I've come across articles on "through-put vs latency" in contexts like networking e.g. https://homepage.cs.uri.edu/~thenry/resources/unix_art/ch12s04.html But in the context of computer architecture / operating systems, I'm not able to understand why would there be a trade-off between latency (response time of a program) and through-put (how many programs we're able to complete in a unit of time, say per hour). Is this solely due to the fact that we can choose to parallelize processing of multiple programs / requests leading to overheads like context switches & sharing of caches which make the start-to-end response time per process to be worse? Or am I missing something here?
In terms of single instructions in a superscalar pipelined out-of-order exec CPU, throughput vs. latency is very important because the CPU is trying to extract parallelism from an instruction stream that has to be executed as if in serial program order. See Assembly - How to score a CPU instruction by latency and throughput and the bottom of my answer on latency vs throughput in intel intrinsics for example.
In terms of OS decisions that affect throughput vs. latency on a much longer timescale than a few clock cycles, that's a totally separate question.
One of the major factors there is choosing how to use the available physical RAM, and whether to page out (to a swap file) infrequently used code / data to make more room to cache disk files. (e.g. Linux's vm.swappiness is widely considered a key tunable in terms of setting it differently between servers and desktops. https://unix.stackexchange.com/questions/88693/why-is-swappiness-set-to-60-by-default).
If you alt-tab to a window when many pages of that process have been paged out, it will take some time before the process can redraw its window. (Multiple hard page faults, can be quite slow especially if paging on a rotational disk, not SSD.) So to optimize for latency, you want the kernel to not aggressively swap out pages from running processes, even if they've been idle for a few hours. Those pages, if they'd been free, could have improved throughput for other processes by acting as buffers / cache.
A related factor is I/O scheduling: trying to group IO requests together to minimize HD seek times (for higher throughput and lower average latency), but sometimes at the expense of delaying a few requests for a longer time (higher worst-case latency). Linux for example has many to choose from, including deadline, Completely Fair Queuing (CFQ), and the original elevator (just grouping requests by locality without consideration of fairness or latency). https://wiki.archlinux.org/title/improving_performance#Input/output_schedulers
CPU scheduling is also a factor: a context-switch hurts throughput, as it takes time itself and caches will likely be cold for the new task on this CPU. You also have to run the kernel's schedule() function to decide which task to run next, so that takes away some time from real work.
To minimize latency (for example between a socket message being sent to a process and it waking up when its poll or select system call returns), you want a short timeslice, like Linux HZ=1000. (Timer interrupts every 1 ms to run the scheduler). And you want to be able to pre-empt even the kernel itself, instead of waiting until the kernel is ready to return to the old user-space to consider the possibility of running a different user-space task.
But neither of these helps throughput, and in fact hurt (assuming the workload has enough parallelism to not bottleneck on latency). So HZ=100 was the default for "server" Linux builds, vs. 1000 on "desktop" builds tuned for interactive use. (Modern Linux can be "tickless", not using a fixed timer interrupt on every core at all, instead deciding when to schedule the next interrupt on a case by case basis.)
Real-time kernels take this even further, spending more time on finer-grained locking and stuff like that to enable pausing work and coming back to it later to minimize interrupt latency and other latencies between it being time to do something and actually starting to do that thing. (There are real-time patches for Linux, and there are also totally separate kernels built from the ground up for real-time operation.)
If you have an embedded system controlling a motor or something, you absolutely need hard real-time latency guarantees that it will never take longer than say 1 millisecond from an interrupt pin being asserted to the interrupt handler starting to run.
(Designing the system to make these guarantees possible often comes at the cost of throughput. e.g. obviously you have to pin some memory to make it not swappable, if we're talking about user-space, making it unavailable for cache even if it goes untouched for days.)
Today's computer architecture are trying to maximize the number of registers. It is faster to access a register (which is an integrated memory circuit near the cpu) than to access first-level cache. The problem is, that each context switch has to save all registers into cache, because the next thread needs other register values. What a modern CPU is doing is to cycle in one second through 100 tasks and everytime it saves the registers, and fetches the old one until the task can be started.
IMHO it would be nice to use one CPU for one task, and no context switching is happening. That means we get 100 CPUs, each 1000 registers which has to be never saved. Is that possible or have I a ignored an important detail?
