essential properties

A. Batch: Jobs with similar needs are batched together and run through the computer as a group, by an operator or automatic job sequencer. Performance is increased by attempting to keep CPU and I/O devices busy at all times through buffering, off-line operation, spooling, and multiprogramming.

B. time-sharing

1. Computer Science. A technique permitting many users simultaneous access to a central computer through remote terminals.
2. also time-share (-shâr') Joint ownership or lease of vacation property by several people who take turns occupying the premises for fixed period

C. Real time

In computer science, real-time computing (RTC) is the study of hardware and software systems that are subject to a "real-time constraint"—i.e., operational deadlines from event to system response. By contrast, a non-real-time system is one for which there is no deadline, even if fast response or high performance is desired or preferred. The needs of real-time software are often addressed in the context of real-time operating systems, and synchronous programming languages, which provide frameworks on which to build real-time application software.

A real time system may be one where its application can be considered (within context) to be mission critical. The anti-lock brakes on a car are a simple example of a real-time computing system — the real-time constraint in this system is the short time in which the brakes must be released to prevent the wheel from locking. Real-time computations can be said to have failed if they are not completed before their deadline, where their deadline is relative to an event. A real-time deadline must be met, regardless of system load.

D. network

A computer network is a group of interconnected computers. Networks may be classified according to a wide variety of characteristics. This article provides a general overview of some types and categories and also presents the basic components of a network.

E.

Distributed computing deals with hardware and software systems containing more than one processing element or storage element, concurrent processes, or multiple programs, running under a loosely or tightly controlled regime.

In distributed computing a program is split up into parts that run simultaneously on multiple computers communicating over a network. Distributed computing is a form of parallel computing, but parallel computing is most commonly used to describe program parts running simultaneously on multiple processors in the same computer. Both types of processing require dividing a program into parts that can run simultaneously, but distributed programs often must deal with heterogeneous environments, network links of varying latencies, and unpredictable failures in the network or the computers.

F. handheld
A mobile device (also known as cellphone device, handheld device, handheld computer, "Palmtop" or simply handheld) is a pocket-sized computing device, typically having a display screen with touch input or a miniature keyboard. In the case of the personal digital assistant (PDA) the input and output are combined into a touch-screen interface. Smartphones and PDAs are popular amongst those who require the assistance and convenience of a conventional computer, in environments where carrying one would not be practical. Enterprise digital assistants can further extend the available functionality for the business user by offering integrated data capture devices like Bar Code, RFID and Smart Card readers.

views of system and users

Operating system (commonly abbreviated to either OS or O/S) is an interface between hardware and user; it is responsible for the management and coordination of activities and the sharing of the resources of the computer. The operating system acts as a host for computing applications that are run on the machine. As a host, one of the purposes of an operating system is to handle the details of the operation of the hardware. This relieves application programs from having to manage these details and makes it easier to write applications. Almost all computers (including handheld computers, desktop computers, supercomputers, video game consoles) as well as some robots, domestic appliances (dishwashers, washing machines), and portable media players use an operating system of some type. ^[1] Some of the oldest models may however use an embedded operating system, that may be contained on a compact disk or other data storage device.

Operating systems offer a number of services to application programs and users. Applications access these services through application programming interfaces (APIs) or system calls. By invoking these interfaces, the application can request a service from the operating system, pass parameters, and receive the results of the operation. Users may also interact with the operating system with some kind of software user interface (UI) like typing commands by using command line interface (CLI) or using a graphical user interface (GUI, commonly pronounced “gooey”). For hand-held and desktop computers, the user interface is generally considered part of the operating system. On large multi-user systems like Unix and Unix-like systems, the user interface is generally implemented as an application program that runs outside the operating system. (Whether the user interface should be included as part of the operating system is a point of contention.)

