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A Unified Approach to Approximating Resource Allocation and Scheduling

A Unified Approach to Approximating Resource Allocation and Scheduling. Amotz Bar-Noy.……...AT&T and Tel Aviv University Reuven Bar-Yehuda….Technion IIT Ari Freund……………Technion IIT Seffi Naor…………….Bell Labs and Technion IIT Baruch Schieber…...…IBM T.J. Watson Slides and paper at:

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A Unified Approach to Approximating Resource Allocation and Scheduling

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  1. A Unified Approach to Approximating Resource Allocation and Scheduling • Amotz Bar-Noy.……...AT&T and Tel Aviv University • Reuven Bar-Yehuda….Technion IIT • Ari Freund……………Technion IIT • Seffi Naor…………….Bell Labs and Technion IIT • Baruch Schieber…...…IBM T.J. Watson • Slides and paper at: • http://www.cs.technion.ac.il/~reuven

  2. Summery of Results:Discrete • Single Machine Scheduling • Bar-Noy, Guha, Naor and Schieber STOC 99: 1/2 Non Combinatorial* •   Berman, DasGupta, STOC 00: 1/2 •   This Talk, STOC 00(Independent)      1/2 • Bandwidth AllocationAlbers, Arora, Khanna SODA 99: O(1) for |Activityi|=1*Uma, Phillips, Wein SODA 00:        1/4 Non combinatorial.This Talk STOC 00 (Independent)       1/3 for w  1/2 This Talk STOC 00 (Independent)       1/5 for w  1    • Parallel Unrelated Machines: • Bar-Noy, Guha, Naor and Schieber STOC 99: 1/3 •   Berman, DasGupta STOC 00: 1/2 •   This Talk, STOC 00(Independent)      1/2

  3. Summery of Results:Continuous • Single Machine Scheduling • Bar-Noy, Guha, Naor and Schieber STOC 99: 1/3 Non Combinatorial • Berman, DasGupta STOC 00: 1/2·(1-) •   This Talk, STOC 00: (Independent)1/2·(1-) • Bandwidth AllocationUma, Phillips, Wein SODA 00:       1/6 Noncombinatorial • This Talk, STOC 00 (Independent)       1/3 ·(1-) for w  1/2   1/5 ·(1-) for w  1 • Parallel unrelated machines: • Bar-Noy, Guha, Naor and Schieber STOC 99: 1/4 • Berman, DasGupta STOC 00: 1/2·(1-) •   This Talk, STOC 00: (Independent)1/2·(1-)

  4. Summery of Results:and more… • General Off-line Caching Problem •  Albers, Arora, Khanna SODA 99: O(1) Cache_Size += O(Largest_Page)    O(log(Cache_Size+Max_Page_Penalty)) • This Talk, STOC 00: 4 • Ring topology: • Transformation of approx ratio from line to ring topology 1/ 1/(+1+) • Dynamic storage allocation (contiguous allocation): • Previous results: none for throughput maximization • Previous results Kierstead 91 for resource minimization: 6 • This paper: 1/35 for throughput max using the result for resource min.

  5. The Local-Ratio Technique: Basic definitions • Given a profit [penalty] vector p. • Maximize[Minimize]p·x • Subject to: feasibility constraints F(x) • x isr-approximationif F(x) and p·x []r·p·x* • An algorithm is r-approximationif for any p, F • it returns an r-approximation

  6. The Local-Ratio Theorem: • xis an r-approximation with respect to p1 • xis an r-approximation with respect to p- p1 •  • xis an r-approximation with respect to p • Proof: (For maximization) • p1 · x  r ×p1* • p2 · x  r ×p2* •  • p · x  r ×( p1*+ p2*) •  r ×(p1 + p2 )*

  7. Special case: Optimization is 1-approximation • xis an optimum with respect to p1 • xis an optimum with respect to p- p1 • xis an optimum with respect to p

