cpu-sched

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CPU Scheduling

CS 519: Operating System TheoryComputer Science, Rutgers University

Instructor: Thu D. Nguyen

TA: Xiaoyan Li

Spring 2002

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2Computer Science, Rutgers CS 519: Operating System Theory

What and Why?

What is processor scheduling?

Why?

At first to share an expensive resource multiprogramming

Now to perform concurrent tasks because processor is sopowerful

Future looks like past + now

Rent-a-computer approach large data/processing centers usemultiprogramming to maximize resource utilization

Systems still powerful enough for each user to run multipleconcurrent tasks

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Assumptions

Pool of jobs contending for the CPU

CPU is a scarce resource

Jobs are independent and compete for resources (this

assumption is not always used)Scheduler mediates between jobs to optimize someperformance criteria

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Types of Scheduling

Were mostly

concerned with

short-term

scheduling

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What Do We Optimize?

System-oriented metrics:

Processor utilization: percentage of time the processor is busy

Throughput: number of processes completed per unit of time

User-oriented metrics:Turnaround time: interval of time between submission andtermination (including any waiting time). Appropriate for batch

jobs

Response time: for interactive jobs, time from the submissionof a request until the response begins to be received

Deadlines: when process completion deadlines are specified,the percentage of deadlines met must be promoted

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Design Space

Two dimensions

Selection function

Which of the ready jobs should be run next?

PreemptionPreemptive: currently running job may be interrupted and moved toReady state

Non-preemptive: once a process is in Running state, it continues toexecute until it terminates or it blocks for I/O or system service

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Job Behavior

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Job Behavior

I/O-bound jobs

Jobs that perform lots of I/O

Tend to have short CPU bursts

CPU-bound jobs

Jobs that perform very littleI/O

Tend to have very long CPUbursts

Distribution tends to be hyper-exponential

Very large number of veryshort CPU bursts

A small number of very longCPU bursts

CPU

Disk

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Histogram of CPU-burst Times

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Example Job Set

Process

Arrival

Time

Service

Time

1

2

3

4

5

0

2

4

6

8

3

6

4

5

2

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Behavior of Scheduling Policies

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Behavior of Scheduling Policies

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Multilevel Queue

Ready queue is partitioned into separate queues:

foreground (interactive)

background (batch)

Each queue has its own scheduling algorithm:

foreground RRbackground FCFS

Scheduling must be done between the queues.

Fixed priority scheduling; i.e., serve all from foreground then frombackground. Possibility of starvation.

Time slice each queue gets a certain amount of CPU time which it canschedule amongst its processes; i.e.,80% to foreground in RR

20% to background in FCFS

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Multilevel Queue Scheduling

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Multilevel Feedback Queue

A process can move between the various queues; agingcan be implemented this way.

Multilevel-feedback-queue scheduler defined by the

following parameters:number of queues

scheduling algorithms for each queue

method used to determine when to upgrade a process

method used to determine when to demote a processmethod used to determine which queue a process will enterwhen that process needs service

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Multilevel Feedback Queues

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Example of Multilevel Feedback Queue

Three queues:

Q0 time quantum 8 milliseconds

Q1 time quantum 16 milliseconds

Q2 FCFSScheduling

A new job enters queue Q0which is servedFCFS. When itgains CPU, job receives 8 milliseconds. If it does not finish in

8 milliseconds, job is moved to queue Q1.At Q1 job is again served FCFS and receives 16 additionalmilliseconds. If it still does not complete, it is preempted andmoved to queue Q2.

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Traditional UNIX Scheduling

Multilevel feedback queues

128 priorities possible (0-127)

1 Round Robin queue per priority

Every scheduling event the scheduler picks the lowestpriority non-empty queue and runs jobs in round-robin

Scheduling events:

Clock interruptProcess does a system call

Process gives up CPU,e.g. to do I/O

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Traditional UNIX Scheduling

All processes assigned a baseline priority based on thetype and current execution status:

swapper 0

waiting for disk 20

waiting for lock 35

user-mode execution 50

At scheduling events, all processs priorities areadjusted based on the amount of CPU used, the current

load, and how long the process has been waiting.Most processes are not running, so lots of computingshortcuts are used when computing new priorities.

