Chapter 8: MLFQ Basic Rules

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• Explain the main ideas and vocabulary in MLFQ Basic Rules.
• Work through the source examples for MLFQ Basic Rules without depending on raw chunk order.
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Prerequisites

• Earlier prerequisite concepts leading into Chapter 8: MLFQ Basic Rules.

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• module-01-processes-scheduling

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• Raw source file: 040-8-1-mlfq-basic-rules.md
• Raw source file: 041-8-2-attempt-1-how-to-change-priority.md
• Raw source file: 042-8-5-tuning-mlfq-and-other-issues.md

Merged source

MLFQ Basic Rules

8.1 MLFQ: Basic Rules

M. Blasgen, J. Gray, M. Mitoma, T. Price

ACM Operating Systems Review, 13:2, April 1979

Perhaps the first reference to convoys, which occurs in databases as well as the OS.

[C54] "Priority Assignment in Waiting Line Problems"

A. Cobham

Journal of Operations Research, 2:70, pages 70-76, 1954

The pioneering paper on using an SJF approach in scheduling the repair of machines.

[K64] "Analysis of a Time-Shared Processor"

Leonard Kleinrock

Naval Research Logistics Quarterly, 11:1, pages 59-73, March 1964

May be the first reference to the round-robin scheduling algorithm; certainly one of the first analyses of said approach to scheduling a time-shared system.

[CK68] "Computer Scheduling Methods and their Countermeasures"

Edward G. Coffman and Leonard Kleinrock

AFIPS '68 (Spring), April 1968

An excellent early introduction to and analysis of a number of basic scheduling disciplines.

[J91] "The Art of Computer Systems Performance Analysis:

Techniques for Experimental Design, Measurement, Simulation, and Modeling"

R. Jain

Interscience, New York, April 1991

The standard text on computer systems measurement. A great reference for your library, for sure.

[O45] "Animal Farm"

George Orwell

Secker and Warburg (London), 1945

A great but depressing allegorical book about power and its corruptions. Some say it is a critique of

Stalin and the pre-WWII Stalin era in the U.S.S.R; we say it's a critique of pigs.

[PV56] "Machine Repair as a Priority Waiting-Line Problem"

Thomas E. Phipps Jr. and W. R. Van Voorhis

Operations Research, 4:1, pages 76-86, February 1956

Follow-on work that generalizes the SJF approach to machine repair from Cobham's original work; also postulates the utility of an STCF approach in such an environment. Specifically, "There are certain types of repair work, ... involving much dismantling and covering the floor with nuts and bolts, which certainly should not be interrupted once undertaken; in other cases it would be inadvisable to continue work on a long job if one or more short ones became available (p.81)."

[MB91] "The effect of context switches on cache performance"

Jeffrey C. Mogul and Anita Borg

ASPLOS, 1991

A nice study on how cache performance can be affected by context switching; less of an issue in today's systems where processors issue billions of instructions per second but context-switches still happen in the millisecond time range.

[W15] "You can't have your cake and eat it" http://en.wikipedia.org/wiki/Youcan'thaveyourcakeandeatit

Wikipedia, as of December 2015

The best part of this page is reading all the similar idioms from other languages. In Tamil, you can't "have both the moustache and drink the soup."

This program,scheduler.py, allows you to see how different schedulers perform under scheduling metrics such as response time, turnaround time, and total wait time. See the README for details.

Questions

1. Compute the response time and turnaround time when running

three jobs of length 200 with the SJF and FIFO schedulers.

2. Now do the same but with jobs of different lengths: 100, 200, and

4. Now do the same, but also with the RR scheduler and a time-slice

of 1.

4. For what types of workloads does SJF deliver the same turnaround

times as FIFO?

5. For what types of workloads and quantum lengths does SJF deliver

the same response times as RR?

6. What happens to response time with SJF as job lengths increase?

Can you use the simulator to demonstrate the trend?

7. What happens to response time with RR as quantum lengths in-

crease? Can you write an equation that gives the worst-case response time, givenNjobs?

8

Scheduling:

The Multi-Level Feedback Queue

In this chapter, we'll tackle the problem of developing one of the most well-known approaches to scheduling, known as theMulti-level Feedback Queue (MLFQ). The Multi-level Feedback Queue (MLFQ) scheduler was first described by Corbato et al. in 1962 [C+62] in a system known as the Compatible Time-Sharing System (CTSS), and this work, along with later work on Multics, led the ACM to award Corbato its highest honor, theTuring Award. The scheduler has subsequently been refined throughout the years to the implementations you will encounter in some modern systems.

