ACM Home Page
Please provide us with feedback. Feedback
Approximating the average response time in broadcast scheduling
Full text PdfPdf (623 KB)
Source Symposium on Discrete Algorithms archive
Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms table of contents
Vancouver, British Columbia
SESSION: Session 3B table of contents
Pages: 215 - 221  
Year of Publication: 2005
ISBN:0-89871-585-7
Authors
Nikhil Bansal  IBM T.J. Watson Research Center, Yorktown Heights, NY
Moses Charikar  Princeton University, Princeton, NJ
Sanjeev Khanna  University of Pennsylvania, Philadelphia PA
Joseph (Seffi) Naor  Technion, Haifa, Israel
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
: SIAM Activity Group on Discrete Mathematics
Publisher
Society for Industrial and Applied Mathematics  Philadelphia, PA, USA
Bibliometrics
Downloads (6 Weeks): 3,   Downloads (12 Months): 28,   Citation Count: 9
Additional Information:

abstract   references   cited by   collaborative colleagues  

Tools and Actions: Review this Article  

ABSTRACT

We consider the problem of approximating the minimum average response time in on-demand data broadcasting systems. The best approximation factors known for this problem involve resource augmentation. We provide the first non-trivial approximation factors in the absence of resource augmentation, achieving an additive O(√n)-approximation, where n is the number of distinct pages. Our result can be extended, for any ε > 0, to a (1 + ε)-speed, additive O(1/ε)-approximation algorithm. Prior to our work, no non-trivial approximation factor was known for the case of ε < 1.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
N. Alon, and J. Spencer, The Probabilistic Method. John Wiley & Sons, 2000.
 
2
 
3
M. Bellare and J. Rompel. Randomness-Efficient Oblivious Sampling. Proc. of the 35th Annual IEEE Symposium on Foundations of Computer Science, pp. 276--287, 1994.
 
4
 
5
 
6
 
7
 
8
 
9
 
10
K. Pruhs, J. Sgall, E. Torng, Online Scheduling. Handbook of Scheduling: Algorithms, Models, and Performance Analysis, editor Joseph Y-T. Leung, CRC Press, 2004.

CITED BY  9
Collaborative Colleagues:
Nikhil Bansal: colleagues
Moses Charikar: colleagues
Sanjeev Khanna: colleagues
Joseph (Seffi) Naor: colleagues