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UNIX Priority Calculation

Every 4 clock ticks a processes priority is updated:

The utilization is incremented every clock tick by 1.

The niceFactor allows some control of job priority. Itcan be set from 20 to 20.

Jobs using a lot of CPU increase the priority value.Interactive jobs not using much CPU will return to thebaseline.

NiceFactornutilizatio

BASELINEP 24

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UNIX Priority Calculation

Very long running CPU bound jobs will get stuck at the highestpriority.

Decay function used to weight utilization to recent CPU usage.

A processs utilization at timetis decayed every second:

The system-wide load is the average number of runnable jobsduring last 1 second

niceFactoruload

loadu tt

)1(

)12(

2

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UNIX Priority Decay

1 job on CPU. load will thus be 1. Assume niceFactor is 0.

Compute utilization at time N:

+1 second:

+2 seconds

+N seconds

01

3

2UU

002

2

11

3

2

3

2

3

2

3

2UUUUU

...3

2

3

22

2

1

nn UUU n

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Scheduling Algorithms

FIFO is simple but leads to poor average response times. Shortprocesses are delayed by long processes that arrive before them

RR eliminate this problem, but favors CPU-bound jobs, which havelonger CPU bursts than I/O-bound jobs

SJN, SRT, and HRRN alleviate the problem with FIFO, but requireinformation on the length of each process. This information is notalways available (although it can sometimes be approximated basedon past history or user input)

Feedback is a way of alleviating the problem with FIFO without

information on process length

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Its a Changing World

Assumption about bi-modal workload no longer holds

Interactive continuous media applications are sometimesprocessor-bound but require good response times

New computing model requires more flexibilityHow to match priorities of cooperative jobs, such asclient/server jobs?

How to balance execution between multiple threads of a singleprocess?

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Lottery Scheduling

Randomized resource allocation mechanism

Resource rights are represented by lottery tickets

Have rounds of lottery

In each round, the winning ticket (and therefore thewinner) is chosen at random

The chances of you winning directly depends on thenumber of tickets that you have

P[wining] = t/T, t = your number of tickets, T = total number oftickets

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Lottery Scheduling

After n rounds, your expected number of wins is

E[win] = nP[wining]

The expected number of lotteries that a client must wait beforeits first win

E[wait] = 1/P[wining]

Lottery scheduling implementsproportional-shareresourcemanagement

Ticket currencies allow isolation between users, processes, and

threadsOK, so how do we actually schedule the processor using lotteryscheduling?

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Implementation

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Performance

Allocated and observedexecution ratios between

two tasks running theDhrystone benchmark.With exception of 10:1

allocation ratio, all observedratios are close to allocations

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Short-term Allocation Ratio

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Isolation

Five tasks running the Dhrystonebenchmark. Let amount.currency

denote a ticket allocation of amountdenominated in currency. Tasks

A1 and A2 have allocations 100.A and

200.A, respectively. Tasks B1 and B2have allocations 100.B and 200.B,respectively. Halfway thru experiment

B3 is started with allocation 300.B.This inflates the number of tickets in Bfrom 300 to 600. Theres no effect on

tasks in currency A or on the aggregateiteration ratio of A tasks to B tasks.Tasks B1 and B2 slow to half theiroriginal rates, corresponding to thefactor of 2 inflation caused by B3.

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Borrowed-Virtual-Time (BVT) Scheduling

Current scheduling in general purpose systems does notsupport rapid dispatch of latency-sensitive applications

Examples include continuous media applications such asteleconferencing, playing movies, voice-over-IP, etc.

Whats the problem with the traditional Unix scheduler?