The fundamental problem MLFQ tries to address is two-fold. First, it would like to optimizeturnaround time, which, as we saw in the previous

note, is done by running shorter jobs first; unfortunately, the OS doesn't generally know how long a job will run for, exactly the knowledge that algorithms like SJF (or STCF) require. Second, MLFQ would like to make a system feel responsive to interactive users (i.e., users sitting and staring at the screen, waiting for a process to finish), and thus minimizeresponse time; unfortunately, algorithms like Round Robin reduce response time but are terrible for turnaround time. Thus, our problem: given that we in general do not know anything about a process, how can we build a scheduler to achieve these goals? How can the scheduler learn, as the system runs, the characteristics of the jobs it is running, and thus make better scheduling decisions?

THECRUX:

HOWTOSCHEDULEWITHOUTPERFECTKNOWLEDGE?

How can we design a scheduler that both minimizes response time for interactive jobs while also minimizing turnaround time withouta priori knowledge of job length?

1

TIP: LEARNFROMHISTORY

The multi-level feedback queue is an excellent example of a system that learns from the past to predict the future. Such approaches are common in operating systems (and many other places in Computer Science, including hardware branch predictors and caching algorithms). Such approaches work when jobs have phases of behavior and are thus predictable; of course, one must be careful with such techniques, as they can easily be wrong and drive a system to make worse decisions than they would have with no knowledge at all.

To build such a scheduler, in this chapter we will describe the basic algorithms behind a multi-level feedback queue; although the specifics of many implemented MLFQs differ [E95], most approaches are similar.

In our treatment, the MLFQ has a number of distinct queues, each assigned a differentpriority level. At any given time, a job that is ready to run is on a single queue. MLFQ uses priorities to decide which job should run at a given time: a job with higher priority (i.e., a job on a higher queue) is chosen to run.

Of course, more than one job may be on a given queue, and thus have thesamepriority. In this case, we will just use round-robin scheduling among those jobs.

Thus, the key to MLFQ scheduling lies in how the scheduler sets priorities. Rather than giving a fixed priority to each job, MLFQvaries the priority of a job based on itsobserved behavior. If, for example, a job repeatedly relinquishes the CPU while waiting for input from the keyboard,

MLFQ will keep its priority high, as this is how an interactive process might behave. If, instead, a job uses the CPU intensively for long periods of time, MLFQ will reduce its priority. In this way, MLFQ will try tolearn about processes as they run, and thus use thehistoryof the job to predict itsfuturebehavior.

Thus, we arrive at the first two basic rules for MLFQ:

• Rule 1:If Priority(A)>Priority(B), A runs (B doesn't).
• Rule 2:If Priority(A)=Priority(B), A & B run in RR. If we were to put forth a picture of what the queues might look like at a given instant, we might see something like the following (Figure 8.1).

In the figure, two jobs (A and B) are at the highest priority level, while job

C is in the middle and Job D is at the lowest priority. Given our current knowledge of how MLFQ works, the scheduler would just alternate time slices between A and B because they are the highest priority jobs in the system; poor jobs C and D would never even get to run -- an outrage!

Of course, just showing a static snapshot of some queues does not really give you an idea of how MLFQ works. What we need is to under-

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Attempt 1 How To Change Priority

8.2 Attempt #1: How To Change Priority

[High Priority] Q8 A B

Q7

Q6

Q5

Q4 C

Q3

Q2

[Low Priority] Q1 D

Figure 8.1:MLFQ Example stand how job prioritychangesover time. And that, in a surprise only to those who are reading a chapter from this book for the first time, is exactly what we will do next.

We now must decide how MLFQ is going to change the priority level of a job (and thus which queue it is on) over the lifetime of a job. To do this, we must keep in mind our workload: a mix of interactive jobs that are short-running (and may frequently relinquish the CPU), and some longer-running "CPU-bound" jobs that need a lot of CPU time but where response time isn't important. Here is our first attempt at a priorityadjustment algorithm:

• Rule 3: When a job enters the system, it is placed at the highest priority (the topmost queue).
• Rule 4a:If a job uses up an entire time slice while running, its pri- ority isreduced(i.e., it moves down one queue).
• Rule 4b:If a job gives up the CPU before the time slice is up, it stays at thesamepriority level.

Example 1: A Single Long-Running Job

Let's look at some examples. First, we'll look at what happens when there has been a long running job in the system. Figure 8.2 shows what happens to this job over time in a three-queue scheduler.

Q2

Q1

Q0 0 50 100 150 200

Figure 8.2:Long-running Job Over Time

As you can see in the example, the job enters at the highest priority (Q2). After a single time-slice of 10 ms, the scheduler reduces the job's priority by one, and thus the job is on Q1. After running at Q1 for a time slice, the job is finally lowered to the lowest priority in the system (Q0), where it remains. Pretty simple, no?