  8. A Local-Ratio Schema for Maximization[Minimization] problems: • Algorithm r-ApproxMax[Min]( Set, p ) • If Set = Φ then returnΦ ; • If  I  Setp(I)  0 then returnr-ApproxMax( Set-{I}, p ) ; • [If I  Setp(I)=0 then return {I} r-ApproxMin( Set-{I}, p ) ;] • Define “good” p1 ; • REC = r-ApproxMax[Min]( S, p- p1) ; • If REC is not an r-approximation w.r.t. p1 then “fix it”; • return REC;

  9. The Local-Ratio Theorem: Applications Applications to some optimization algorithms (r = 1): ( MST) Minimum Spanning Tree (Kruskal) ( SHORTEST-PATH) s-t Shortest Path (Dijkstra) (LONGEST-PATH) s-t DAG Longest Path (Can be done with dynamic programming) (INTERVAL-IS) Independents-Set in Interval Graphs Usually done with dynamic programming) (LONG-SEQ) Longest (weighted) monotone subsequence (Can be done with dynamic programming) ( MIN_CUT) Minimum Capacity s,t Cut (e.g. Ford, Dinitz) Applications to some 2-Approximation algorithms: (r = 2) ( VC) Minimum Vertex Cover (Bar-Yehuda and Even) ( FVS) Vertex Feedback Set (Becker and Geiger) ( GSF) Generalized Steiner Forest (Williamson, Goemans, Mihail, and Vazirani) ( Min 2SAT) Minimum Two-Satisfibility (Gusfield and Pitt) ( 2VIP) Two Variable Integer Programming (Bar-Yehuda and Rawitz) ( PVC) Partial Vertex Cover (Bar-Yehuda) ( GVC) Generalized Vertex Cover (Bar-Yehuda and Rawitz) Applications to some other Approximations: ( SC) Minimum Set Cover (Bar-Yehuda and Even) ( PSC) Partial Set Cover (Bar-Yehuda) ( MSP) Maximum Set Packing (Arkin and Hasin) Applications Resource Allocation and Scheduling: ….

  10. Maximum Independent Set in Interval Graphs • Activity9 • Activity8 • Activity7 • Activity6 • Activity5 • Activity4 • Activity3 • Activity2 • Activity1 • time • Maximize s.t.For each instance I: • For each time t:

  11. Maximum Independent Set in Interval Graphs: How to select P1 to get optimization? P1=0 • Activity9 • Activity8 • Activity7 • Activity6 • Activity5 • Activity4 • Activity3 • Activity2 • Activity1 Î • time • Let Î be an interval that ends first; • 1 if I in conflict with Î • For all intervals I define: p1(I) = • 0 else • For every feasible x: p1 ·x  1 • Every Î-maximal is optimal. • For every Î-maximal x: p1 ·x  1 P1=1 P1=0 P1=0 P1=0 P1=1 P1=0 P1=1 P1=1

  12. Maximum Independent Set in Interval Graphs: An Optimization Algorithm P1=P(Î ) • Activity9 • Activity8 • Activity7 • Activity6 • Activity5 • Activity4 • Activity3 • Activity2 • Activity1 Î • time • Algorithm MaxIS( S, p ) • If S = Φ then returnΦ ; • If ISp(I) 0 then returnMaxIS( S - {I}, p); • Let ÎS that ends first; • IS define: p1(I) = p(Î)  (I in conflict with Î) ; • IS = MaxIS( S, p- p1) ; • If IS is Î-maximal then returnIS else return IS {Î}; P1=0 P1=0 P1=0 P1=0 P1=P(Î ) P1=0 P1=P(Î ) P1=P(Î )

  13. Maximum Independent Set in Interval Graphs: Running Example P(I5) = 3 -4 P(I6) = 6 -4 -2 P(I3) = 5 -5 P(I2) = 3 -5 P(I1) = 5 -5 P(I4) = 9 -5 -4 -4 -5 -2

