Class 9
CS 202
26 February 2015

On the board
------------

1. Last time
2. Scheduling intro
3. Survey, workload, productivity
4. Scheduling disciplines
5. Scheduling lessons and conclusions

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1. Last time

    Therac-25 discussion

    Example of what can go wrong in concurrent programming (and, more
    generally, with poor programming practice). 

    Another kind of end-to-end test: radiation phantom.

2. Scheduling intro

    High-level problem: operating system has to decide which process (or
    thread) to run.

    A. When scheduling decisions happen:

        [draw process transition diagram]

                                                   exit (iv)
                                                   |-------->[terminated]
              admitted         (ii) interrupt      |
	[new] -->   [ready]   <-------------- [running]
	              ^        --------------->      |
       I/O or event    \       scheduler dispatch    |
     completion (iii)   \                 ___________|
                         \               /
			      [waiting] v  I/O or event wait (i)


	scheduling decisions take place when a process:

	    (i) Switches from running to waiting state 
	    (ii) Switches from running to ready state 
	    (iii) Switches from waiting to ready 
	    (iv) Exits 

	preemptive scheduling: at all four points

	nonpreemptive scheduling: at points (i), (iv), only (this is the
	definition of nonpreemptive scheduling)



    B. what are the metrics and criteria?

	--turnaround time
	    time for each process to complete

        --waiting/response/output time. variations on this definition,
        but they all capture the time spent waiting for something to
        happen rather than the time from launch to exit.

            --time between when job enters system and starts executing
            following your text, we will call this "response time"
            [some texts call this "waiting time"]

            --time from request to first response (e.g., key press to
            character echo)
            we will call this "output time" 
            [some texts call this "response time"]


	--system throughput
	    # of processes that complete per unit time

	--fairness

	    different possible definitions:

		--freedom from starvation

		--all users get equal time on CPU

		--highest priority jobs get most of CPU

		--etc.

	    [often conflicts with efficiency. true in life as well.]

    C. context switching has a cost

        --CPU time in kernel

	    --save and restore registers

	    --switch address spaces 

	--indirect costs

	    --TLB shootdowns, processor cache, OS caches (e.g., buffer
	    caches)

	--result: more frequent context switches will lead to worse
	throughput (higher overhead)


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Admin notes

    --can bring one two-sided sheet of paper into the exam; will post
    formatting

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3. Survey, workload, productivity

    --survey results

        --lots of people feel the workload is killing them

        --grades: don't panic

        --I think there was a correlation between comfort with some of
        the basics of software development (being able to navigate code,
        use gdb, etc.) and enjoyment of the course.

    --workload intended to be heavy (that's life), push you, and
    potentially cause you to recalibrate what it means to be challenged.
    BUT not intended to kill or destroy you.
    
        --> let's try to increase your speed and effectiveness.

    --motivating anti-patterns (we will address others):
    
        --> "there are 5 places in my code where I don't know what the
        right thing is. let me try all combinations to see what compiles
        and passes the test scripts.": this takes exponential time.

        --> "the compiler complains about my code. let me quickly change
        it.": this does not cause you to internalize the right syntax.

        --> "my code doesn't pass the test scripts. let me change it.":
        this does not result in understanding, and often doesn't address
        the core issue.

    --concrete steps: us and you

        --we will cover some productivity and skills in lecture, probably
        next week's.

        --we will assign reading on this

        --we will enhance lab4 to cover some of these basics, and we
        will make lab4 due later, to give you this time.

        --one-on-one sessions: make an appointment with a TA or me for
        one-on-one help. 
        
            we will watch you work for 5-7 minutes, and then offer
            1-on-1 coaching for 5-7 minutes. 

            2 such sessions per student

            Intent: NOT to help you get your code working but help
            you approach the problem of software development.
    
        --advice, in response to common anti-patterns:

            --"I work late at night and slow down a bit": Don't work
            when you're exhausted! (Wake up the morning, stop working,
            whatever.) Working in a fog does more harm than good.

            --"I'm digging with a spoon because I don't have time to make
            a shovel": take the time to make the shovel! (Read the
            guides, internalize the tools, learn the syntax.)

