Applying Extra-Resource Analysis to Load Balancing 1 2

Previously, extra-resource analysis has been used to argue that certain on-line algorithms are good choices for solving specific problems because these algorithms perform well with respect to the optimal off-line algorithm when given extra resources. We now introduce a new application for extra-resource analysis: deriving a qualitative divergence between off-line and on-line algorithms. We do this for the load balancing problem, the problem of assigning a list of jobs on m identical machines to minimize the makespan, the maximum load on any machine. We analyze the worst-case performance of on-line and off-line approximation algorithms relative to the performance of the optimal off-line algorithm when the approximation algorithms have k extra machines. Our main results are the following: The Longest-Processing-Time (LPT ) algorithm will produce a schedule with makespan no larger than that of the optimal off-line algorithm if LPT has at least (4m− 1)/3 machines while the optimal off-line algorithm has m machines. In contrast, no on-line algorithm can guarantee the same with any number of extra machines.

[1]  Jeff Edmonds Scheduling in the dark , 2000, Theor. Comput. Sci..

[2]  Allan Borodin,et al.  Competitive paging with locality of reference , 1991, STOC '91.

[3]  Cynthia A. Phillips,et al.  Optimal Time-Critical Scheduling via Resource Augmentation , 1997, STOC '97.

[4]  Piotr Berman,et al.  Speed is More Powerful than Clairvoyance , 1998, Nord. J. Comput..

[5]  Michael B. Richey,et al.  Improved bounds for harmonic-based bin packing algorithms , 1991, Discret. Appl. Math..

[6]  PruhsKirk,et al.  Speed is as powerful as clairvoyance , 2000 .

[7]  David B. Shmoys,et al.  Using dual approximation algorithms for scheduling problems: practical and theoretical results , 1987 .

[8]  Joan Boyar,et al.  The Accommodating Function - A Generalization of the Competitive Ratio , 1999, WADS.

[9]  Zsolt Tuza,et al.  A 13/12 Approximation Algorithm for Bin Packing with Extendable Bins , 1998, Inf. Process. Lett..

[10]  Yossi Azar,et al.  On-Line Bin-Stretching , 1998, RANDOM.

[11]  Tak Wah Lam,et al.  Trade-offs between speed and processor in hard-deadline scheduling , 1999, SODA '99.

[12]  Jeffrey D. Ullman,et al.  Worst-Case Performance Bounds for Simple One-Dimensional Packing Algorithms , 1974, SIAM J. Comput..

[13]  Bala Kalyanasundaram,et al.  The Online Transportation Problem , 2000, SIAM J. Discret. Math..

[14]  Christos H. Papadimitriou,et al.  Beyond competitive analysis [on-line algorithms] , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.

[15]  Eric Torng,et al.  Generating adversaries for request-answer games , 2000, SODA '00.

[16]  Rajmohan Rajaraman,et al.  Analysis of a local search heuristic for facility location problems , 2000, SODA '98.

[17]  Joan Boyar,et al.  The Seat Reservation Problem , 1999, Algorithmica.

[18]  Susanne Albers,et al.  Better bounds for online scheduling , 1997, STOC '97.

[19]  Richard M. Karp,et al.  An efficient approximation scheme for the one-dimensional bin-packing problem , 1982, 23rd Annual Symposium on Foundations of Computer Science (sfcs 1982).

[20]  Zsolt Tuza,et al.  On‐line approximation algorithms for scheduling tasks on identical machines withextendable working time , 1999, Ann. Oper. Res..

[21]  Bala Kalyanasundaram,et al.  Speed is as powerful as clairvoyance , 2000, JACM.

[22]  Ronald L. Graham,et al.  Bounds for certain multiprocessing anomalies , 1966 .

[23]  David S. Johnson,et al.  `` Strong '' NP-Completeness Results: Motivation, Examples, and Implications , 1978, JACM.

[24]  Edward G. Coffman,et al.  Approximation algorithms for bin packing: a survey , 1996 .

[25]  Sanjeev Khanna,et al.  Page replacement for general caching problems , 1999, SODA '99.