The only way to completely avoid context switching is by having at least as many cores as there are tasks. Generally, there is no guarantee regarding the maximum number of tasks that may run. Current GPUs and manycore processors and co-processors contain hundreds of small cores. If you put multiple of these things in the same system or in a cluster of systems, you can have thousands or more cores. Still, even if you could avoid context switching with such design, these cores are much slower than the traditional high-end CPU cores, so the net effect might be negative.
But let's take a step back here. The number of context switches is not primarily determined by the number of tasks and cores. Tasks don't just perform computations, they also need to interact with I/O devices and wait for things to happen such as results from other tasks or user input. So some tasks would be in a wait state. The overhead of context switching depends on not only the number of tasks but also the behavior of these tasks.
Both processors architects and OS developers are aware of context switching overhead and employ a variety of techniques to alleviate it. For example, x86 provides a number of instructions that are tuned to saving the context (partially) of the current task. The OS thread scheduler uses techniques such as priorities, preemption (with possibly large time slices on servers), and priority boosting. All of these help reducing the number of context switches and therefore their overall overhead. In addition, reducing the overhead of context switching is not the only thing that matters. In particular, the responsiveness of the system is very important as well, which is at odds with that overhead.
I want to know a few application examples where one needs to use RTOS in order to ensure a working system.
I did some google search and whatever examples I found, I feel could be implemented using a windows or linux system.
The primary difference between an RTOS and a GPOS is that an RTOS guarantees deterministic response. That is to say that the worst case response time to an event is precisely bounded (and usually fast). A GPOS schedules processes generally on "balanced load" basis - it assumes that all processes and events are of equal importance and will be allotted a "fair" share of processor resources. For that reason when a process has the CPU, unless it yields "cooperatively" it will have sole use of the CPU for the duration of its time slot (assuming a single core - multi-core processors allow true concurrency, but the GPOS still allots the cores of a balanced load basis). A time slot may be several tens of milliseconds, and the time taken to service a particular process will depend greatly on the number of processes simultaneously demanding CPU time. Outside perhaps of implementing a kernel level driver, achieving timing constraints of a few tens of microseconds (or less) is not possible (or desirable) in a GPOS.
If your application is what Microsoft's marketing used to call "soft" real-time (i.e. not real time at all) that a GPOS may suit. Linux can be built with "real-time" scheduling support, but it does not really make Linux suited to a large set of "hard" real-time tasks, and it is still "soft" in the sense that most of the time it will meet deadlines, but in some outlier conditions it may fail. If that is your medical life-support system, you probably don't want to trust to that!
As an example of an attempt to run essentially real-time tasks on a GPOS that fails, years ago when MMX instructions were added to Pentium processors (running typically at 60MHz then), someone had the bright idea of "Host Signal Processing", a method applied to reduce the cost of PSTN modems (dial-up) by performing the signal processing on the PC rather than using a dedicated processor or DSP in the modem hardware - these "modems" were not really modems at all; they were telephone interfaces and digital converters for modem software. At the time I worked for a company producing PSTN modem test equipment, and we tried one of these early HSP modems, and it worked right up until you launched Microsoft Word (or pretty much any large application), when it would instantly drop the connection. Things improved as PCs became faster, but the point is that it was not guaranteed to work - it just mostly did.
Another example I have worked on is on a carton loading machine in food packaging. The product is inserted into the carton, a glue stripe applied, and the closure folded. The carton is moving continuously during this process an the timing of the glue gun is critical - for a glue stripe to be accurate to within one millimetre on a carton moving at one metre per second requires timing within one millisecond.
Another example is that of TDMA communication as used in digital telephony for example. Such communication allocates a time slot for each stations transmission and failure to transmit in exactly the correct time slot, or encroaching on the time slot of another station is unacceptable. Such systems are globally synchronised to atomic-clock accuracy (typically derived from a GPS receiver). A GSM time slot for example is 577 microseconds, in this time, the transmitter must ramp-up the transmitter power, transmit the data and ramp-down
In short any example that requires 100 percent deterministic timing needs an RTOS. If your timing constraints are say > 100ms, and a small probability of failure to meet timing is tolerable, then a GPOS may work all or most of the time. If timing constraints are sub-millisecond or the cost or consequences of failure unacceptable, then an RTOS is appropriate.
I have read the definitions for the different notions of real-time, and the examples provided for hard and soft real-time systems make sense to me. But, there is no real explanation or example of a firm real-time system. According to the link above:
Firm: Infrequent deadline misses are tolerable, but may degrade the system's quality of service. The usefulness of a result is zero after its deadline.