Common contemporary operating system families include BSD, Darwin (Mac OS X), Linux, SunOS (Solaris/OpenSolaris), and Windows NT (XP/Vista/7). While servers generally run Unix or some Unix-like operating system, embedded system markets are split amongst several operating systems.

multiprogramming, batch, time-sharing system

THE multiprogramming system was a computer operating system designed by a team led by Edsger W. Dijkstra, described in monographs in 1965-66 and published in 1968. Dijkstra never named the system; "THE" is simply the abbreviation of "Technische Hogeschool Eindhoven", then the name (in Dutch) of the Eindhoven University of Technology of the Netherlands. The THE system was primarily a batch system[1] that supported multitasking; it was not designed as a multi-user operating system. It was much like the SDS 940, but "the set of processes in the THE system was static."[1]The THE system apparently introduced the first forms of software-based memory segmentation (the Electrologica X8 did not support hardware-based memory management)[1], freeing programmers from being forced to use actual physical locations on the drum memory. It did this by using a modified ALGOL compiler (the only programming language supported by Dijkstra's system) to "automatically generate calls to system routines, which made sure the requested information was in memory, swapping if necessary."[1]

Time-sharing is sharing a computing resource among many users by multitasking. Its introduction in the 1960s, and emergence as the prominent model of computing in the 1970s, represents a major historical shift in the history of computing. By allowing a large number of users to interact simultaneously on a single computer, time-sharing dramatically lowered the cost of providing computing, while at the same time making the computing experience much more interactive.

Batch system-In computing, system for processing data with little or no operator intervention. Batches of data are prepared in advance to be processed during regular ‘runs’ (for example, each night). This allows efficient use of the computer and is well suited to applications of a repetitive nature, such bulk file format conversion, as a company payroll, or the production of utility bills.

advantge of parallel system

One capability of MatLab is that its Parallel Computing Toolbox can automatically parcel out work based on the availability of units of computation, whether they are cores, clusters, or grids. Further, the Parallel Computing Toolbox enables users to run MatLab applications in up to eight cores locally, taking advantage of the latest trends in desktop system processors.
For those of you who write your own MatLab code, you can add some code to help it still more. Using parallel commands as a part of, for example, FOR loops (assuming that the repeating computations are independent), you can explicitly tell those loops to execute simultaneously on whatever resources are available. Rather than FOR, the MatLab instruction is PARFOR. And any code will execute on all defined resources in the project.
There is more to analysis than code. You may also have extremely large datasets. By breaking up problems on different computers in a cluster or grid, you can keep a smaller set of data in memory during parallel computation and still collect the data and analyze it later. If you can t run on a desktop system because of heap space limitations, maybe now you can.
The end result is you can take existing MatLab code and run it in parallel on multiple cores, often with few or no changes. Depending on where you are executing this code, you can get some pretty significant performance improvements. And best of all, you can largely use your existing MatLab routines.
While not everyone uses MatLab, it provides engineers with the opportunity to make full use of the power of their desktop computers and other inexpensive clusters and grids, including the Amazon EC2 cloud. Just be prepared to say goodbye to renting time on your favorite supercomputer.

symmetric multiprocessing and Asymmetric multiprocessing

Asymmetric multiprocessing - In asymmetric multiprocessing (ASMP), the operating system typically sets aside one or more processors for its exclusive use. The remainder of the processors run user applications. As a result, the single processor running the operating system can fall behind the processors running user applications. This forces the applications to wait while the operating system catches up, which reduces the overall throughput of the system. In the ASMP model, if the processor that fails is an operating system processor, the whole computer can go down.
Symmetric mMultiprocessing - Symmetric multiprocessing (SMP) technology is used to get higher levels of performance. In symmetric multiprocessing, any processor can run any type of thread. The processors communicate with each other through shared memory.
SMP systems provide better load-balancing and fault tolerance. Because the operating system threads can run on any processor, the chance of hitting a CPU bottleneck is greatly reduced. All processors are allowed to run a mixture of application and operating system code. A processor failure in the SMP model only reduces the computing capacity of the system.
SMP systems are inherently more complex than ASMP systems. A tremendous amount of coordination must take place within the operating system to keep everything synchronized. For this reason, SMP systems are usually designed and written from the ground up.

Goals of OS

The main functions of an OS include:
In a multitasking operating system where multiple programs can be running at the same time, the operating system determines which applications should run in what order and how much time should be allowed for each application before giving another application a turn.
It manages the sharing of internal memory among multiple applications.
It handles and monitors input and output to and from attached hardware devices, such as hard disks, printers, and dial-up ports. [8]
It sends messages to each application or interactive user (or to a system operator) about the status of operation and any errors that may have occurred.
It can offload the management of what are called batch jobs (for example, printing) so that the initiating application is freed from this work.
On computers that can provide parallel processing, an operating system can manage how to divide the program so that it runs on more than one processor at a time.
Scheduling the activities of the CPU and resources to achieve efficiency and prevention of deadlock. [9]