  14. Bar-Noy, Guha, Naor and Schieber STOC 99: 1/2 LP Berman, DasGupta, STOC 00: 1/2 This Talk, STOC 00(Independent)      1/2 Single Machine Scheduling : • Activity9 • Activity8 • Activity7 • Activity6 • Activity5 • Activity4 • Activity3 • Activity2 • Activity1 ????????????? • time • Maximize s.t.For each instance I: • For each time t: • For each activity A:

  15. Single Machine Scheduling: How to select P1 to get ½-approximation ? P1=1 P1=0 • Activity9 • Activity8 • Activity7 • Activity6 • Activity5 • Activity4 • Activity3 • Activity2 • Activity1 Î • time • Let Î be an interval that ends first; • 1 if I in conflict with Î • For all intervals I define: p1(I) = • 0 else • For every feasible x: p1 ·x  2 • Every Î-maximal is 1/2-approximation • For every Î-maximal x: p1 ·x  1 P1=0 P1=1 P1=0 P1=0 P1=0 P1=0 P1=0 P1=0 P1=1 P1=0 P1=1 P1=0 P1=0 P1=1 P1=1 P1=1 P1=1 P1=1

  16. Single Machine Scheduling: The ½-approximation Algorithm • Activity9 • Activity8 • Activity7 • Activity6 • Activity5 • Activity4 • Activity3 • Activity2 • Activity1 Î • time • Algorithm MaxIS( S, p ) • If S = Φ then returnΦ ; • If ISp(I) 0 then returnMaxIS( S - {I}, p); • Let ÎS that ends first; • IS define: p1(I) = p(Î)  (I in conflict with Î) ; • IS = MaxIS( S, p- p1) ; • If IS is Î-maximal then returnIS else return IS {Î};

  17. Albers, Arora, Khanna SODA 99: O(1) |Ai|=1* Uma, Phillips, Wein SODA 00: 1/4 LP. This Talk 1/3 for w  1/2 and 1/5 for w  1 Bandwidth Allocation • Activity9 • Activity8 • Activity7 • Activity6 • Activity5 • Activity4 • Activity3 • Activity2 • Activity1 I w(I) • s(I) e(I) time • Maximize s.t.For each instance I: • For each time t: • For each activity A:

  18. Bandwidth Allocation for w 1/2 How to select P1 to get 1/3-approximation? • Activity9 • Activity8 Î • Activity7 • Activity6 • Activity5 • Activity4 • Activity3 • Activity2 • Activity1 I w(I) • s(I) e(I) time • 1if I in the same activity of Î • For all intervals I define: p1(I) = 2*w(I)if I in time conflict with Î • 0 else • For every feasible x: p1 ·x 3 • Every Î-maximal is 1/3-approximation • For every Î-maximal x: p1 ·x 1

  19. Bandwidth Allocation The 1/5-approximation for any w  1 • Activity9 • Activity8 w > ½ • Activity7 w > ½ w > ½ • Activity6 • Activity5 w > ½ • Activity4 • Activity3 w > ½ w > ½ • Activity2 • Activity1 w > ½w > ½ w > ½ • Algorithm: • GRAY = Find 1/2-approximation for gray (w>1/2) intervals; • COLORED = Find 1/3-approximation for colored intervals • Return the one with the larger profit • Analysis: • If GRAY*  40%OPT then GRAY 1/2(40%OPT)=20%OPT else • COLORED*  60%OPT thus COLORED 1/3(60%OPT)=20%OPT

  20. Continuous Scheduling { • Single Machine Scheduling (w=1) • Bar-Noy, Guha, Naor and Schieber STOC 99: 1/3 Non Combinatorial •   Berman, DasGupta STOC 00: 1/2·(1-) •   This Talk, STOC 00: (Independent) 1/2·(1-) • Bandwidth AllocationUma, Phillips, Wein SODA 00:       1/6 Noncombinatorial • This Talk, STOC 00 (Independent)       1/3 ·(1-) for w  1/2   1/5 ·(1-) for w  1 w(I)  d(I)  s(I) e(I)