Is there a clear distinction between firm real-time vs. hard or soft real-time, and is there a good example that illustrates that distinction?
In comments, Charles asked that I submit tag wikis for the new tags. The example of a "firm real-time system" I provided for the firm-real-time tag was a milk serving system. If the system delivers milk after its expiration time, then the milk is considered "not useful". One can tolerate eating cereal without milk, but the quality of the experience is degraded.
This is just the idea I formed in my head when I initially read the definition. I am looking for a much better example, and perhaps a better definition of firm real-time that will improve my notion of it.
Hard Real-Time
The hard real-time definition considers any missed deadline to be a system failure. This scheduling is used extensively in mission critical systems where failure to conform to timing constraints results in a loss of life or property.
Examples:
Air France Flight 447 crashed into the ocean after a sensor malfunction caused a series of system errors. The pilots stalled the aircraft while responding to outdated instrument readings. All 12 crew and 216 passengers were killed.
Mars Pathfinder spacecraft was nearly lost when a priority inversion caused system restarts. A higher priority task was not completed on time due to being blocked by a lower priority task. The problem was corrected and the spacecraft landed successfully.
An Inkjet printer has a print head with control software for depositing the correct amount of ink onto a specific part of the paper. If a deadline is missed then the print job is ruined.
Firm Real-Time
The firm real-time definition allows for infrequently missed deadlines. In these applications the system can survive task failures so long as they are adequately spaced, however the value of the task's completion drops to zero or becomes impossible.
Examples:
Manufacturing systems with robot assembly lines where missing a deadline results in improperly assembling a part. As long as ruined parts are infrequent enough to be caught by quality control and not too costly, then production continues.
A digital cable set-top box decodes time stamps for when frames must appear on the screen. Since the frames are time order sensitive a missed deadline causes jitter, diminishing quality of service. If the missed frame later becomes available it will only cause more jitter to display it, so it's useless. The viewer can still enjoy the program if jitter doesn't occur too often.
Soft Real-Time
The soft real-time definition allows for frequently missed deadlines, and as long as tasks are timely executed their results continue to have value. Completed tasks may have increasing value up to the deadline and decreasing value past it.
Examples:
Weather stations have many sensors for reading temperature, humidity, wind speed, etc. The readings should be taken and transmitted at regular intervals, however the sensors are not synchronized. Even though a sensor reading may be early or late compared with the others it can still be relevant as long as it is close enough.
A video game console runs software for a game engine. There are many resources that must be shared between its tasks. At the same time tasks need to be completed according to the schedule for the game to play correctly. As long as tasks are being completely relatively on time the game will be enjoyable, and if not it may only lag a little.
Siewert: Real-Time Embedded Systems and Components.
Liu & Layland: Scheduling Algorithms for Multiprogramming in a Hard Real-Time Environment.
Marchand & Silly-Chetto: Dynamic Scheduling of Soft Aperiodic Tasks and Periodic Tasks with Skips.
Hard real-time means you must absolutely hit every deadline. Very few systems have this requirement. Some examples are nuclear systems, some medical applications such as pacemakers, a large number of defense applications, avionics, etc.
Firm/soft real time systems can miss some deadlines, but eventually performance will degrade if too many are missed. A good example is the sound system in your computer. If you miss a few bits, no big deal, but miss too many and you're going to eventually degrade the system. Similar would be seismic sensors. If you miss a few datapoints, no big deal, but you have to catch most of them to make sense of the data. More importantly, nobody is going to die if they don't work correctly.
The line is fuzzy, because even a pacemaker can be off by a small amount without killing the patient, but that's the general gist.
It's sort of like the difference between hot and warm. There's not a real divide, but you know it when you feel it.
After reading the Wikipedia page and other pages on real-time computing. I made the following inferences:
1> For a Hard real-time system, if the system fails to meet the deadline even once the system is considered to have Failed.
2> For a Firm real-time system, even if the system fails to meet the deadline, possibly more than once (i.e. for multiple requests), the system is not considered to have failed. Also, the responses for the requests (replies to a query, result of a task, etc.) are worthless once the deadline for that particular request has passed (The usefulness of a result is zero after its deadline). A hypothetical example can be a storm forecast system (if a storm is predicted before arrival, then the system has done its job, prediction after the event has already happened or when it is happening is of no value).
