Scheduling in multi-channel wireless networks: rate function optimality in the small-buffer regime
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Coding and control for communication networks
Queueing Systems: Theory and Applications
V-uniform ergodicity for state-dependent single class queueing networks
Queueing Systems: Theory and Applications
Stable and utility-maximizing scheduling for stochastic processing networks
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Low-complexity scheduling algorithms for multi-channel downlink wireless networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
On scheduling for minimizing end-to-end buffer usage over multihop wireless networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
On wireless scheduling algorithms for minimizing the queue-overflow probability
IEEE/ACM Transactions on Networking (TON)
Optimal queue-size scaling in switched networks
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Asymptotically tight steady-state queue length bounds implied by drift conditions
Queueing Systems: Theory and Applications
Low-complexity scheduling algorithms for multichannel downlink wireless networks
IEEE/ACM Transactions on Networking (TON)
Hi-index | 0.01 |
It is shown that stability of the celebrated MaxWeight or back pressure policies is a consequence of the following interpretation: either policy is myopic with respect to a surrogate value function of a very special form, in which the “marginal disutility” at a buffer vanishes for a vanishingly small buffer population. This observation motivates the $h$-MaxWeight policy, defined for a wide class of functions $h$. These policies share many of the attractive properties of the MaxWeight policy as follows: (i) Arrival rate data is not required in the policy. (ii) Under a variety of general conditions, the policy is stabilizing when $h$ is a perturbation of a monotone linear function, a monotone quadratic, or a monotone Lyapunov function for the fluid model. (iii) A perturbation of the relative value function for a workload relaxation gives rise to a myopic policy that is approximately average-cost optimal in heavy traffic, with logarithmic regret. The first results are obtained for a general Markovian network model. Asymptotic optimality is established for a general Markovian scheduling model with a single bottleneck, and with homogeneous servers.