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We followed the suggestions you gave us at the multicore workshop in Kaiserlsautern on October. For other people: it is a real-time scheduling policy based on the Earliest Deadline First EDF algorithm, one of the most common real-time scheduling algorithms.

The involved partners which include Ericsson Research, Evidence Srl, AKAtech strongly believe that a general-purpose operating system like Linux should provide a standard real-time scheduling policy like the one implemented in this patch still allowing to schedule non-real-time tasks in the usual way. This new scheduling class will also be the topic of a paper and a talk at the next Real-Time Linux Workshop in Dresden.

The full patch is available as attachment of this e-mail. For easy of review, a split patch-set will come soon. Why do we need a further scheduling class? The existing scheduling classes i. However, 1. In fact, although it is possible to assign a share of the processor time to a task or a group of tasksthere is no way to specify that a task must execute for 20msec within msec, unlike using a real-time scheduling algorithm, such as EDF.

rt bandwidth constraints enforced by hierarchical dl scheduling

As a proof of concept, we implemented a very simple test to run two tasks that need to execute for 20msec every 50msec. We scheduled them using CFS with the same CPU share, and we observed that even if there is enough spare CPU time the system is idlethe tasks occasionally experience some deadline miss. The test is available on the project website see below. Using EDF, instead, this time is deterministic, bounded and known at any instant of time.

These issues are particularly critical when running time-sensitive or control applications.

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Without a real-time scheduler, in fact, it is not possible to make any feasibility study of the system under development, and developers cannot be sure that the timing requirements will be met under any circumstance.

And we feel that this prevents the usage of Linux in industrial contexts like the one addressed by our project. A key feature of our scheduling class is that "temporal isolation" is ensured. This means that the temporal behavior of each task i. In other words, even if a task misbehaves, it is not able to exploit larger execution times than the amount it has been allocated. At any instant of time, the system schedules the ready task having earliest deadline.

During execution, task's budget is decreased by an amount equal to the time executed. When task's budget reaches zero i. At that time, the task is made runnable again, its budget is refilled and a new deadline is computed.Is giving all control of congestion to the TCP layer really the only option?

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As the Internet has evolved, so have situations in which we may not want routers handling all traffic on a first-come, first-served basis. But even without real-time traffic, we might be interested in guaranteeing that each of several customers gets an agreed-upon fraction of bandwidth, regardless of what the other customers are receiving. This may make the first customer quite unhappy. The fundamental mechanism for achieving these kinds of traffic-management goals in a shared network is through queuing ; that is, in deciding how the routers prioritize traffic.

In this chapter we will take a look at what router-based strategies are available in the toolbox; in the following chapter we will study how some of these ideas have been applied to develop distributed quality-of-service options. Previously, in These are examples of queuing disciplinesa catchall term for anything that supports a way to accept and release packets. The RED gateway strategy Queuing disciplines provide tools for meeting administratively imposed constraints on traffic.

Alternatively, a sender might be required not to send in bursts of more than 10 packets at a time. Closely allied to the idea of queuing is scheduling : deciding what packets get sent when. While priority queuing is one practical alternative to FIFO queuing, we will also look at so-called fair queuingin both flat and hierarchical forms.

Fair queuing provides a straightforward strategy for dividing bandwidth among multiple senders according to preset percentages. Also introduced here is the token-bucket mechanism, which can be used for traffic scheduling but also for traffic description.

Some of the material here — in particular that involving fair queuing and the Parekh-Gallager theorem — may give this chapter a more mathematical feel than earlier chapters. Mostly, however, this is confined to the proofs; the claims themselves are more straightforward. One application for advanced queuing mechanisms is to support real-time transport — that is, traffic with delay constraints on delivery.

In its original conception, the Internet was arguably intended for non-time-critical transport. If you wanted to place a digital phone call where every or almost every byte was guaranteed to arrive within 50 ms, your best bet might be to use the separate telephone network instead.

And, indeed, having an entirely separate network for real-time transport is definitely a workable solution. It is, however, expensive; there are many economies of scale to having just a single network.

