1 server scheduling in the l p norm nikhil bansal (cmu) kirk pruhs (univ. of pittsburgh)
TRANSCRIPT
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Server Scheduling in the Lp norm
Nikhil Bansal (CMU)Kirk Pruhs (Univ. of Pittsburgh)
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Scheduling
Provide service such that users are satisfied
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Motivation
Single MachineArbitrary arrival or release time ( rj)
Arbitrary processing requirement or size ( pj)
t=0 (r1) r2
r3
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Motivation
t=0 (r1) r2
r3
c2 c3c1
Job 1 preempted
Single MachineArbitrary arrival or release time ( rj)
Arbitrary processing requirement or size ( pj)
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Flow Time
t=0 (r1) r2
r3
c2 c3c1
Completion time: cj
Flow time: fj = cj-rj (time user waits)
Flow Time of Job 1
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Flow Time
t=0 (r1) r2
r3
c2 c3c1
Completion time: cj
Flow time: fj = cj-rj (time user waits)
Flow Time of Job 3
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Stretch
Stretch (i) = Flow time (i) / Size (i) [Bender Chakrabarti Muthukrishnan’98]
Jobs willing to tolerate flow time proportional to size
Also known as normalized flow timeEach job contributes equally
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Known Results
Minimize total flow time (i fi) [L1 norm]
Optimal Online algorithm:Work on job with smallest remaining proc. time
(SRPT)
SRPT is 2 – competitive for total stretch [Gehrke,
Muthukrishnan, Rajaraman, Shaheen’99]Concern: Big jobs could be stuck! Starvation!
Another end: Minimize maximum flow time [L1 norm]
First Come First Served (FCFS) is optimal But bad average performance [Smalls stuck behind
big]
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Balancing average and maximum Lp norms: Penalize outliers, good average
performance
Online algorithms for minimizing1) Flow time (i.e. (j fj
p)1/p)
2) Stretch (i.e. (j sjp)1/p)
Lp norms, previously studied: Load balancing [Awerbuch Azar Grove+ ’95, Alon Azar
Woeginger Yadid ’97, Avidor Azar Sgall ’01] Completion time scheduling [Epstein,Sgall’99]
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Lower Bound
L3 jobs of size 1
By time L3 ,If do not finish size L job, bad!
Size L
Online: Cost of big (L3)2 = L6
Opt: L3 Smalls delayed by L= O(L5)
No no(1) competitive randomized alg for Lp norms offlow time and stretch, for 1<p<1
SRPT, FCFS optimal algorithms for p=1, 1
Suppose p=2
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Lower Bound
No no(1) competitive randomized alg for Lp norms offlow time and stretch, for 1<p<1
SRPT, FCFS optimal algorithms for p=1, 1
L3 jobs of size 1
By time L3 ,If do not finish size L job, bad!
L5 jobs of size 1/L2
Optional Stream of jobsSize L
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Results
Thm: SRPT, SJF (Shortest Job First) are 1+ speed O(1/) competitive
Resource Augmentation: [Kalyanasundaram, Pruhs 95]
Online has more resourcess-speed, c-competitive maxI Online(I,s) / Opt(I,1) · c
No starvation unless close to peak capacity
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Interpreting Resource Augmentation
Load
Perf
orm
ance
Optimal
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Interpreting Resource Augmentation
Load
Perf
orm
ance
Optimal
Online
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Proof Sketch
Lp norm of flow time as ‘weighted’ Flow Time problem Age(j,t) = 0 if t<rj or t>cj
t-rj if rj < t < cj
Observation: fj2/2 = t age(j,t)
j fj2 /2= t j: alive at time t age(j,t)
Proof idea: Show, at all times total age of alive jobs < O(1/ )¢ total age under Opt
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Proof Idea
If job J of size x delayed for t units under SJFThen either,
1) Had lot of work of size < x before arrival of J2) Lot of work arriving continuously since J arrived.
