Advanced Operating Systems - Spring 2009 Lecture 14 – February 25, 2009 Dan C. Marinescu Email:...
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Advanced Operating Systems - Spring 2009 Lecture 14 – February 25, 2009 Dan C. Marinescu Email: [email protected] Office: HEC 439 B. Office hours: M, Wd 3 – 4:30 PM. TA: Chen Yu Email: yuchen@cs.ucf.edu Office: HEC 354. Office hours: M, Wd 1.00 – 3:00 PM. 1
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Transcript of Advanced Operating Systems - Spring 2009 Lecture 14 – February 25, 2009 Dan C. Marinescu Email:...
Advanced Operating Systems - Spring 2009Lecture 14 – February 25, 2009Dan C. Marinescu
Email: [email protected]: HEC 439 B. Office hours: M, Wd 3 – 4:30 PM.
TA: Chen YuEmail: [email protected]: HEC 354. Office hours: M, Wd 1.00 – 3:00 PM.
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Last, Current, Next Lecture Last time:
CPU Scheduling
Today M/M/m systems Scheduling Algorithms Memory management
Next time: Caching and Virtual Memory
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Scheduling algorithms
A scheduling problems is defined by : The machine environment A set of side constrains and characteristics The optimality criterion
Machine environments: 1 One-machine. P Parallel identical machines Q Parallel machines of different speeds R Parallel unrelated machines O Open shop. m specialized machines; a job requires a
number of operations each demanding processing by a specific machine
F Floor shop
)()(
)(
),,(
One-machine environment
n jobs 1,2,….n. pj amount of time required by job j.
rj the release time of job j, the time when job j is available for processing.
wj the weight of job j.
dj due time of job j; time job j should be completed.
A schedule S specifies for each job j which pj units of time are used to process the job.
CSj the completion time of job j under schedule S.
The makespan of S is: CSmax = max CS
j The average completion time is
n
j
SjCn 1
1
One-machine environment (cont’d)
Average weighted completion time:Optimality criteria minimize:
the makespan CSmax
the average completion time :The average weighted completion time:
the lateness of job j maximum lateness of any
job under schedule S. Another optimality criteria, minimize maximum lateness.
n
j
SjjCw
1
n
j
SjC
1
n
j
SjjCw
1
Sj
nj LL 1max max j
Sjj dCL
Priority rules for one machine environmentTheorem: scheduling jobs according to SPT – shortest
processing time is optimal for
Theorem: scheduling jobs in non-decreasing order of is optimal for
jjCw||1 j
j
p
w
jC||1
Earliest deadline first (EDF)Dynamic scheduling algorithm for real-time OS. When a scheduling event occurs (task finishes, new
task released, etc.) the priority queue will be searched for the process closest to its deadline. This process will then be scheduled for execution next.
EDF is an optimal scheduling preemptive algorithm for uniprocessors, in the following sense: if a collection of independent jobs, each characterized by an arrival time, an execution requirement, and a deadline, can be scheduled (by any algorithm) such that all the jobs complete by their deadlines, the EDF will schedule this collection of jobs such that they all complete by their deadlines.
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EDF
8
11
n
j jp
djU
Process Execution Time Period
P1 1 8
P2 2 5
P3 4 10
The schedulability test for EDF is:
In this case U = 1/8 +2/5 + 4/10 = 0.925 = 92.5%
It has been proved that the problem of deciding if it is possible to schedule a set of periodic processes is NP-hard if the periodic processes use semaphores to enforce mutual exclusion.
Priority InversionA high priority process is blocked by a lower
priority one.Example: J1 and J3 share a data structure
guarded by a binary semaphore S.prty(J1) > prty(J2) > prty(J3).J1 in initiated while J3 is in its critical sectionWhen J1 attempts to enter the critical section it is
blocked.The duration of this blocking cannot be determined
as because J3 can be preempted by a higher priority job J2. prty
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