Hard And Soft Timing Constraints

HARD AND SOFT TIMING CONSTRAINTS: It is common to divide timing constraints into two types: hard and soft. There are many definitions of hard and soft real-time constraints. Before stating the definition used in this book, let us first look at three frequently encountered definitions so you will be aware of them. They are based on the functional criticality of jobs, usefulness of late results, and deterministic or probabilistic nature of the constraints.

Common Definitions: According to a commonly used definition, a timing constraint or deadline is hard if the failure to meet it is considered to be a fatal fault. A hard deadline is imposed on a job because a late result produced by the job after the deadline may have disastrous consequences. (As examples, a late command to stop a train may cause a collision, and a bomb dropped too late may hit a civilian population instead of the intended military target.) In contrast, the late completion of a job that has a soft deadline is undesirable. However, a few misses of soft deadlines do no serious harm; only the system’s overall performance becomes poorer and poorer when more and more jobs with soft deadlines complete late. This definition of hard and soft deadlines invariably leads to the question of whether the consequence of a missed deadline is indeed serious enough. The question of whether a timing constraint is hard or soft degenerates to that of how serious is serious.

In real-time systems literature, the distinction between hard and soft timing constraints is sometimes stated quantitatively in terms of the usefulness of results (and therefore the overall system performance) as functions of the tardinesses of jobs. The tardiness of a job measures how late it completes respective to its deadline. Its tardiness is zero if the job completes at or before its deadline; otherwise, if the job is late, its tardiness is equal to the difference between its completion time (i.e., the time instant at which it completes execution) and its deadline. The usefulness of a result produced by a soft real-time job (i.e, a job with a soft deadline) decreases gradually as the tardiness of the job increases, but the usefulness of a result produced by a hard real-time job (i.e., a job with a hard deadline) falls off abruptly and may even become negative when the tardiness of the job becomes larger than zero. The deadline of a job is softer if the usefulness of its result decreases at a slower rate. By this means, we can define a spectrum of hard/soft timing constraints. This quantitative measure of hardness and softness of deadlines is sometimes useful. It is certainly more appealing to computer scientists and engineers who have been trained not to rely on handwaving, qualitative measures. However, there is often no natural choice of usefulness functions. When choices are made, it is difficult to validate that the choices are sound and that different measures of the overall system performance as functions of tardinesses indeed behave as specified by the usefulness functions. Consequently, this kind of quantitative measure is not as rigorous as it appears to be.

Sometimes, we see this distinction made on the basis of whether the timing constraint is expressed in deterministic or probabilistic terms. If a job must never miss its deadline, then the deadline is hard. On the other hand, if its deadline can be missed occasionally with some acceptably low probability, then its timing constraint is soft. An example is that the system recovery job or a point-of-sales transaction completes within one minute 99.999 percent of the time. In other words, the probability of failure to meet the one-minute relative deadline is 10−5. This definition ignores completely the consequence of a timing failure. In our example, if the failure of an on-time recovery could cause loss of life and property, we would require a rigorous demonstration that the timing failure probability is indeed never more than 10−5. However, we would not require a demonstration of nearly the same rigor for a credit validation.

Hard Timing Constraints and Temporal Quality-of-Service Guarantees: In most of this book, we adopt a simple operational definition: The timing constraint of a job is hard, and the job is a hard real-time job, if the user requires the validation that the system always meet the timing constraint. By validation, we mean a demonstration by a provably correct, efficient procedure or by exhaustive simulation and testing. A large part of this book is devoted to efficient validation algorithms and methods as well as scheduling and resource management strategies that allow the system to be thus validated.

On the other hand, if no validation is required, or only a demonstration that the job meet some statistical constraint (i.e., a timing constraint specified in terms of statistical averages) suffices, then the timing constraint of the job is soft. The satisfaction of statistical constraints (e.g., the average number of missed deadlines per minute is two or less) can usually be demonstrated with a performance profile somewhat more thorough than those used to demonstrate the performance of general interactive systems. Most of the techniques for validation discussed in later chapters are not needed.

This way to differentiate between hard and soft timing constraints is compatible with the distinction between guaranteed and best-effort services [Lock86, Clar90]. Stated another way, if the user wants the temporal quality (e.g., response time and jitter) of the service provided by a task guaranteed and the satisfaction of the timing constraints defining the temporal quality validated, then the timing constraints are hard. On the other hand, if the user demands the best quality of service the system can provide but allows the system to deliver qualities below what is defined by the timing constraints, then the timing constraints are soft.

We call an application (task) with hard timing constraints a hard real-time application and a system containing mostly hard real-time applications a hard real-time system. For many traditional hard real-time applications (e.g., digital controllers), all the tasks and jobs executed in every operation mode of the system are known a priori. The traditional approach to building and validating such systems is to avoid hardware and software features that may lead to nondeterminism. Therefore, it is possible to verify the satisfaction of all hard timing constraints by exhaustive simulation and testing. Indeed, until recently, this has been the only approach used to build hard real-time systems.

In recent years, several efficient validation methods for a large class of hard real-time applications have been developed. These methods make on-line validation feasible and, thus, make hard real-time applications that dynamically create and destroy tasks feasible. When an application creates a new task with hard timing constraints, it submits an admission request to the scheduler. Upon the receipt of such a request, the scheduler does an acceptance test to determine whether the system can meet the timing constraints of the new task while meeting all the hard timing constraints of tasks previously admitted into the system. The scheduler accepts and admits the new task to the system only when the task passes the acceptance test. This acceptance test is an on-line validation test. Many of the validation algorithms described in Chapters 6–9 are suitable for this purpose.