Simulated annealing: theory and applications
Simulated annealing: theory and applications
Convex Optimization
Capacity of large-scale CSMA wireless networks
Proceedings of the 15th annual international conference on Mobile computing and networking
Towards utility-optimal random access without message passing
Wireless Communications & Mobile Computing - Recent Advances in Wireless Communications and Networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Back-of-the-Envelope Computation of Throughput Distributions in CSMA Wireless Networks
IEEE Transactions on Mobile Computing
A tutorial on cross-layer optimization in wireless networks
IEEE Journal on Selected Areas in Communications
Cross-layer optimization for wireless networks with deterministic channel models
INFOCOM'10 Proceedings of the 29th conference on Information communications
Distributed stochastic optimization in opportunistic networks: the case of optimal relay selection
Proceedings of the 5th ACM workshop on Challenged networks
Optimal neighbor selection in BitTorrent-like peer-to-peer networks
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
On the performance of TCP over throughput-optimal CSMA
Proceedings of the Nineteenth International Workshop on Quality of Service
Optimal neighbor selection in BitTorrent-like peer-to-peer networks
ACM SIGMETRICS Performance Evaluation Review - Performance evaluation review
Capacity of large-scale CSMA wireless networks
IEEE/ACM Transactions on Networking (TON)
Applications of belief propagation in CSMA wireless networks
IEEE/ACM Transactions on Networking (TON)
Stability and delay of distributed scheduling algorithms for networks of conflicting queues
Queueing Systems: Theory and Applications
Cross-layer lifetime maximisation in wireless multihop networks with network coding
International Journal of Ad Hoc and Ubiquitous Computing
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Many important network design problems can be formulated as a combinatorial optimization problem. A large number of such problems, however, cannot readily be tackled by distributed algorithms. The Markov approximation framework studied in this paper is a general technique for synthesizing distributed algorithms. We show that when using the log-sum-exp function to approximate the optimal value of any combinatorial problem, we end up with a solution that can be interpreted as the stationary probability distribution of a class of timereversible Markov chains. Certain carefully designed Markov chains among this class yield distributed algorithms that solve the log-sum-exp approximated combinatorial network optimization problem. By three case studies, we illustrate that Markov approximation technique not only can provide fresh perspective to existing distributed solutions, but also can help us generate new distributed algorithms in various domains with provable performance. We believe the Markov approximation framework will find applications in many network optimization problems, and this paper serves as a call for participation.