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There is, therefore, a great deal of interest in figuring out how to get the Internet to support real-time traffic directly. The central strategy for mixing real-time and bulk traffic is to use queuing disciplines to give the real-time traffic the service it requires. Priority queuing is the simplest mechanism, though the fair-queuing approach below offers perhaps greater flexibility.

We round out the chapter with the Parekh-Gallager theorem, which provides a precise delay bound for real-time traffic that shares a network with bulk traffic.

All that is needed is that the real-time traffic satisfies a token-bucket specification and is assigned bandwidth guarantees through fair queuing; the volume of bulk traffic does not matter. This is exactly what is needed for real-time support. While this chapter contains some rather elegant theory, it is not at all clear how much it is put into practice today, at least for real-time traffic at the ISP level. We will return to this issue in the following chapter, but for now we acknowledge that real-time traffic management in general has seen limited adoption.

Even if none of your traffic has real-time constraints, you still may wish to allocate bandwidth according to administratively determined percentages. If you are an ISP, or the manager of a public Wi-Fi access point, you might wish to guarantee that everyone gets a roughly equal share of the available bandwidth, or, alternatively, that no one gets more bandwidth than they paid for. If you want any unused capacity to be divided among the non-idle users, fair queuing is the tool of choice, though in some contexts it may benefit from cooperation from your ISP.

If the users are more like customers receiving only the bandwidth they pay for, you might want to enforce flat caps even if some bandwidth thus goes unused; token-bucket filtering would then be the way to go. If bandwidth allocations are not only by department or customer but also by workgroup or customer-specific subcategorythen hierarchical queuing offers the necessary control.

In general, network management divides into managing the hardware and managing the traffic; the tools in this chapter address this latter component.

Unlike support for real-time traffic, above, use of traffic management is widespread throughout the Internet, though often barely visible. To get started, let us fill in the details for priority queuingwhich we looked at briefly in Here a given outbound interface can be thought of as having two or more physical queues representing different priority levels.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Server Fault is a question and answer site for system and network administrators. It only takes a minute to sign up. Instead of giving a linear service curve as with pretty much every other scheduling algorithmyou can specify a convex or concave service curve and thus it is possible to decouple bandwidth and delay.

However, even though this paper mentions to kind of scheduling algorithms being used real-time and link-shareit always only mentions ONE curve per scheduling class the decoupling is done by specifying this curve, only one curve is needed for that.

Both implementations added two additional service curves, that were NOT in the original paper! A real-time service curve and an upper-limit service curve.

Again, please note that the original paper mentions two scheduling algorithms real-time and link-sharebut in that paper both work with one single service curve.

There never have been two independent service curves for either one as you currently find in BSD and Linux. This all makes the HFSC scheduling even more complex than the algorithm described in the original paper and there are tons of tutorials on the Internet that often contradict each other, one claiming the opposite of the other one.

This is probably the main reason why nobody really seems to understand how HFSC scheduling really works. Before I can ask my questions, we need a sample setup of some kind.

I'll use a very simple one as seen in the image below:. What for do I need a real-time curve at all? What would this be good for?

To give those two a higher priority? According to original paper I can give them a higher priority by using a curvethat's what HFSC is all about after all.

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Does the real-time bandwidth count towards the link-share bandwidth? So does each class has a bandwidth "requirement" of real-time plus link-share? Or does a class only have a higher requirement than the real-time curve if the link-share curve is higher than the real-time curve current link-share requirement equals specified link-share requirement minus real-time bandwidth already provided to this class?

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Is upper limit curve applied to real-time as well, only to link-share, or maybe to both? Some tutorials say one way, some say the other way. What is the truth? If I use the seperate real-time curve to increase priorities of classes, why would I need "curves" at all?

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Why is not real-time a flat value and link-share also a flat value? Why are both curves? The need for curves is clear in the original paper, because there is only one attribute of that kind per class.

