SOFT REAL-TIME SYSTEMS
A system in which jobs have soft deadlines is a soft real-time system. The developer of a soft real-time system is rarely required to prove rigorously that the system surely meet its realtime performance objective. Examples of such systems include on-line transaction systems and telephone switches, as well as electronic games. The less rigorous validation required of the system and, often, more relaxed timing constraints allow the developer to consider other performance metrics equally seriously. Meeting all deadlines is not the only consideration, sometimes, not even the primary consideration. An occasional missed deadline or aborted execution is usually considered tolerable; it may be more important for such a system to have a small average response time and high throughput.
A system may have critical timing requirements but is nevertheless considered to be a soft real-time system. An example is a stock price quotation system. It should update the price of each stock each time the price changes. Here, a late update may be highly undesirable, because the usefulness of a late price quotation decreases rapidly with time. However, in a volatile market when prices fluctuate at unusually high rates, we expect that the system cannot keep up with every price change but does its best according to some criteria. Occasional late or missed updates are tolerated as a trade-off for other factors, such as cost and availability of the system and the number of users the system can serve.
The timing requirements of soft real-time systems are often specified in probabilistic terms. Take a telephone network for example. In response to our dialing a telephone number, a sequence of jobs executes in turn, each routes the control signal from one switch to another in order to set up a connection through the network on our behalf. We expect that our call will be put through in a short time. To ensure that this expectation is met most of the time by the network, a timing constraint may be imposed on this sequence of jobs as a design objective (e.g., the sequence must complete in no more than 10 seconds for 95 percent of the time and in no more than 20 seconds for 99.95 percent of the time). The users are usually satisfied if after extensive simulation and trial use, the system indeed appears to meet this requirement.
As a final example, let us consider multimedia systems that provide the user with services of “guaranteed” quality. For example, a frame of a movie must be delivered every thirtieth of a second, and the difference in the times when each video frame is displayed and when the accompanied speech is presented should be no more than 80 msec. In fact, it is common to subject each new video stream to be transmitted by a network to an acceptance test. If the network cannot guarantee the satisfaction of timing constraints of the stream without violating the constraints of existing streams, the new stream is rejected, and its admission is requested again at some later time. However, the users are often willing to tolerate a few glitches, as long as the glitches occur rarely and for short lengths of time. At the same time, they are not willing to pay the cost of eliminating the glitches completely. For this reason, we often see timing constraints of multimedia systems guaranteed on a statistical basis, (e.g., the average number of late/lost frames per minute is less than 2). Moreover, users of such systems rarely demand any proof that the system indeed honor its guarantees. The quality-of-service guarantee is soft, the validation requirement is soft, and the timing constraints defining the quality are soft
