a polylog competitive algorithm for the k-server problem
DESCRIPTION
A polylog competitive algorithm for the k-server problem. 3. 1. 2. Move Closest Sever Algorithm. The k-server Problem. n. 1. The Paging/Caching Problem. Set of pages {1,2,…,n} , cache of size kTRANSCRIPT
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A polylog competitive algorithm for the k-server problem
Nikhil Bansal (Eindhoven)
Niv Buchbinder (Open Univ., Israel)
Aleksander Madry (MIT)
Seffi Naor (Technion)
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The k-server Problem
1 2 3
Move Closest Sever Algorithm
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The Paging/Caching Problem
Set of pages {1,2,…,n} , cache of size k<n.Request sequence of pages 1, 6, 4, 1, 4, 7, 6, 1, 3, …
a) If requested page already in cache, no penalty.b) Else, cache miss. Need to fetch page in cache (possibly) evicting some other page.
Goal: Minimize the number of cache misses.
Paging: K-server on the uniform metric.(Server on location p = page p in cache)
1 n. . .
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Previous Results: Paging
Paging (Deterministic) [Sleator Tarjan 85]:
• Any deterministic algorithm >= k-competitive.
• LRU is k-competitive (also other algorithms)
Paging (Randomized):
• Rand. Marking O(log k) [Fiat, Karp, Luby, McGeoch, Sleator, Young 91].
• Lower bound Hk [Fiat et al. 91], tight results known.
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K-server conjecture
[Manasse-McGeoch-Sleator ’88]:
There exists k competitive algorithm on any metric space.
Initially no f(k) guarantee.Fiat-Rababi-Ravid’90: exp(k log k) …Koutsoupias-Papadimitriou’95: 2k-1
Chrobak-Larmore’91: k for trees.
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Randomized k-server Conjecture
There is an O(log k) competitive algorithm for any metric.
Uniform Metric: log k
Polylog for very special cases (uniform-like)
Line: n2/3 [Csaba-Lodha’06]
exp(O(log n)1/2) [Bansal-Buchbinder-Naor’10]
Depth 2-tree: No o(k) guarantee
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Our Result
Thm: There is an O(log2 k log3 n) competitive* algorithm for k-server on any metric with n points.
* Hiding some log log n terms
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Our Approach
Hierarchically Separated Trees (HSTs) [Bartal 96].
Any Metric space
Problems on HST often reduced to Uniform metrics.[Bartal-Blum-Burch-Tomkins 97, Kleinberg-Tardos 01, …]
Allocation Problem (uniform metrics): [Cote-Meyerson-Poplawski’08]
(We work with a weaker “fractional” allocation problem)
O(log n)
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Outline
• Introduction• HSTs + Allocation Problem• Fractional view of Randomized Algorithms• Fractional Allocation Problem
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Designing Algorithm on HST
d+1 level HST
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Allocation Problem
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Allocation to k-server
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Outline
• Introduction• Allocation Problem• Fractional view of Randomized Algorithms• Fractional Allocation Algorithm
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Fractional View of Randomized Algorithms
To specify a randomized algorithm:
i) Prob. distribution on states at time t.
ii) How it changes at time t+1.
Fractional view: Just specify some marginals.
Eg. Paging, actual algorithm = distribution over k-tuples
but, Fractional: p1,…,pn s.t. p1 + …+ pn = k
Cost: If p1,…,pn changes to q1,…,qn , pay i |pi – qi|
Suffices: Fractional Paging -> Randomized Paging (2x loss)
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Fractional Allocation Problem
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A gap example
Allocation Problem on 2 points
Requests alternate on locations.Left: (1,1,…,1,0) Right: (1,0,…,0,0)
Any integral solution must pay (T) over T steps.
Claim: Fractional Algorithm pays only T/(2k) . Left: 0 servers w/p 1/k, and k servers w/p 1-1/k Right: has 1 server w/p 1.
No move cost. Hit cost of 1/k on left requests.
Left Right
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Main Steps
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A word about Fractional Allocation
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Concluding Remarks
Removing dependence of depth on aspect ratio.
Thm: HST -> Weighted HST with O(log n) depth.
Extend Allocation to weighted star.
Main question: Can we remove dependence on n.
1. Metric -> HST
2. But even on HST (lose depth of HST)
1 2 4 8
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Thank you