            --"My job is to answer the purple box in Exercise 1." No,
            your job is to acquire the basics and figure out what's
            going on. If you have done that, the exercises do not take
            very long.

            --"I'm going to start lab4 two days before it's due and
            enjoy the time between now and then.": Bad idea. Exploit the
            lab schedule adjustment. 

                --take this time to learn the skills

                --also, if you got very little of lab2 working (for
                example, only the parser), it may be to your advantage
                to work on it and resubmit late.


4. Scheduling disciplines

    Assume first that processes/threads do not do I/O.

        (Completely unrealistic but lets us build intuition, as a
        warmup.)

    A. FCFS/FIFO

	--run each job until it's done

	--P1 needs 24 seconds

	--P2 needs 3 seconds

	--P3 needs 3 seconds
	
	--[ P1         P2  P3 ] 

	--throughput: 3 jobs / 30 seconds = .1 jobs/sec

	--average turnaround time?
	    1/3(24 + 27 + 30) = 27

	--observe: scheduling P2,P3,P1 would drastically reduce average
	turnaround time

	--advantages to FCFS:
	    --simple
	    --no starvation
	    --few context switches

	--disadvantage:
	    --short jobs get stuck behind long ones


    B. SJF and STCF

        --SJF
            Schedule the job whose next CPU burst is the shortest

        --STCF
            preemptive version of SJF: if job arrives that has a shorter
            time to completion than the remaining time on the current job,
            immediately preempt CPU to give to new job

        --idea:
	    --get short jobs out of the system
	    --big (positive) effect on short jobs, small
	    (negative) effect on large jobs

	--example:

	    process     arrival time         burst time
	    P1		0			7
	    P2		2			4
	    P3		4			1
	    P4		5			4

	    preemptive:
	    0  1  2  3  4  5  6  7  8  9  10 11 12 13 14 15
	    P1 P1 P2 P2 P3 P2 P2 P4 P4 P4 P4 P1 P1 P1 P1 P1

        --defer advantages and disadvantages to after we introduce
        relaxation of "no I/O" assumption
    
    let's decide we also care about response time (as defined by our
    book: duration between when a job arrives and when it is first
    scheduled.)

    C. Round-robin (RR)

	--add timer

	--preempt CPU from long-running jobs. per time slice or quantum

	--after time slice, go to the back of the ready queue

	--most OSes do something of this flavor (see MLFQ below)

	--advantages:

	    --fair allocation of CPU across jobs
	    --low average response time when job lengths vary
	    --good for output time if small number of jobs

	--disadvantages:

	    --what if jobs are same length?
	    --example: 2 jobs of 50 time units each, quantum of 1
	    --average turnaround time: 100 (vs. 75 for FCFS)

	--how to choose the quantum size?
	    
	    --want much larger than context switch cost

	    --majority of bursts should be less than quantum

	    --pick too small, and spend too much time context switching

	    --pick too large, and response time suffers (extreme case:
	    system reverts to FCFS)

	    --typical time slice is between 10-100 milliseconds. context
	    switches are usually microseconds or tens of microseconds
	    (maybe hundreds)

    D. Incorporating I/O (relax assumption from before)

        motivating example:

       	    3 jobs
	    A, B: both CPU bound, run for a week
	    C: I/O bound, loop
		1 ms of CPU
		10 ms of disk I/O

	    by itself, C uses 90% of disk
	    By itself, A or B uses 100% of CPU

	    what happens if we use FIFO?
		--if A or B gets in, keeps CPU for 2 weeks

	    what about RR with 100msec time slice?
		--only get 5% disk utilization

	    what about RR with 1msec time slice?
		--get nearly 90% disk utilization
		--but lots of preemptions

        what about STCF? (applied to remaining burst time)
                --would give us good disk utilization ... 