Example 2: Along Came A Short Job

Now let's look at a more complicated example, and hopefully see how

MLFQ tries to approximate SJF. In this example, there are two jobs: A, which is a long-running CPU-intensive job, and B, which is a short-running interactive job. Assume A has been running for some time, and then B arrives. What will happen? Will MLFQ approximate SJF for B?

Figure 8.3 plots the results of this scenario. A (shown in black) is running along in the lowest-priority queue (as would any long-running CPUintensive jobs); B (shown in gray) arrives at timeT = 100, and thus is

Q2

Q1

Q0 0 50 100 150 200

Figure 8.3:Along Came An Interactive Job

Q2

Q1

Q0 0 50 100 150 200

Figure 8.4:A Mixed I/O-intensive and CPU-intensive Workload inserted into the highest queue; as its run-time is short (only 20 ms), B completes before reaching the bottom queue, in two time slices; then A resumes running (at low priority).

From this example, you can hopefully understand one of the major goals of the algorithm: because it doesn'tknowwhether a job will be a short job or a long-running job, it firstassumesit might be a short job, thus giving the job high priority. If it actually is a short job, it will run quickly and complete; if it is not a short job, it will slowly move down the queues, and thus soon prove itself to be a long-running more batch-like process.

In this manner, MLFQ approximates SJF.

Example 3: What About I/O?

Let's now look at an example with some I/O. As Rule 4b states above, if a process gives up the processor before using up its time slice, we keep it at the same priority level. The intent of this rule is simple: if an interactive job, for example, is doing a lot of I/O (say by waiting for user input from the keyboard or mouse), it will relinquish the CPU before its time slice is complete; in such case, we don't wish to penalize the job and thus simply keep it at the same level.

Figure 8.4 shows an example of how this works, with an interactive job

B (shown in gray) that needs the CPU only for 1 ms before performing an

I/O competing for the CPU with a long-running batch job A (shown in black). The MLFQ approach keeps B at the highest priority because B keeps releasing the CPU; if B is an interactive job, MLFQ further achieves its goal of running interactive jobs quickly.

Problems With Our Current MLFQ

We thus have a basic MLFQ. It seems to do a fairly good job, sharing the

CPU fairly between long-running jobs, and letting short or I/O-intensive interactive jobs run quickly. Unfortunately, the approach we have developed thus far contains serious flaws. Can you think of any?

(This is where you pause and think as deviously as you can)

Q1 Q1

Boost Boost Boost Boost

Q0 Q0 0 50 100 150 200 0 50 100 150 200

Figure 8.5:Without (Left) and With (Right) Priority Boost

First, there is the problem of starvation: if there are "too many" interactive jobs in the system, they will combine to consumeall CPU time, and thus long-running jobs willneverreceive any CPU time (theystarve).

We'd like to make some progress on these jobs even in this scenario.

Second, a smart user could rewrite their program togame the scheduler. Gaming the scheduler generally refers to the idea of doing something sneaky to trick the scheduler into giving you more than your fair share of the resource. The algorithm we have described is susceptible to the following attack: before the time slice is over, issue an I/O operation (to some file you don't care about) and thus relinquish the CPU; doing so allows you to remain in the same queue, and thus gain a higher percentage of CPU time. When done right (e.g., by running for 99% of a time slice before relinquishing the CPU), a job could nearly monopolize the CPU.

Finally, a program maychange its behaviorover time; what was CPUbound may transition to a phase of interactivity. With our current approach, such a job would be out of luck and not be treated like the other interactive jobs in the system.

8.3 Attempt #2: The Priority Boost

Let's try to change the rules and see if we can avoid the problem of starvation. What could we do in order to guarantee that CPU-bound jobs will make some progress (even if it is not much?).

The simple idea here is to periodicallyboostthe priority of all the jobs in system. There are many ways to achieve this, but let's just do something simple: throw them all in the topmost queue; hence, a new rule:

• Rule 5: After some time periodS, move all the jobs in the system to the topmost queue. Our new rule solves two problems at once. First, processes are guaranteed not to starve: by sitting in the top queue, a job will share the CPU

Q1 Q1

Q0 Q0 0 50 100 150 200 0 50 100 150 200

Figure 8.6:Without (Left) and With (Right) Gaming Tolerance with other high-priority jobs in a round-robin fashion, and thus eventually receive service. Second, if a CPU-bound job has become interactive, the scheduler treats it properly once it has received the priority boost.

Let's see an example. In this scenario, we just show the behavior of a long-running job when competing for the CPU with two short-running interactive jobs. Two graphs are shown in Figure 8.5 (page 6). On the left, there is no priority boost, and thus the long-running job gets starved once the two short jobs arrive; on the right, there is a priority boost every 50 ms (which is likely too small of a value, but used here for the example), and thus we at least guarantee that the long-running job will make some progress, getting boosted to the highest priority every 50 ms and thus getting to run periodically.