3> For a Soft real-time system, even if the system fails to meet the deadline, possibly more than once (i.e. for multiple requests), the system is not considered to have failed. But, in this case the results of the requests are not worthless value for a result after its deadline, is not zero, rather it degrades as time passes after the deadline. Eg.: Streaming audio-video.
Here is a link to a resource that was very helpful.
It's popular to associate some great catastrophe with the definition of hard real-time, but this is not relevant. Any failure to meet a hard real-time constraint simply means that the system is broken. The severity of the outcome when something is labelled "broken" isn't material to the definition.
Firm and soft simply fail to be automatically declared broken on failing to meet a single deadline.
For a fair example of hard real-time, from the page you linked:
Early video game systems such as the Atari 2600 and Cinematronics vector graphics had hard real-time requirements because of the nature of the graphics and timing hardware.
If something in the video generation loop missed just a single deadline then the whole display would glitch, which would be intolerable, even if it was rare. That would be a broken system and you'd take it back to the shop for a refund. So it's hard real-time.
Obviously any system can be subject to situations it cannot handle, so it's necessary to restrict the definition to being within the expected operating conditions -- noting that in safety-critical applications people must plan for terrible conditions ("the coolant has evaporated", "the brakes have failed", but rarely "the sun has exploded").
And lets not forget that sometimes there's an implicit "while anybody is watching" operating condition. If nobody sees you break the rules (or if they did but they die the fire before telling anyone), and nobody can prove that you broke the rules after the fact, then it's kind of the same as if you never broke the rules!
The simplest way to distinguish between the different kinds of real-time system types is answering the question:
Is a delayed system response (after the deadline) is still useful or not?
So depending on the answer you get for this question, your system could be included as one of the following categories:
Hard: No, and delayed answers are considered a system failure
This is the case when missing the dead-line will make the system unusable. For example the system controlling the car Airbag system should detect the crash and inflate rapidly the bag. The whole process takes more or less one-twenty-fifth of a second. Thus, if the system for example react with 1 second of delay the consequences could be mortal and it will be no benefit having the bag inflated once the car has already crashed.
Firm: No, but delayed answers are not necessary a system failure
This is the case when missing the deadline is tolerable but it will affect the quality of the service. As a simple example consider a video encryption system. Normally the password of encryption is generated in the server (video Head end) and sent to the customer set-top box. This process should be synchronized so normally the set-top box receives the password before starts receiving the encrypted video frames. In this case a delay it may lead to video glitches since the set-top box is not able to decode the frames because it hasn't received the password yet. In this case the service (film, an interesting football match, etc) could be affected by not meeting the deadline. Receiving the password with delay in this case is not useful since the frames encrypted with the same have already caused the glitches.
Soft: Yes, but the system service is degraded
As from the the wikipedia description the usefulness of a result degrades after its deadline. That means, getting a response from the system out of the deadline is still useful for the end user but its usefulness degrade after reaching the deadline. A simple example for this case is a software that automatically controls the temperature of a room (or a building). In this case if the system has some delays reading the temperature sensors it will be a little bit slow to react upon brusque temperature changes. However, at the end it will end up reacting to the change and adjusting accordingly the temperature to keep it constant for example. So in this case the delayed reaction is useful, but it degrades the system quality of service.
A soft real time is easiest to understand, in which even if the result is obtained after the deadline, the results are still considered as valid.
Example: Web browser- We request for certain URL, it takes some time in loading the page. If the system takes more than expected time to provide us with the page, the page obtained is not considered as invalid, we just say that the system's performance wasn't up to the mark (system gave low performance!).
In hard real time system, if the result is obtained after the deadline, the system is considered to have failed completely.
Example: In case of a robot doing some job like line tracing, etc. If a hindrance comes on its path, and the robot doesn't process this information within some programmed deadline (almost instant!), the robot is said to have failed in its task (the robot system may also get completely destroyed!).
In firm real time system, if the result of process execution comes after the deadline, we discard that result, but the system is not termed to have been failed.
Example: Satellite communication for enemy position monitoring or some other task. If the ground computer station to which the satellites send the frames periodically is overloaded, and the current frame (packet) is not processed in time and the next frame comes up, the current packet (the one who missed the deadline) doesn't matter whether the processing was done (or half done or almost done) is dropped/discarded. But the ground computer is not termed to have completely failed.
To define "soft real-time," it is easiest to compare it with "hard real-time." Below we will see that the term "firm real-time" constitutes a misunderstanding about "soft real-time."