Beauty of BVT is its simplicity

Corollary: not that much to say

Tricky part is figuring out the appropriate parameters of the paper is on this (which Im going to skip)

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BVT Scheduling: Basic Idea

Scheduling is done based on virtual time

Each thread has

EVT (effective virtual time)

AVT (actual virtual time)

W (warp factor)

warpBack (whether warp is on or not)

EVT of thread is computed as

Threads accumulate virtual time as they run

Thread with earliest EVT is scheduled next

)0:?( WwarpBackAE

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BVT Scheduling: Details

Can only switch every Ctime units to prevent thrashing

Threads can accumulate virtual time at different rates

Allow for weighted fair sharing of CPU

To make sure that latency-sensitive threads arescheduled right away, give these threads high warpvalues

Have limits on how much and how long can warp to

prevent abuse

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BVT Scheduling: Performance

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BVT vs. Lottery

How do the two compare?

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Parallel Processor Scheduling

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Simulating Ocean Currents

Model as two-dimensional gridsDiscretize in space and time

finer spatial and temporalresolution => greater accuracy

Many different computations pertime step

set up and solve equations

Concurrency across and withingrid computations(a) Cross sections (b) Spatial discretizationof a cross section

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m1m

2

r2

Star on which f orcesare being computed

Star too close toapproximate

Small group far enough away toapproximate to center of mass

Large group farenough away toapproximate

Case Study 2: Simulating Galaxy Evolution

Simulate interactions of many stars evolving over time

Computing forces is expensive

O(n2) brute force approach

Hierarchical Methods take advantage of force law: G

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Case Study 2: Barnes-Hut

Many time steps, plenty of concurrency across stars within eachLocality Goal

Particles close together in space should be on same processor

Difficulties: Nonuniform, dynamically changing

Spatial Domain Quad-tree

C St d 3 R d i S b R

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Case Study 3: Rendering Scenes by RayTracing

Shoot rays into scene throughpixels in projection plane

Result is color for pixel

Rays shot through pixels inprojection plane are calledprimaryrays

Reflect and refract when they hitobjects

Recursive process generates raytree per primary ray

Tradeoffs between execution timeand image quality

Viewpoint

Projection Plane

3D Scene

Ray from

viewpoint to

upper right cornerpixel

Dynamically

generated ray

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Partitioning

Need dynamic assignment

Use contiguous blocks to exploit spatial coherence amongneighboring rays, plus tiles for task stealing

A block,the unit ofassignment

A tile,the unit of decompositionand stealing

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Sample Speedups

h d l ( )

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Coscheduling (Gang)

Cooperating processes may interact frequentlyWhat problem does this lead to?

Fine-grained parallel applications haveprocess workingset

Two things neededIdentify process working set

Coschedule them

Assumption: explicitly identified process working setSome good recent work has shown that it may bepossible to dynamically identify process working set

h d l

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Coscheduling

What is coscheduling?

Coordinating across nodes to make sure that processesbelonging to the same process working set arescheduled simultaneously

How might we do this?

Impact of OS Scheduling Policies and

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Impact of OS Scheduling Policies andSynchronization on Performance

Consider performance for a set of applications for

Feedback priority scheduling

Spinning

Blocking

Spin-and-block

Block-and-hand-off

Block-and-affinity

Gang scheduling (time-sharing coscheduling)

Process control (space-sharing coscheduling)

A li i

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Applications

N l S h d li i h S i i

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Normal Scheduling with Spinning

N l S h d li ith Bl ki L k

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Normal Scheduling with Blocking Locks

G S h d li

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Gang Scheduling

P C t l (S Sh i )

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Process Control (Space-Sharing)

M lti S h d li

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Multiprocessor Scheduling

Load sharing: poor locality; poor synchronizationbehavior; simple; good processor utilization. Affinityor per processor queues can improve locality.

Gang scheduling: central control; fragmentation --unnecessary processor idle times (e.g., twoapplications with P/2+1 threads); goodsynchronization behavior; if careful, good locality

Hardware partitions: poor utilization for I/O-intensiveapplications; fragmentation unnecessary processoridle times when partitions left are small; good localityand synchronization behavior

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