games offer an immersive experience whereby very large numbers of users interact through the Internet with a persistent virtual world. Leading examples of such games include Sony’s EverQuest II and EVE Online from the Finnish
company CCP Games. Such worlds have increased significantly in sophistication and now include, complex playing arenas (for example EVE, Online consists of a universe with over 5,000 star systems) and multifarious social and economic systems. The number of players is also rising, with systems able to support over 50,000 simultaneous
online players (and the total number of players perhaps ten times this figure). The engineering of MMOGs represents a major challenge for distributed systems technologies, particularly because of the need for fast response times to preserve the user experience of the game. Other challenges include the real-time propagation of events to the many players and maintaining a consistent view of the shared world. This, therefore, provides an excellent example of the challenges facing modern distributed systems designers.
A number of solutions have been proposed for the design of massively multiplayer online games:
• Perhaps surprisingly, the largest online game, EVE Online, utilizes a client-server an architecture where a single copy of the state of the world is maintained on a centralized server and accessed by client programs running on players’ consoles or other devices. To support large numbers of clients, the server is a complex the entity in its own right consisting of a cluster architecture featuring hundreds of computer nodes (this client-server approach is discussed in more detail in Section 1.4 and cluster approaches are discussed in Section 1.3.4). The centralized architecture helps significantly in terms of the management of the virtual world and the single copy also eases consistency concerns. The goal is then to ensure fast response through optimizing network protocols and ensuring a rapid response to incoming events. To support this, the load is partitioned by allocating individual ‘star systems’ to particular computers within the cluster, with highly loaded star systems having their own dedicated computer and others sharing a computer.
Incoming events are directed to the right computers within the cluster by keeping track of movement of players between star systems.
• Other MMOGs adopt more distributed architectures where the universe is partitioned across a (potentially very large) number of servers that may also be geographically distributed. Users are then dynamically allocated a particular server based on current usage patterns and also the network delays to the server (based on geographical proximity for example). This style of architecture, which
is adopted by EverQuest, is naturally extensible by adding new servers.
• Most commercial systems adopt one of the two models presented above, but researchers are also now looking at more radical architectures that are not based on client-server principles but rather adopt completely decentralized approaches based on peer-to-peer technology where every participant contributes resources (storage and processing) to accommodate the game.
As a final example, we look at distributed systems support financial trading markets. The financial industry has long been at the cutting edge of distributed systems technology with its need, in particular, for real-time access to a wide range of information sources (for example, current share prices and trends, economic and political developments). The industry employs automated monitoring and trading applications (see below).
Note that the emphasis in such systems is on the communication and processing of items of interest, known as events in distributed systems, with the need also to deliver events reliably and in a timely manner to potentially very large numbers of clients who have a stated interest in such information items. Examples of such events include a drop in a share price, the release of the latest unemployment figures, and so on. This requires a very different style of underlying architecture from the styles mentioned above (for example client-server), and such systems typically employ what are known as distributed event-based systems. We present an illustration of a typical use of such systems below and return to this important topic in more depth in Chapter 6.
Figure 1.2 illustrates a typical financial trading system. This shows a series of event feeds coming into a given financial institution. Such event feeds share the following characteristics. Firstly, the sources are typically in a variety of formats, such as Reuters market data events and FIX events (events following the specific format of the Financial Information eXchange protocol), and indeed from different event
technologies, thus illustrating the problem of heterogeneity as encountered in most distributed systems (see also Section 1.5.1). The figure shows the use of adapters which translate heterogeneous formats into a common internal format. Secondly, the trading system must deal with a variety of event streams, all arriving at rapid rates, and often requiring real-time processing to detect patterns that indicate trading opportunities. This used to be a manual process but competitive pressures have led to increasing automation in terms of what is known as Complex Event Processing (CEP), which offers a way of composing event occurrences together into logical, temporal or spatial patterns.
This approach is primarily used to develop customized algorithmic trading strategies covering both buying and selling of stocks and shares, in particular looking for patterns that indicate a trading opportunity and then automatically responding by placing and managing orders. As an example, consider the following script:
MSFT price moves outside 2% of MSFT Moving Average
MyBasket moves up by 0.5%
HPQ’s price moves up by 5%
MSFT’s price moves down by 2%
any 2 minute time period
This script is based on the functionality provided by Apama [www.progress.com], a commercial product in the financial world originally developed out of research carried out at the University of Cambridge. The script detects a complex temporal sequence based on the share prices of Microsoft, HP and a basket of other share prices, resulting in decisions to buy or sell particular shares.
This style of technology is increasingly being used in other areas of financial systems including the monitoring of trading activity to manage risk (in particular, tracking exposure), to ensure compliance with regulations and to monitor for patterns of activity that might indicate fraudulent transactions. In such systems, events are typically
intercepted and passed through what is equivalent to a compliance and risk firewall before being processed (see also the discussion of firewalls in Section)