In either case, Opt can be shown to have sufficiently many “old’’ unfinished jobs,
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Non-Clairvoyant Scheduling
Non-Clairvoyant Model [Motwani Phillips Torng ’94]
Scheduler does not know size of a jobLearns size only when job finishes.
Cannot do things like Shortest Job First, SRPT
What can we do?FCFS, Round Robin (time-sharing), …
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Measuring Performance
Resource Augmentation: s-speed, c-competitive
Online non-clairvoyant (s,I) Opt offline clairvoyant (1,I) · cMax I
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The Algorithm (MLF)
Multi-Level Feedback (MLF): Used in Windows NT, Unix
Levels L0,L1,L2,… job enters L0 first
In Li, receive 2i amount of work,
then promoted to Li+1
Work on level i, iff no job in level 0 .. i-1
L0
L1
L2
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Previous Results (Non-Clairvoyant)L1 norm of Flow Time:
MLF: 1+ speed, O(1/) competitive [Kalyanasundaram, Pruhs
’95]
L1 norm of Stretch:
MLF: 1+ speed, O(1/4 log2 B) competitive [B.,
Dhamdhere, Konemann, Sinha’ 03]Any algorithm is 1+ speed (log B/) competitive
B = ratio of maximum to minimum job size
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Our Results
MLF is 1+ speed , O(1/4) competitivefor all norms of flow time
MLF is 1+ speed, O(1/4 log2 B) competitivefor all norms of stretch
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Analysis
Generic technique: reduce MLF to SJF (Shortest Job First) Reduces a non-clairvoyant problem into a clairvoyant one.
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Final Result
Round Robin: At any time, share processor equally
Considered to be fair (each job treated equally) But not good according to the Lp norm criteria
Round Robin is not 1+ speed no(1) competitive(for sufficiently small )
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Open Problems
1) Offline case totally open NP-Hard? Non-trivial approximation algorithms?
2) Multiple machines
3) Other notions to deal with tradeoff between average vs. max performance
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Thank You!
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An example
Goal: Minimize fi2 (i.e. p=2)
a job of size 1 arrives for n time units
If n small ( < L2), work on the small jobsIf n big ( > L2), at time L2, shift to finish the big job
………
Good algorithm: A combination of SRPT + FCFS
Size L
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MLF
1
3
7
15
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MLF
1
3
7
15
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MLF
1
3
7
15
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MLF
1
3
7
15
1
3
7
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MLF
1
3
7
15
1
3
7
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MLF
1
3
7
15
1
3
7
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MLF
1
3
7
15
1
3
7
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MLF
1
3
7
15
1
3
7
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Connection between MLF and SJF
1
3
7
15
1 2 4 8
Instance J : 2i-1Instance J’ : 1,2,4,…,2i-1
J J’
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MLF and SJF
1
3
7
15
1 2 4 8
Instance J : 2i-1Instance J’ : 1,2,4,…,2i-1
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MLF and SJF
1
3
7
15
1 2 4 8
Instance J : 2i-1Instance J’ : 1,2,4,…,2i-1
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MLF and SJF
1
3
7
15
1 2 4 8
1
3
7
1 2 4
Instance J : 2i-1Instance J’ : 1,2,4,…,2i-1
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MLF and SJF
1
3
7
15
1 2 4 8
1
3
7
1 2 4
MLF works on a job in level i SJF works on 2i size copy of same job
MLF works on smallest levelSJF works on smallest job
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MLF and SJF
1
3
7
15
1 2 4 8
1
3
7
1 2 4
MLF works on a job in level i SJF works on 2i size copy of same job
MLF works on smallest levelSJF works on smallest job
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Analysis Idea
2 Main ideas:1) MLF(J) can be viewed as SJF (J’)
2) We know that SJF(J’) ¼ Opt(J’), so MLF(J) ¼ Opt(J’)
Opt(J)
previous clairvoyant result
Fairly general technique, usually allows us to reduce a non-clairvoyant problem into a clairvoyant one.