ABSTRACT
In this paper, we present a simple distributed algorithm for resource allocation which simultaneously approximates the optimum value for a large class of objective functions. In particular, we consider the class of canonical utility functions U that are symmetric, non-decreasing, concave, and satisfy U(0) = 0. Our distributed algorithm is based on primal-dual updates. We prove that this algorithm is an O(log ρ)-approximation for all canonical utility functions simultaneously, i.e. without any knowledge of U. The algorithm needs at most O(log2 ρ) iterations. Here n is the number of flows, m is the number of edges, R is the ratio between the maximum capacity and the minimum capacity of the edges in the network, and ρ is max (n, m, R).We extend this result to multi-path routing, and also to a natural pricing mechanism that results in a simple and practical protocol for bandwidth allocation in a network. When the protocol reaches equilibrium, the allocated bandwidths are the same as when the distributed algorithm converges; hence the protocol is also an O(log ρ) approximation for all canonical utility functions.
- Y. Afek, Y. Mansour, and Z. Ostfeld. Convergence complexity of optimistic rate based flow control algorithms. Journal of Algorithms, 30(1):106--143, 1999. Google ScholarDigital Library
- A. Awerbuch and Y. Azar. Local optimization of global objectives: competitive distributed deadlock resolution and resource allocation. Proceedings of the 35th Annual Symposium on Foundations of Computer Science, pages 240--49, 1994.Google ScholarDigital Library
- B. Awerbuch and Y. Shavitt. Converging to approximated max-min flow fairness in logarithmic time. Proceedings of the 17th IEEE Infocom conference, pages 1350--57, 1998.Google ScholarCross Ref
- Y. Bartal, J. Byers, and D. Raz. Global optimization using local information with applications to flow control. 38th Annual Symposium on Foundations of Computer Science, pages 303--312, 1997. Google ScholarDigital Library
- Y. Bartal, M. Farach-Colton, M. Andrews, and L. Zhang. Fast fair and frugal bandwidth allocation in atm networks. Proceedings of the 10th Annual ACM-SIAM Symposium on Discrete Algorithms, pages 92--101, 1999. Google ScholarDigital Library
- R. Bhargava, A. Goel, and A. Meyerson. Using approximate majorization to characterize protocol fairness. Proceedings of ACM Sigmetrics, pages 330--331, June 2001. Google ScholarDigital Library
- S. Cho and A. Goel. Bandwidth allocation in networks: a single dual-update subroutine for multiple objectives. Lecture Notes in Computer Science (proceedings of the first Workshop on Combinatorial and Algorithmic Aspects of Networks (CAAN), Aug 2004), 3405:28--41. Google ScholarDigital Library
- B. Codenotti, A. Saberi, K. Varadarajan, and Y. Ye. Leontief economies encode nonzero sum two-player games. SODA 2006 (to appear), 2006. Google ScholarDigital Library
- L.K. Fleischer. Approximating fractional multicommodity flows independent of the number of commodities. SIAM J. Discrete Math., 13(4):505--520, 2000. Google ScholarDigital Library
- N. Garg and J. Konemann. Faster and simpler algorithms for multicommodity flow and other fractional packing problems. 39th Annual Symposium on Foundations of Computer Science, pages 300--309, 1998. Google ScholarDigital Library
- N. Garg and N. Young. On-line, end-to-end congestion control. IEEE Foundations of Computer Science, pages 303--312, 2002. Google ScholarDigital Library
- A. Goel and A. Meyerson. Simultaneous optimization via approximate majorization for concave profits or convex costs. Algorithmica, 44(4):301--323, May 2006.Google ScholarCross Ref
- A. Goel, A. Meyerson, and S. Plotkin. Combining fairness with throughput: Online routing with multiple objectives. Journal of Computer and Systems Sciences, 63(1):62--79, 2001. Google ScholarDigital Library
- A. Goel, A. Meyerson, and S. Plotkin. Approximate majorization and fair online load balancing. ACM Transactions on Algorithms, 1(2):338--349, October 2005. Google ScholarDigital Library
- G.H. Hardy, J.E. Littlewood, and G. Polya. Some simple inequalities satisfied by convex functions. Messenger Math., 58:145--152, 1929.Google Scholar
- G.H. Hardy, J.E. Littlewood, and G. Polya. Inequalities. 1st ed., 2nd ed. Cambridge University Press, London and New York., 1934, 1952.Google Scholar
- F.P. Kelly, A.K. Maulloo, and D.K.H. Tan. Rate control in communication networks: shadow prices, proportional fairness and stability. Journal of the Operational Research Society, 49:237--252, 1998.Google ScholarCross Ref
- J. Kleinberg, Y. Rabani, and E. Tardos. Fairness in routing and load balancing. J. Comput. Syst. Sci., 63(1):2--20, 2001. Google ScholarDigital Library
- A. Kumar and J. Kleinberg. Fairness measures for resource allocation. Proceedings of 41st IEEE Symposium on Foundations of Computer Science, 2000. Google ScholarDigital Library
- S. Low, L. Peterson, and L. Wang. Understanding TCP Vegas: a duality model. Proceedings of ACM Sigmetrics, 2001. Google ScholarDigital Library
- M. Luby and N. Nisan. A parallel approximation algorithm for positive linear programming. Proceedings of 25th Annual Symposium on the Theory of Computing, pages 448--57, 1993. Google ScholarDigital Library
- A.W. Marshall and I. Olkin. Inequalities: theory of majorization and its applications. Academic Press (Volume 143 of Mathematics in Science and Engineering), 1979.Google Scholar
- S. Plotkin, D. Shmoys, and E. Tardos. Fast approximation algorithms for fractional packing and covering problems. Math of Oper. Research, pages 257--301, 1994. Google ScholarDigital Library
- A. Tamir. Least majorized elements and generalized polymatroids. Mathematics of Operations Research, 20(3):583--589, 1995. Google ScholarDigital Library
Index Terms
- Pricing for fairness: distributed resource allocation for multiple objectives
Recommendations
Envy-Free Pricing in Large Markets: Approximating Revenue and Welfare
We study the classic setting of envy-free pricing, in which a single seller chooses prices for its many items, with the goal of maximizing revenue once the items are allocated. Despite the large body of work addressing such settings, most versions of ...
Pricing commodities
How should a seller price her goods in a market where each buyer prefers a single good among his desired goods, and will buy the cheapest such good, as long as it is within his budget? We provide efficient algorithms that compute near-optimal prices for ...
Contingent Pricing to Reduce Price Risks
The price for a product may be set too low, causing the seller to leave money on the table, or too high, driving away potential buyers. Contingent pricing can be useful in mitigating these problems. In contingent pricing arrangements, price is ...
Comments