Distributing Computer Processing

The enhancement to traditional systems architectures by computer designers in meeting the requirements of data-centric applications was to distribute the I/O processing. The efforts in research and design moved I/O further away from the computer elements, (CPU, RAM, and bus connectivity components ), which in turn provided an opportunity to increase the number of I/O paths to storage devices.

Symmetric Multiprocessing Systems (SMP)

Symmetric Multiprocessing Systems (SMP) became one of the first alternative designs to offer the use of more than one CPU component, as indicated in Figure 4-1. This first became available with IBM mainframes to increase the throughput scalability and processing capabilities by providing additional CPU components. The machines that tightly integrated these features with the operating system (MVS) were known by their design terms of dyadic, triadic, and quadratic, depending on the number of CPUs available on the machine. This further enhanced the processing of IBM mainframe configurations with their available loosely clustered systems of MVS-JES2 and little-known tightly coupled clustered systems of MVS_JES3 (see the Note in this section).

Figure 4-1: SMP processing

This type of architecture has become widely available through all major system vendors as the SMP designs provided an entry into increased processing power necessary to handle larger applications. However, as with IBM mainframe configurations that are integrated with MVS features and functions, all SMP systems are closely integrated with operating systems features. This is necessary to handle the additional activities of processing with more than a single CPU, sharing memory resources and spaces, and coordination within a shared I/O bus. Although they provide additional processing capacities for large-scale applications, they also bring their own limitations. Two are very apparent in Figure 4-1: the sharing of system RAM and the scalability of the I/O system.

Note 

Multiple Virtual Systems (MVS) is a proprietary operating system offered by IBM and used exclusively with its mainframe computer systems. Available since the middle 1970s, MVSs longevity can partly be attributed to its modular architecture. This allows software subsystems to specialize in particular areas of operation. MVS was early in its move to separate I/O functions into a separate subsystem. In addition, it offers proprietary subsystems of Job Entry Subsystems (providing two alternatives, JES2 and JES3) that provide, among many other functions, the inter-systems communications necessary for a cluster of mainframes to share processing workloads. IBM installations are moving to zOS, an enhanced version of MVS that supports POSIX compliance and open systems standard functionality such as TCP/IP access and web software services.

Massive Parallel Processing Systems (MPP)

Another advancement from the traditional client/server model was the innovation of Massively Parallel Processing Systems (MPP). These systems enabled multiple computer systems, each specializing in a particular aspect of an application process communicating through a high-speed link. The ability to apply parallel tasks to complex applications provided the processing throughput necessary to complete what was once considered impossible . The MPP architecture evolved into two categories of machines: one for process intensive applications and one for database intensive applications. Each machine category was differentiated by its high-speed link architecture, its integrated database functionality, and configuration flexibility.

The links could be network or switched based (or in some cases a hybrid of both). This link provided a communications network that enabled each node to work on individual processing tasks, while controlling its own systems resources, as illustrated in Figure 4-2. This delineates the node structure within the MPP system whereby each computing node does not share computer resources such as CPU, RAM, or local I/O, thus acquiring the name Shared Nothing configurations.

Figure 4-2: MPP processing

Even with the potential that MPP architectures demonstrated, the complexities have proved far greater and limited the proliferation of these machines as general-purpose servers. The complicated enhancements to operating systems communications to enable the coordination of multiple single-image operating systems has proved to be problematic and costly. In addition, the overhead required as increased computing nodes are added increases in a non-linear fashion and consequently limits the effectiveness and throughput. The size , operational complexities, and management challenges of MPP configurations have limited their usage to specialized applications.