Speaking casually, most people implicitly have an informal mental model that considers information or an event as being "real-time"
• if, or to the extent that, it is manifest to them with a delay (latency) that can be related to its perceived currency
• i.e., in a time frame that the information or event has acceptably satisfactory value to them.
There are numerous different ad hoc definitions of "hard real-time," but in that mental model, hard real-time is represented by the "if" term. Specifically, assuming that real-time actions (such as tasks) have completion deadlines, acceptably satisfactory value of the event that all tasks complete is limited to the special case that all tasks meet their deadlines.
Hard real-time systems make the very strong assumptions that everything about the application and system and environment is static and known a' priori—e.g., which tasks, that they are periodic, their arrival times, their periods, their deadlines, that they won’t have resource conflicts, and overall the time evolution of the system. In an aircraft flight control system or automotive braking system and many other cases those assumptions can usually be satisfied so that all the deadlines will be met.
This mental model is deliberately and very usefully general enough to encompass both hard and soft real-time--soft is accommodated by the "to the extent that" phrase. For example, suppose that the task completions event has suboptimal but acceptable value if
no more than 10% of the tasks miss their deadlines
or no task is more than 20% tardy
or the average tardiness of all tasks is no more than 15%
or the maximum tardiness among all tasks is less than 10%
These are all common examples of soft real-time cases in a great many applications.
Consider the single-task application of picking your child up after school. That probably does not have an actual deadline, instead there is some value to you and your child based on when that event takes place. Too early wastes resources (such as your time) and too late has some negative value because your child might be left alone and potentially in harm's way (or at least inconvenienced).
Unlike the static hard real-time special case, soft real-time makes only the minimum necessary application-specific assumptions about the tasks and system, and uncertainties are expected. To pick up your child, you have to drive to the school, and the time to do that is dynamic depending on weather, traffic conditions, etc. You might be tempted to over-provision your system (i.e., allow what you hope is the worst case driving time) but again this is wasting resources (your time, and occupying the family vehicle, possibly denying use by other family members).
That example may not seem to be costly in terms of wasted resources, but consider other examples. All military combat systems are soft real-time. For example, consider performing an aircraft attack on a hostile ground vehicle using a missile guided with updates to it as the target maneuvers. The maximum satisfaction for completing the course update tasks is achieved by a direct destructive strike on the target. But an attempt to over-provision resources to make certain of this outcome is usually far too expensive and may even be impossible. In this case, you may be less but sufficiently satisfied if the missile strikes close enough to the target to disable it.
Obviously combat scenarios have a great many possible dynamic uncertainties that must be accommodated by the resource management. Soft real-time systems are also very common in many civilian systems, such as industrial automation, although obviously military ones are the most dangerous and urgent ones to achieve acceptably satisfactory value in.
The keystone of real-time systems is "predictability." The hard real-time case is interested in only one special case of predictability--i.e., that the tasks will all meet their deadlines and the maximum possible value will be achieved by that event. That special case is named "deterministic."
There is a spectrum of predictability. Deterministic (determinism) is one end-point (maximum predictability) on the predictability spectrum; the other end-point is minimum predictability (maximum non-determinism). The spectrum's metric and end-points have to be interpreted in terms of a chosen predictability model; everything between those two end-points is degrees of unpredictability (= degrees of non-determinism).
Most real-time systems (namely, soft ones) have non-deterministic predictability, for example, of the tasks' completions times and hence the values gained from those events.
In general (in theory), predictability, and hence acceptably satisfactory value, can be made as close to the deterministic end-point as necessary--but at a price which may be physically impossible or excessively expensive (as in combat or perhaps even in picking up your child from school).
Soft real-time requires an application-specific choice of a probability model (not the common frequentist model) and hence predictability model for reasoning about event latencies and resulting values.
Referring back to the above list of events that provide acceptable value, now we can add non-deterministic cases, such as
the probability that no task will miss its deadline by more than 5% is greater than 0.87. (Note the number of scheduling criteria expressed in there.)
In a missile defense application, given the fact that in combat the offense always has the advantage over the defense, which of these two real-time computing scenarios would you prefer:
because the perfect destruction of all the hostile missiles is very unlikely or impossible, assign your defensive resources to maximize the probability that as many of the most threatening (e.g., based on their targets) hostile missiles will be successfully intercepted (close interception counts because it can move the hostile missile off-course);
complain that this is not a real-time computing problem because it is dynamic instead of static, and traditional real-time concepts and techniques do not apply, and it sounds more difficult than static hard real-time, so you are not interested in it.
