ACM Home Page
Please provide us with feedback. Feedback
Network adiabatic theorem: an efficient randomized protocol for contention resolution
Full text PdfPdf (695 KB)
Source
Joint International Conference on Measurement and Modeling of Computer Systems archive
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems table of contents
Seattle, WA, USA
SESSION: Wireless networks table of contents
Pages 133-144  
Year of Publication: 2009
ISBN:978-1-60558-511-6
Authors
Shreevatsa Rajagopalan  MIT, Cambridge, MA, USA
Devavrat Shah  MIT, Cambridge, MA, USA
Jinwoo Shin  MIT, Cambridge, MA, USA
Sponsors
ACM: Association for Computing Machinery
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 115,   Downloads (12 Months): 293,   Citation Count: 2
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1555349.1555365
What is a DOI?

ABSTRACT

The popularity of Aloha-like algorithms for resolution of contention between multiple entities accessing common resources is due to their extreme simplicity and distributed nature. Example applications of such algorithms include Ethernet and recently emerging wireless multi-access networks. Despite a long and exciting history of more than four decades, the question of designing an algorithm that is essentially as simple and distributed as Aloha while being efficient has remained unresolved.

In this paper, we resolve this question successfully for a network of queues where contention is modeled through independent-set constraints over the network graph. The work by Tassiulas and Ephremides (1992) suggests that an algorithm that schedules queues so that the summation of `weight' of scheduled queues is maximized, subject to constraints, is efficient. However, implementing such an algorithm using Aloha-like mechanism has remained a mystery. We design such an algorithm building upon a Metropolis-Hastings sampling mechanism along with selection of `weight' as an appropriate function of the queue-size. The key ingredient in establishing the efficiency of the algorithm is a novel adiabatic-like theorem for the underlying queueing network, which may be of general interest in the context of dynamical systems.


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. Abramson and F. Kuo (Editors). The aloha system. Computer-Communication Networks, 1973.
 
2
3
 
4
M. Born and V. A. Fock. Beweis des adiabatensatzes. Zeitschrift für Physik a Hadrons and Nuclei, 51(3--4):165--180, 1928.
 
5
J. G. Dai. Stability of fluid and stochastic processing networks. Miscellanea Publication, (9), 1999.
 
6
A. Ephremides and B. Hajek. Information theory and communication networks: an unconsummated union. IEEE Transactions on Information Theory, 44(6):2416--2432, 1998.
 
7
A. Eryilmaz, A. Ozdaglar, D. Shah, and E. Modiano. Distributed cross-layer algorithms for the optimal control of multi-hop wireless networks. submitted to IEEE/ACM Transactions on Networking, 2008.
 
8
S. Foss and Takis Konstantopoulos. An overview of some stochastic stability methods. Journal of Operations Research, Society of Japan, 47(4), 2004.
 
9
R. K. Getoor. Transience and recurrence of markov processes. In AzÖma, J. and Yor, M., editors, Séminaire de Probabilités XIV, pages 397--409, 1979.
 
10
 
11
Leslie Ann Goldberg. Design and analysis of contention-resolution protocols, epsrc research grant gr/l60982. http://www.csc.liv.ac.uk/ leslie/contention.html, Last updated, Oct. 2002.
12
 
13
D. J. Griffiths. Introduction to Quantum Mechanics. Pearson Prentice Hall, 2005.
 
14
P. Gupta and A. L. Stolyar. Optimal throughput allocation in general random-access networks. In Proceedings of 40th Annual Conf. Inf. Sci. Systems, IEEE, Princeton, NJ, pages 1254--1259, 2006.
 
15
 
16
L. Jiang and J. Walrand. A distributed csma algorithm for throughput and utility maximization in wireless networks. In Proceedings of 46th Allerton Conference on Communication, Control, and Computing, Urbana-Champaign, IL, 2008.
 
17
J.Liu and A. L. Stolyar. Distributed queue length based algorithms for optimal end-to-end throughput allocation and stability in multi-hop random access networks. In Proceedings of 45th Allerton Conference on Communication, Control, and Computing, Urbana-Champaign, IL, 2007.
 
18
F. P. Kelly. Stochastic models of computer communication systems. J. R. Statist. Soc B, 47(3):379--395, 1985.
 
19
F.P. Kelly and I.M. MacPhee. The number of packets transmitted by collision detect random access schemes. The Annals of Probability, 15(4):1557--1568, 1987.
 
20
I.M. MacPhee. On optimal strategies in stochastic decision processes, d. phil. thesis, university of cambridge, 1989.
 
21
P. Marbach. Distributed scheduling and active queue management in wireless networks. In Proceedings of IEEE INFOCOM, Minisymposium, 2007.
 
22
P. Marbach, A. Eryilmaz, and A. Ozdaglar. Achievable rate region of csma schedulers in wireless networks with primary interference constraints. In Proceedings of IEEE Conference on Decision and Control, 2007.
 
23
24
 
25
Microsoft research lab. self organizing neighborhood wireless mesh networks. http://research.microsoft.com/mesh/.
26
 
27
D. Shah and D. J. Wischik. Optimal scheduling algorithm for input queued switch. In Proceeding of IEEE INFOCOM, 2006.
 
28
D. Shah and D. J. Wischik. Heavy traffic analysis of optimal scheduling algorithms for switched networks. Submitted, 2007.
 
29
 
30
A. L. Stolyar. Dynamic distributed scheduling in random access bnetworks. Journal of Applied Probabability, 45(2):297--313, 2008.
 
31
L. Tassiulas and A. Ephremides. Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Transactions on Automatic Control, 37:1936--1948, 1992.
 
32
B.S. Tsybakov and N. B. Likhanov. Upper bound on the capacity of a random multiple-access system. Problemy Peredachi Informatsii, 23(3):64--78, 1987.


Collaborative Colleagues:
Shreevatsa Rajagopalan: colleagues
Devavrat Shah: colleagues
Jinwoo Shin: colleagues