Network adiabatic theorem: an efficient randomized protocol for contention resolution

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.

[1]  V. Fock,et al.  Beweis des Adiabatensatzes , 1928 .

[2]  E. Villaseñor Introduction to Quantum Mechanics , 2008, Nature.

[3]  Norman Abramson,et al.  The ALOHA SYSTEM. , 1972 .

[4]  R. Getoor Transience and recurrence of Markov processes , 1980 .

[5]  Frank Kelly,et al.  Stochastic Models of Computer Communication Systems , 1985 .

[6]  Philippe Flajolet,et al.  Estimating the multiplicities of conflicts to speed their resolution in multiple access channels , 1987, JACM.

[7]  David J. Aldous Ultimate instability of exponential back-off protocol for acknowledgment-based transmission control of random access communication channels , 1987, IEEE Trans. Inf. Theory.

[8]  Frank Thomson Leighton,et al.  Analysis of backoff protocols for multiple access channels , 1987, STOC '87.

[9]  I. MacPhee,et al.  The Number of Packets Transmitted by Collision Detect Random Access Schemes , 1987 .

[10]  Leandros Tassiulas,et al.  Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks , 1990, 29th IEEE Conference on Decision and Control.

[11]  Leandros Tassiulas,et al.  Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks , 1992 .

[12]  Richard L. Tweedie,et al.  Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.

[13]  Frank Thomson Leighton,et al.  Analysis of Backoff Protocols for Multiple Access Channels , 1996, SIAM J. Comput..

[14]  Anthony Ephremides,et al.  Information Theory and Communication Networks: An Unconsummated Union , 1998, IEEE Trans. Inf. Theory.

[15]  Sampath Kannan,et al.  A Bound on the Capacity of Backoff and Acknowledgement-Based Protocols , 2000, ICALP.

[16]  S. Foss,et al.  AN OVERVIEW OF SOME STOCHASTIC STABILITY METHODS( Network Design, Control and Optimization) , 2004 .

[17]  Heriot-Watt University AN OVERVIEW OF SOME STOCHASTIC STABILITY METHODS , 2004 .

[18]  Devavrat Shah,et al.  Optimal Scheduling Algorithms for Input-Queued Switches , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[19]  Eytan Modiano,et al.  Maximizing throughput in wireless networks via gossiping , 2006, SIGMETRICS '06/Performance '06.

[20]  P. Gupta,et al.  Optimal Throughput Allocation in General Random-Access Networks , 2006, 2006 40th Annual Conference on Information Sciences and Systems.

[21]  R. Srikant,et al.  Network Optimization and Control , 2008, Found. Trends Netw..

[22]  Jiaping Liu,et al.  Distributed queue-length based algorithms for optimal end4o-end throughput allocation and stability in multi-hop random access networks , 2007 .

[23]  Peter Marbach,et al.  Distributed Scheduling and Active Queue Management in Wireless Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[24]  Asuman E. Ozdaglar,et al.  Achievable rate region of CSMA schedulers in wireless networks with primary interference constraints , 2007, 2007 46th IEEE Conference on Decision and Control.

[25]  Alexander L. Stolyar Dynamic Distributed Scheduling in Random Access Networks , 2005 .

[26]  Charles Bordenave,et al.  Performance of Random Medium Access - An asymptotic approach , 2008 .

[27]  Alexandre Proutière,et al.  Performance of random medium access control, an asymptotic approach , 2008, SIGMETRICS '08.

[28]  R. Srikant,et al.  Distributed Link Scheduling With Constant Overhead , 2006, IEEE/ACM Transactions on Networking.

[29]  Eytan Modiano,et al.  Distributed Cross-Layer Algorithms for the Optimal Control of Multihop Wireless Networks , 2010, IEEE/ACM Transactions on Networking.

[30]  Eytan Modiano,et al.  Distributed cross-layer algorithms for the optimal control of multihop wireless networks , 2010, IEEE/ACM Trans. Netw..

[31]  Jean C. Walrand,et al.  A Distributed CSMA Algorithm for Throughput and Utility Maximization in Wireless Networks , 2010, IEEE/ACM Transactions on Networking.