Of course, the addition of the time periodSleads to the obvious question: what shouldSbe set to? John Ousterhout, a well-regarded systems researcher [O11], used to call such values in systemsvoo-doo constants, because they seemed to require some form of black magic to set them correctly. Unfortunately,Shas that flavor. If it is set too high, long-running jobs could starve; too low, and interactive jobs may not get a proper share of the CPU.

8.4 Attempt #3: Better Accounting

We now have one more problem to solve: how to prevent gaming of our scheduler? The real culprit here, as you might have guessed, are

Rules 4a and 4b, which let a job retain its priority by relinquishing the

CPU before the time slice expires. So what should we do?

The solution here is to perform betteraccountingof CPU time at each level of the MLFQ. Instead of forgetting how much of a time slice a process used at a given level, the scheduler should keep track; once a process has used its allotment, it is demoted to the next priority queue. Whether

Q2

Q1

Q0 0 50 100 150 200

Figure 8.7:Lower Priority, Longer Quanta it uses the time slice in one long burst or many small ones does not matter.

We thus rewrite Rules 4a and 4b to the following single rule:

• Rule 4: Once a job uses up its time allotment at a given level (re- gardless of how many times it has given up the CPU), its priority is reduced (i.e., it moves down one queue). Let's look at an example. Figure 8.6 (page 7) shows what happens when a workload tries to game the scheduler with the old Rules 4a and 4b (on the left) as well the new anti-gaming Rule 4. Without any protection from gaming, a process can issue an I/O just before a time slice ends and thus dominate CPU time. With such protections in place, regardless of the I/O behavior of the process, it slowly moves down the queues, and thus cannot gain an unfair share of the CPU.

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Tuning MLFQ And Other Issues

8.5 Tuning MLFQ And Other Issues

A few other issues arise with MLFQ scheduling. One big question is how toparameterizesuch a scheduler. For example, how many queues should there be? How big should the time slice be per queue? How often should priority be boosted in order to avoid starvation and account for changes in behavior? There are no easy answers to these questions, and thus only some experience with workloads and subsequent tuning of the scheduler will lead to a satisfactory balance.

For example, most MLFQ variants allow for varying time-slice length across different queues. The high-priority queues are usually given short time slices; they are comprised of interactive jobs, after all, and thus quickly alternating between them makes sense (e.g., 10 or fewer milliseconds). The low-priority queues, in contrast, contain long-running jobs that are CPU-bound; hence, longer time slices work well (e.g., 100s of ms). Figure 8.7 shows an example in which two long-running jobs run for 10 ms at the highest queue, 20 in the middle, and 40 at the lowest.

TIP: AVOIDVOO-DOOCONSTANTS(OUSTERHOUT'SLAW)

Avoiding voo-doo constants is a good idea whenever possible. Unfortunately, as in the example above, it is often difficult. One could try to make the system learn a good value, but that too is not straightforward.

The frequent result: a configuration file filled with default parameter values that a seasoned administrator can tweak when something isn't quite working correctly. As you can imagine, these are often left unmodified, and thus we are left to hope that the defaults work well in the field. This tip brought to you by our old OS professor, John Ousterhout, and hence we call itOusterhout's Law.

The Solaris MLFQ implementation -- the Time-Sharing scheduling class, or TS -- is particularly easy to configure; it provides a set of tables that determine exactly how the priority of a process is altered throughout its lifetime, how long each time slice is, and how often to boost the priority of a job [AD00]; an administrator can muck with this table in order to make the scheduler behave in different ways. Default values for the table are 60 queues, with slowly increasing time-slice lengths from 20 milliseconds (highest priority) to a few hundred milliseconds (lowest), and priorities boosted around every 1 second or so.

Other MLFQ schedulers don't use a table or the exact rules described in this chapter; rather they adjust priorities using mathematical formulae. For example, the FreeBSD scheduler (version 4.3) uses a formula to calculate the current priority level of a job, basing it on how much CPU the process has used [LM+89]; in addition, usage is decayed over time, providing the desired priority boost in a different manner than described herein. See Epema's paper for an excellent overview of suchdecay-usage algorithms and their properties [E95].

Finally, many schedulers have a few other features that you might encounter. For example, some schedulers reserve the highest priority levels for operating system work; thus typical user jobs can never obtain the highest levels of priority in the system. Some systems also allow some useradviceto help set priorities; for example, by using the command-line utilityniceyou can increase or decrease the priority of a job (somewhat) and thus increase or decrease its chances of running at any given time.

See the man page for more.