        --advantages to STCF
            --disk utilization (see above)
            --optimal (minimum) waiting/response/output time (can prove this)
            --low overhead (no needless preemptions)

        --disadvantages to STCF
            --long-running jobs can get starved
	    --does not optimize turnaround time (only
	    waiting/response/output time)
            --requires predicting the future

    
	so useful as a yardstick for measuring other policies (good way
	to do CS design and development: figure out what the absolute
	best answer is, then figure out how to approximate it)

	however, can attempt to estimate future based on past (another
	thing that people do when designing systems):

	    --Exponentially weighted average a good idea 

	    --t_n: length of proc's nth CPU burst

	    --\tao_{n+1}: estimate for n+1 burst

	    --choose \alpha, 0 < \alpha <= 1

	    --set \tao_{n+1} = \alpha * t_n + (1-\alpha)*\tao_n

	    --this is called an exponential weighted moving average
	    (EWMA)

	    --reacts to changes, but smoothly

	upshot: favor jobs that have been using CPU the least amount of
	time; that ought to approximate STCF


    E. Priority

	--priority scheme: give every process a number (set by
	administrator), and give the CPU to the process with the highest
	priority (which might be the lowest or highest number, depending
	on the scheme)

	    --can be done preemptively or non-preemptively

	--generally a bad idea to use strict priority because of
	starvation of low priority tasks.
	
	    (--note: SJF is priority scheduling where priority is the
	    predicted next CPU burst time )

	--solution to this starvation is to increase a process's
	priority as it waits

    F. Multi-level feedback queue (MLFQ)

        three ideas:

	    --*multiple queues, each with different priority*. 32
	    queues, for example.
	    
	    --round-robin within each queue (flush a priority level
	    before moving onto the next one). 

	    --feedback: process's priority changes, for example based on
	    how little or much it's used the CPU

        advantages:
	    --approximates SRTCF

	disadvantages:
	    --can't donate priority
	    --not very flexible
	    --not good for real-time and multimedia
	    --can be gameable: user puts in meaningless I/O to keep job's
	    priority high

	see textbook (chap 8) for details

    G. Lottery and stride scheduling

	[citations:
	
	C. A. Waldsburger and W. E. Weihl. Lottery
	Scheduling: Flexible Proportional-Share Resource Management.
	Proc. Usenix Symposium on Operating Systems Design and
	Implementation, November, 1994.
	http://www.usenix.org/publications/library/proceedings/osdi/full_papers/waldspurger.pdf]

	C. A. Waldsburger and W. E. Weihl.  Stride Scheduling:
	Deterministic Proportional-Share Resource Management.
	Technical Memorandum MIT/LCS/TM-528, MIT Laboratory for
	Computer Science, June 1995. 
	http://www.psg.lcs.mit.edu/papers/stride-tm528.ps

	Carl A. Waldspurger. Lottery and Stride Scheduling:
	Flexible Proportional-Share Resource Management, Ph.D.
	dissertation, Massachusetts Institute of Technology,
	September 1995. Also appears as Technical Report
	MIT/LCS/TR-667.
	http://waldspurger.org/carl/papers/phd-mit-tr667.pdf
	]

	Issue lottery tickets to processes 
	-- Let p_i have t_i tickets 
	-- Let T be total # of tickets, T = \sum t_i
	-- Chance of winning next quantum is t_i / T
	-- Note lottery tickets not used up, more like season tickets

	controls long-term average proportion of CPU for each
	process

	can also group processes hierarchically for control
	    --subdivide lottery tickets
	    --can model as currency, so there can be an exchange
	    rate between real currencies (money) and lottery tickets

	--lots of nice features

	    --deals with starvation (have one ticket --> will make
	    progress)

	    --don't have to worry that adding one high priority job will
	    starve all others

	    --adding/deleting jobs affects all jobs proportionally (T
	    gets bigger)

	    --can transfer tickets between processes: highly useful if a
	    client is waiting for a server. then client can donate
	    tickets to server so it can run.

		--note difference between donating tix and donating
		priority. with donating tix, recipient amasses enough
		until it runs. with donating priority, no difference
		between one process donating and 1000 processes donating

	--many other details

	    --ticket inflation for processes that don't use their whole
	    quantum

	    --use fraction f of quantum; inflate tix by 1/f until it
	    next gets CPU

	--disadvantages

	    --latency unpredictable

	    --expected error somewhat high 

		--for those comfortable with probability: this winds up
		being a binomial distribution. variance n*p*(1-p) -->
		standard deviation \proportional \sqrt(n),
		    --where:
			p is fraction of tickets owned
			n is number of quanta

	--in reaction to these disadvantages, Waldspurger and Weihl
	proposed *Stride Scheduling*
 
	    --basically, a deterministic version of lottery scheduling.
	    less randomness --> less expected error.

