Online Job Scheduling
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Online Job Scheduling. Marek Chrobak University of California, Riverside. Outline: Online optimization problems Competitive analysis Example: caching 2. List scheduling for minimum makesnan Graham’s algorithm is 2-competitive 3. List scheduling for related machines

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Online Job Scheduling

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Online job scheduling

Online Job Scheduling

Marek Chrobak

University of California, Riverside

1


Online job scheduling

  • Outline:

  • Online optimization problems

    • Competitive analysis

    • Example: caching

      2. List scheduling for minimum makesnan

    • Graham’s algorithm is 2-competitive

      3. List scheduling for related machines

      4. Minimizing total completion time

    • Optimal algorithm for the preemptive case

    • 2-competitive algorithm for the non-preemptive case

      5. Scheduling of unit jobs for maximum throughput

    • 2-competitive greedy algorithm

    • 1.618-competitive algorithm for the 2-bounded case

    • Randomized 1.58-competitive algorithm

      6. Scheduling equal-length jobs for maximum throughput

2


Online job scheduling

RAM

disk

k

CPU

  • Paging:

  • 2-Level memory system with large disk and k pages in RAM

  • Sequence of requests to pages

  • If the requested page is not in RAM (miss), it must be fetched from disk (another page may need to be evicted)

  • Objective: minimize the number of misses

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Online job scheduling

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  • Paging example:

  • k= 5

  • Initial RAM state: 4 3 5 6 1

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Requests:

  • Questions:

  • Can we reduce the number of misses?

Yes

No

  • Can we reduce it to zero?

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Online job scheduling

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  • Main question: What page to evict on a miss?

  • Common replacement strategies:

  • LRU = least recently used

  • FIFO = first-in first-out

LRU Example:

Requests:

…4 5 3 6

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Online job scheduling

online

offline

usually b=0

Performance measure?

Algorithm A is R-competitive if for any sequence S of page

requests

(# of misses of A on S) ≤ R·(minimum # of misses on S) + b

where C is a constant independent of S

Competitive ratio of A = smallest such R

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Online job scheduling

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How to determine optimum?

Belady Algorithm: at each step evict the page whose next request is farthest in the future.

…4 5 3 6

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Online job scheduling

LRU:

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Optimum:

How bad is LRU?

Try k=2

each request

is a miss

every second

request is a miss

So competitive ratio of LRU is ≥ 2

8


Online job scheduling

misses of LRU

Requests: … x y … r z r… p r p … s p s … r’ … p’ … s’

optimum must

have r in cache

phase = request

until the 2nd miss

In this phase: at least 2 different requests other than r, so optimum must miss at least once

How good is LRU?

Try k=2 again

We have p  r and s  p

Case 1: s  r. Then r,p,s are different

Case 2: s = r. So on p we evicted r. There must

be another request z  r,p between r, p

for otherwise LRU would not evict r

So competitive ratio of LRU is ≤ 2

Exercise: Show that the competitive ratio of LRU for k pages is ≤ k.

Hint: Similar idea. If all faults in a phase different, you are done. Else, consider two equal faults…

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Online job scheduling

k requests

Q

B = Belady’s

pages in

memory

So at most one fault here

Is it possible to improve ratio k?

Theorem: No online paging algorithm has competitive

ratio smaller than k.

Proof: A = some online algorithm.

Adversary strategy: fix a set of pages 1,2,…,k+1. At each step request the page that A does not have in memory.

  • Then:

  • Algorithm A faults at each time step

  • Belady faults at most once in each k consecutive steps (why?)

Then B and Q differ by at most 1

Therefore the competitive ratio of A is at least k

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