Encyclopedia of Computer Science and Technology

eal-time processing 399• RAID 1—mirroring (data stored on at least two disks),data intact as long as one disk is still operating• RAID 3 and 4—striping plus a dedicated disk for parity(error checking)• RAID 5—striping with distributed parity; data can berestored automatically after a failed disk is replaced• RAID 6—like RAID 5 but with parity distributed sothat data remains intact unless more than two drivesfailIn actuality, RAID configurations can be very complex,where different levels can be “layered” above one another,with each treating the next as a virtual drive, until onegets down to the actual hardware. Although RAID is oftenimplemented using a physical (hardware) controller, operatingsystems can also create a virtual RAID structure insoftware, interposed between the logical drive as seen bythe read/write routines and the physical drives.Although RAID is most commonly used with largeshared storage units (see file server and networkedstorage), with the drastic decline in hard drive prices,simple RAID configurations (such as two mirrored drives)are also appearing in higher-end desktop PCs.Further ReadingLeider, Joel. “How to Select a RAID Disk Array.” Enterprise StorageForum. Available online. URL: http://www.enterprisestorageforum.com/hardware/features/article.php/726491.AccessedNovember 8, 2007.“Redundant Array of Inexpensive Disks (RAID).” PC Guide. Availableonline. URL: http://pcguide.com./ref/hdd/perf/raid/index.htm. Accessed November 8, 2007.random number generationComputer applications such as simulations, games, andgraphics applications often need the ability to generate oneor more random numbers (see simulation and computergames). Random numbers can be defined as numbers thatshow no consistent pattern, with each number in the seriesneither affected in any way by the preceding number, norpredictable from it.One way to get random digits is to simply start with anarbitrary number with a specified number of digits, perhaps10. This first number is called the seed. Multiply the seed bya constant number of the same length, and take that numberof digits off the right end of the product. The result becomesthe new seed. Multiply it by the original constant to generatea new product, and repeat as often as desired. The result isa series of digits that appear randomly distributed as thoughgenerated by throwing a die or spinning a wheel. This typeof algorithm is called a congruential generator.The quality of a random number generator is proportionalto its period, or the number of numbers it can producebefore a repeating pattern sets in. The period for acongruential generator is approximately 2 32 , quite adequatefor many applications. However, for applications such asvery large-scale simulations, different algorithms (calledshift-register and lagged-Fibonacci) can be used, althoughthese also have some drawbacks. Combining two differenttypes of generators produces the best results. The widelyused McGill Random Number Generator Super-Duper combinesa congruential and a shift-register algorithm.Generating a random number series from a single seedwill work fine with most simulations that rely upon generatingrandom events under the control of probabilities(Monte Carlo simulations). However, although the sequenceof numbers generated from a given seed is randomly distributed,it is always the same series of numbers for the sameseed. Thus, a computer poker game that simply used a givenseed would always generate the same hands for each player.What is needed is a large collection of potential seeds fromwhich one can be more or less randomly chosen. If thereare enough possible seeds, the odds of ever getting the sameseries of numbers become vanishingly small.One way to do this is to read the time (and perhaps date)from the computer’s system clock and generate a seed basedon that value. Since the clock value is in milliseconds, thereare millions of possible values to choose from. Anothercommon technique is to use the interval between the user’skeystrokes (in milliseconds). Although they are not perfect,these techniques are quite adequate for games.So-called true random number generators extract randomnumbers from physical phenomena such as a radioactivesource (the HotBits service at Fourmilab in Switzerland)or even atmospheric noise as detected by a radio receiver.For the ultimate in random numbers, researchers havelooked to quantum processes that are inherently random.In 2007 researchers at an institute in Zagreb, Croatia, beganto offer the Quantum Random Bit Generator Service, whichis keyed to unpredictable emissions of photons in a semiconductor.The output of most random number services canbe interfaced with MATLAB and other popular mathematicalsoftware packages.Further ReadingGentle, James E. Random Number Generation and Monte CarloMethods. 2nd ed. New York: Springer, 2004.HotBits: Genuine Random Numbers, Generated by RadioactiveDecay. Available online. URL: http://www.fourmilab.ch/hotbits/. Accessed August 18, 2007.“Introduction to Randomness and Random Numbers.” Availableonline. URL: http://www.random.org. Accessed August 18,2007.real-time processingThere are many computer applications (such as air trafficcontrol or industrial process control) that require that thesystem respond almost immediately to its inputs.In designing a real-time system there are always twoquestions to answer: Will it respond quickly enough mostof the time? How much variation in response time can wetolerate? A system that responds to real-time environmentalconditions (such as the amount of traction or torque actingon a car’s wheels) needs to have a sampling rate and a rateof processing the sampled data that’s fast enough so thatthe system can correct a dangerous condition in time. Theresponsiveness required of course varies with the situation