	    --see textbook (chap 9) for details
	
    H. What Linux does

	--the current Linux scheduler (post 2.6.23), called CFS
	(Completely Fair Scheduler), roughly reinvented the ideas of
	Stride Scheduling


5. Scheduling lessons and conclusions

    --Scheduling comes up all over the place

	--m requests share n resources

	--disk arm: which read/write request to do next?

	--memory: which process to take physical page from?

    --This topic was popular in the days of time sharing, when there was
    a shortage of resources all around, but many scheduling problems
    become not very interesting when you can just buy a faster CPU or a
    faster network.

	--Exception 1: web sites and large-scale networks often cannot
	be made fast enough to handle peak demand (flash crowds,
	attacks) so scheduling becomes important again. For example may
	want to prioritize paying customers, or address
	denial-of-service attacks.

	--Exception 2: some scheduling decisions have non-linear effects
	on overall system behavior, not just different performance for
	different users.

	--Exception 3: real-time systems:
	    soft real time: miss deadline and CD or MPEG decode will skip
	    hard real time: miss deadline and plane will crash

	    Plus, at some level, every system with a human at the other
	    end is a real-time system. If a Web server delays too long,
	    the user gives up. So the ultimate effect of the system may
	    in fact depend on scheduling!
	    
    --In principle, scheduling decisions shouldn't affect program's
    results

	--This is good because it's rare to be able to calculate the
	best schedule

	--So instead, we build the kernel so that it's correct to do a
	context switch and restore at any time, and then *any* schedule
	will get the right answer for the program

	--This is a case of a concept that comes up a fair bit in
	computer systems: the policy/mechanism split. In this case, the
	idea is that the *mechanism* allows the OS to switch any time
	while the *policy* determines when to switch in order to meet
	whatever goals are desired by the scheduling designer

	    [[--In my view, the notion of "policy/mechanism split" is
	    way overused in computer systems, for two reasons:
	    
		--when someone says they separated policy from mechanism
		in some system, usually what's going on is that they
		separated the hard problem from the easy problem and
		solved the easy problem; or

		--it's simply not the case that the two are separate.
		*every* mechanism encodes a range of possible policies,
		and by choice of mechanism you are usually constraining
		what policies are possible. That point is obvious but
		tends to be overlooked when people advertise that
		they've "fully separated policy from mechanism"]]

    --But there are cases when the schedule *can* affect correctness

	--multimedia: delay too long, and the result looks or sounds
	wrong

	--Web server: delay too long, and users give up


    --Three lessons (besides policy/mechanism split):

	(i) Know your goals: write them down!

	(ii) Compare against optimal, even if optimal can't be built. 

	    --It's a useful benchmark. Don't waste your time improving
	    something if it's already at 99% of optimal.

	    --Provides helpful insight. (For example, we know from the
	    fact that STCF is optimal that it's impossible to be optimal
	    and fair, so don't spend time looking for an optimal
	    algorithm that is also fair.)

	(iii) There are actually many different schedulers in the
	system that interact:

	    --mutexes, etc. are implicitly making scheduling decisions

	    --interrupts: likewise (by invoking handlers)

	    --disk: the disk scheduler doesn't know to favor one
	    process's I/O above another

	    --network: same thing: how does the network code know which
	    process's packets to favor? (it doesn't)

	    --example of multiple interacting schedulers:

		you can optimize the CPU's scheduler and still find it
		does nothing (e.g., if you're getting interrupted
		200,000 times per second, only the interrupt handler is
		going to get the CPU, so you need to solve that problem
		before you worry about how the main CPU scheduler
		allocates the CPU to jobs)

	    --Basically, the _existence_ of interrupts is bad for
	    scheduling (also true in life)


[Acknowledgments: Frans Kaashoek, David Mazieres, Mike Dahlin]