Real-Time for the Real World ™
Work-in-progress preview of a monograph:
Fundamentals of Timeliness and Predictability
in Dynamically Real-Time Systems [Jensen 202X]
Informally, a system is a real-time one if its core properties of timeliness and predictability of timeliness are integral to its logic, not just performance measures. In general, those properties are dynamic due to inevitable kinds and degrees of imprecisions and uncertainties in open-world system models. Such systems constitute the spectrum of dynamically real-time ones. Traditional (e.g., so-called “hard,” “firm”) real-time computing systems are narrow special (albeit often important) cases whose system models and core properties are predominantly static—or are treated as being (e.g., worst-case execution times). Dynamically real-time systems are often thought of as being instances of “soft” real-time ones, in various different senses. However, none of those senses encompass the essential properties of “dynamic” intrinsic to open-world real-time sysems. Traditional real-time systems have very limited applicability compared to the general dynamic case. Dynamically real-time (including, but not limited to, computing) systems are widely used, often in application domains outside those of the traditional real-time computing field—thus are unfamiliar to that field’s researchers and practitioners. There is a great deal of research and development on the theories associated with the individual constituent properties of dynamic systems, such as imprecision and uncertainty. However, there is a paucity of effort to formulate these results into coherent foundations general enough to express and achieve application-specific timeliness and predictability in an adequate variety of dynamically real-time systems. This monograph summarizes one partial approach to that goal, by providing a mental model for scheduling in general dynamically real-time (including, but not limited to, computing) systems. Timeliness is dynamically expressive using my time/utility (née time/value) functions and utility accrual (née value-based) scheduling paradigm [Jensen 77] [Jensen+ 85] [Time-Utility Function] (TUF/UA). Schedules’ accrued utility and predictability of their timeliness requires reasoning about them with some formal theories of uncertainty. System model parameter imprecision is handled with an imprecise probability theory (such as fuzzy sets). Orthodox probability theory and even Bayesian probability theory cannot deal with the ignorance and paradoxical information which frequently occur in the reality outside of static closed-world system models. This monograph briefly surveys some popular uncertainty theories which can do so, and focuses on applying belief-based theories (i.e., Dempster-Shafer theory and its subsequent derivations, such as the Transferable Belief Model). The foundation summarized here has been successfully employed in a multiplicity of different contexts—notably many classified DoD real-time ones (e.g., battle management, combat and surveillance platform management) having wickedly dynamic system models and core properties, where traditional static real-time perspectives and traditions were inadequate and counter-productive. The time frames of these contexts tend to range from milliseconds to megaseconds. However, from its inception and increasingly since, the TUF/UA paradigm—together with dynamic uncertainty accommodation—have often relied for sufficient performance on hardware augmentations (e.g., using multiprocessors/multicomputers, GPUs, FPGAs, custom ICs).
N.B. There still are pages from the previous (c. 2008-2012) version of this site which I have not yet updated and integrated (or removed).