Physical models



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A physical model is a representation of the underlying hardware elements of a distributed system that abstracts away from specific details of the computer and networking technologies employed.
Baseline physical model: A distributed system was defined in Chapter 1 as one in which hardware or software components located at networked computers communicate and coordinate their actions only by passing messages. This leads to a minimal physical model of a distributed system as an extensible set of computer nodes interconnected by a computer network for the required passing of messages. Beyond this baseline model, we can usefully identify three generations of distributed systems.
Early distributed systems: Such systems emerged in the late 1970s and early 1980s in response to the emergence of local area networking technology, usually Ethernet. These systems typically consisted of between 10 and 100 nodes interconnected by a local area network, with limited Internet connectivity and supported
a small range of services such as shared local printers and file servers as well as email and file transfer across the Internet. Individual systems were largely homogeneous and openness was not a primary concern. Providing quality of service was still very much in
its infancy and was a focal point for much of the research around such early systems.
Internet-scale distributed systems: Building on this foundation, larger-scale distributed systems started to emerge in the 1990s in response to the dramatic growth of the Internet during this time (for example, the Google search engine was first launched in 1996). In
such systems, the underlying physical infrastructure consists of a physical model, that is, an extensible set of nodes interconnected by
a network of networks (the Internet). Such systems exploit the infrastructure offered by the Internet to become truly global. They incorporate large numbers of nodes and provide distributed system services for global organizations and across organizational
boundaries. The level of heterogeneity in such systems is significant in terms of networks, computer architecture, operating systems, languages employed and the development teams involved. This has led to an increasing emphasis on open standards and associated middleware technologies such as CORBA and more recently, web
services. Additional services were employed to provide end-to-end quality of service properties in such global systems.
Contemporary distributed systems: In the above systems, nodes were typically desktop computers and therefore relatively static (that is, remaining in one physical location for extended periods), discrete (not embedded within other physical entities) and autonomous (to a large extent independent of other computers in terms of their physical
infrastructure). The key trends identified in Section 1.3 have resulted in significant further developments in physical models:
The emergence of mobile computing has led to physical models where nodes such as laptops or smart phones may move from location to location in a distributed system, leading to the need for added capabilities such as service discovery and support for spontaneous interoperation.

The emergence of ubiquitous computing has led to a move from discrete nodes to architectures where computers are embedded in everyday objects and in the surrounding environment (for example, in washing machines or in smart homes more generally).

The emergence of cloud computing and, in particular, cluster architectures has led to a move from autonomous nodes performing a given role to pools of nodes that together provide a given service (for example, a search service as offered by Google).
The end result is a physical architecture with a significant increase in the level of heterogeneity embracing, for example, the tiniest embedded devices utilized in ubiquitous computing through to complex computational elements found in Grid computing. These systems deploy an increasingly varied set of networking technologies
and offer a wide variety of applications and services. Such systems potentially involve up to hundreds of thousands of nodes.
Distributed systems of systems • A recent report discusses the emergence of ultralarge-scale (ULS) distributed systems [www.sei.cmu.edu]. The report captures the complexity of modern distributed systems by referring to such (physical) architectures
as systems of systems (mirroring the view of the Internet as a network of networks). A system of systems can be defined as a complex system consisting of a series of subsystems that are systems in their own right and that come together to perform a particular task or tasks.
As an example of a system of systems, consider an environmental management system for flood prediction. In such a scenario, there will be sensor networks deployed to monitor the state of various environmental parameters relating to rivers, flood plains, tidal effects and so on. This can then be coupled with systems that are responsible for predicting the likelihood of floods, by running (often complex) simulations on, for example, cluster computers (as discussed in Chapter 1). Other systems may be established to maintain and analyze historical data or to provide early warning systems to key stakeholders via mobile phones.
Summary • The overall historical development captured in this section is summarized, with the table highlighting the significant challenges associated with contemporary distributed systems in terms of managing the levels of heterogeneity and providing key properties such as openness and quality of service.

 


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