  21. Continuous Scheduling: Split and Round Profit (Loose additional (1-) factor) • If currant p(I1)   original p(I1) then delete I1 • else Split I2=(s2,e2] to I21=(s2, s1+d1] and I22=(s1+d1,e2]  d(I1)   d(I2)  I11 I12  d(I1)  I21 I22  d(I2) 

  22. Minimization problem: • General Off-line Caching Problem

  23. The Demand Scheduling Problem • Resource • 0.9 • 0.8 • 0.7 • 0.6 • 0.5 • 0.4 • 0.3 • 0.2 • 0.1 w(I) • 0.0 • Minimize s.t.For each instance I: • For each time t: • t Demand(t)

  24. Special Case: “Min Knapsack” • Demand = 1 • For all intervals I define: p1(I) = Min {w(I), 1} • For every feasible x: p1 ·x 1 • minimal is 2-approximation • For every minimal x: p1 ·x  2

  25. From Knapsack to Demand Scheduling • max demand=1 • at time t’ • For all intervals I intersecting time t’define: p1(I) = Min {w(I), 1} • p1(others) = 0 • p1 (all “right-minimal”) is at most 2 • p1 (all “left-minimal”) is at most 2 • For every minimal x: p1 ·x  2+2 • For every feasible x: p1 ·x  1 • Every minimal is 4-approximation

  26. Albers, Arora, Khanna SODA 99: O(1) Cache_Size += O(Largest_Page)O(log(Cache_Size+Max_Page_Penalty)) This Talk 4 General Off-line Caching Problem • 0.9 • 0.8 • w(page2)=0.7 • 0.6 • w(page1)=0.5 • 0.4 • w(page3)=0.3 • 0.2 • 0.1 • 0.0 • page1 page2page3page1page3 page2page3 • w(pagei) = size of pagei • p(pagei) = the reload cost of the page Cache size

  27. 4-Approximation for Demand Scheduling • Algorithm MinDemandCover( S, p ) • If S = Φ then returnΦ; • If there exists an interval IS s.t. p(I) = 0 ; • then return {I}+MinDemandCover( S - {I}, p); • Let t’ be the time with maximum demand k; • Let S’ be the set of instances intersects time t ; • Let δ= MIN{p(I)/w(I) : I S’}; • MIN {w(I) ,k} if I S’ • For all intervals I  S define:p1 (I) = δ× • 0 else • C = MinDemandCover( S, p- p1) ; • Remove elements form C until it is minimal and returnC ;

  28. Application: 4-Approximationfor the Loss Minimization Problem • Resource • The cost of a schedule is the sum of profits of instances not in the schedule. • For the special case where Ai is a singleton {Ii} the problem is equivalent • to the Min Demand Scheduling where:

  29. END?

  30. Parallel Unrelated Machines: Continous Bar-Noy, Guha, Naor and Schieber STOC 99: 1/3 1/4   Berman, DasGupta STOC 00: 1/2 1/2·(1-)   This Talk, STOC 00(Independent)      1/2 1/2·(1-) d d d d time jobs d d d d d d

  31. Parallel unrelated machines: i k A c i h c c k d time d h i A machine c h d d

  32. Parallel unrelated machines: 1/5-approximation (not in the paper)Each machine resource 1p1(Red ) = p1(orange d) = 1;p1(Yellowd ) = 2width; p1 (All others) = 0; i k A c i d h c c k d time d h i d A machine c h d d d

  33. END!

  34. Preliminaries • Activity9 • Activity8 • Activity7 • Activity6 • Activity5 • Activity4 • Activity3 • Activity2 • Activity1I w(I) • s(I) e(I) time

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