Despite the various misunderstandings about soft real-time in the real-time computing community, soft real-time is very general and powerful, albeit potentially complex compared with hard real-time. Soft real-time systems as summarized here have a lengthy successful history of use outside the real-time computing community.
To directly answer the OP question:
A hard real-time system can provide deterministic guarantees—most commonly that all tasks will meet their deadlines, interrupt or system call response time will always be less than x, etc.—IF AND ONLY IF very strong assumptions are made and are correct that everything that matters is static and known a' priori (in general, such guarantees for hard real-time systems are an open research problem except for rather simple cases)
A soft real-time system does not make deterministic guarantees, it is intended to provide the best possible analytically specified and accomplished probabilistic timeliness and predictability of timeliness that are feasible under the current dynamic circumstances, according to application-specific criteria.
Obviously hard real-time is a simple special case of soft real-time. Obviously soft real-time's analytical non-deterministic assurances can be very complex to provide, but are mandatory in the most common real-time cases (including the most dangerous safety-critical ones such as combat) since most real-time cases are dynamic not static.
"Firm real-time" is an ill-defined special case of "soft real-time." There is no need for this term if the term "soft real-time" is understood and used properly.
I have a more detailed much more precise discussion of real-time, hard real-time, soft real-time, predictability, determinism, and related topics on my web site real-time.org.
real-time - Pertaining to a system or mode of operation in which computation is performed during the actual time that an external process occurs, in order that the computation results can be used to control, monitor, or respond to the external process in a timely manner. [IEEE Standard 610.12.1990]
I know this definition is old, very old. I can't, however, find a more recent definition by the IEEE (Institute of Electrical and Electronics Engineers).
Consider a task that inputs data from the serial port. When new data arrives the serial port triggers an event. When the software services that event, it reads and processes the new data. The serial port has a hardware to store incoming data (2 on the MSP432, 16 on the TM4C123) such that if the buffer is full and more data arrives, the new data is lost. Is this system hard, firm, or soft real time?
It is hard real time because if the response is late, data may be lost.
Consider a hearing aid that inputs sounds from a microphone, manipulates the sound data, and then outputs the data to a speaker. The system usually has small and bounded jitter, but occasionally other tasks in the hearing aid cause some data to be late, causing a noise pulse on the speaker. Is this system hard, firm or soft real time?
It is firm real time because it causes an error that can be perceived but the effect is harmless and does not significantly alter the quality of the experience.
Consider a task that outputs data to a printer. When the printer is idle the printer triggers an event. When the software services that event, it sends more data to the printer. Is this system hard, firm or soft real time?
It is soft real time because the faster it responses the better, but the value of the system (bandwidth is amount of data printed per second) diminishes with latency.
UTAustinX: UT.RTBN.12.01x Realtime Bluetooth Networks
Maybe the definition is at fault.
From my experience, I would separate the two as being hardware and software dependant.
If you have 200ms to service a hardware driven interrupt, that is what you've got. You stick 300ms of code in there and the system isn't broken, it hasn't been developed. You'll be switched out before you've finished. Your code doesn't work or is not fit for purpose. Many systems have hard defined processing periods. Video, telecoms etc.
If you're writing an application that's real-time, this could be considered soft. If you run out of time you could hope for less load next time, you could tune the OS, add some memory or even upgrade the hardware. You have options.
To look at it from a UX perspective is not helpful. A Skoda might not be broken if it glitches, but a BMW sure as hell will be.
The definition has expanded over the years to the detriment of the term. What is now called "Hard" real-time is what used to be simply called real-time. So systems in which missing timing windows (rather than single-sided time deadlines) would result incorrect data or incorrect behavior should be consider real-time. Systems without that characteristic would be considered non-real-time.
That's not to say that time isn't of interest in non-real-time systems, it just means that timing requirements in such systems don't result in fundamentally incorrect results.
Hard real time systems uses preemptive version of priority scheduling, so that critical tasks get immediately scheduled, whereas soft real time systems uses non-preemptive version of the priority scheduling, which allows the present task to be finished before control is transferred to the higher priority task, causing additional delays. Thus the task deadlines are critically followed in Hard real time systems, whereas in soft real time systems they are handled not that seriously.