But now, having three attributes real-time, link-share, and upper-limit what for do I still need curves on each one? Why would I want the curves shape not average bandwidth, but their slopes to be different for real-time and link-share traffic? According to the little documentation available, real-time curve values are totally ignored for inner classes class A and Bthey are only applied to leaf classes A1, A2, B1, B2.

rt bandwidth constraints enforced by hierarchical dl scheduling

Isn't that completely pointless? Which one is right or are they maybe both wrong? One tutorial said you shall forget all the theory. No matter how things really work schedulers and bandwidth distributionimagine the three curves according to the following "simplified mind model": real-time is the guaranteed bandwidth that this class will always get.

In case there is excess bandwidth, the class might even get offered more bandwidth than necessary to become satisfied, but it may never use more than upper-limit says. And if assumption above is really accurate, where is prioritization in that model?

In that case I must still prioritize somehow, even among real-time traffic of those classes. Would I prioritize by the slope of the curves?Tasks are then scheduled using EDF[1] on these scheduling deadlines the task with the earliest scheduling deadline is selected for execution. Summing up, the CBS[2,3] algorithm assigns scheduling deadlines to tasks so that each task runs for at most its runtime every period, avoiding any interference between different tasks bandwidth isolationwhile the EDF[1] algorithm selects the task with the earliest scheduling deadline as the one to be executed next.

These two parameters are initially set to 0. When the current time is equal to the replenishment time of a throttled task, the scheduling deadline and the remaining runtime are updated as:.

When a task blocks, it does not become immediately inactive since its bandwidth cannot be immediately reclaimed without breaking the real-time guarantees. It therefore enters a transitional state called ActiveNonContending. When an inactive task wakes up, it enters the ActiveContending state and its utilization is added to the active utilization of the runqueue where it has been enqueued. The algorithm reclaims the bandwidth of the tasks in Inactive state.

It does so by decrementing the runtime of the executing task Ti at a pace equal to. This behavior is currently implemented only for ARM architectures. A particular care must be taken in case the time needed for changing frequency is of the same order of magnitude of the reservation period.

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In such cases, setting a fixed CPU frequency results in a lower amount of deadline misses. Anyway, we strongly recommend to come back here and continue reading once the urge for testing is satisfied :P to be sure of fully understanding all technical details. There are no limitations on what kind of task can exploit this new scheduling discipline, even if it must be said that it is particularly suited for periodic or sporadic real-time tasks that need guarantees on their timing behavior, e.

A typical real-time task is composed of a repetition of computation phases task instances, or jobs which are activated on a periodic or sporadic fashion.

Summing up, a real-time task can be described as. The utilization of a real-time task is defined as the ratio between its WCET and its period or minimum inter-arrival timeand represents the fraction of CPU time needed to execute the task. Moreover, if the total utilization is larger than M, then we risk starving non- real-time tasks by real-time tasks.

If, instead, the total utilization is smaller than M, then non real-time tasks will not be starved and the system might be able to respect all the deadlines. As a matter of fact, in this case it is possible to provide an upper bound for tardiness defined as the maximum between 0 and the difference between the finishing time of a job and its absolute deadline.

More precisely, it can be proven that using a global EDF scheduler the maximum tardiness of each task is smaller or equal than. It is important to notice that this condition is only sufficient, and not necessary: there are task sets that are schedulable, but do not respect the condition.

If h t is smaller than t that is, the amount of time needed by the tasks in a time interval of size t is smaller than the size of the interval for all the possible values of t, then EDF is able to schedule the tasks respecting all of their deadlines.

Since performing this check for all possible values of t is impossible, it has been proven[4,5,6] that it is sufficient to perform the test for values of t between 0 and a maximum value L. The cited papers contain all of the mathematical details and explain how to compute h t and L.

In any case, this kind of analysis is too complex as well as too time-consuming to be performed on-line.Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy. See our Privacy Policy and User Agreement for details. If you wish to opt out, please close your SlideShare account.

Learn more. Published on Mar 3, In this presentation, Juri Lelli, after giving a very briefly review of the current set of features, will deep dive into the details of all the new features currently under development: CPU capacity and clock frequency scaling, bandwidth reclaiming, coupling with clock frequency selection and cgroups support.

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