Optimization flow control—I: basic algorithm and convergence
IEEE/ACM Transactions on Networking (TON)
Resource allocation and cross-layer control in wireless networks
Foundations and Trends® in Networking
Optimal delay scheduling in networks with arbitrary constraints
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Order optimal delay for opportunistic scheduling in multi-user wireless uplinks and downlinks
IEEE/ACM Transactions on Networking (TON)
Delay analysis for maximal scheduling with flow control in wireless networks with bursty traffic
IEEE/ACM Transactions on Networking (TON)
Delay reduction via lagrange multipliers in stochastic network optimization
WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
Dynamic server allocation to parallel queues with randomly varying connectivity
IEEE Transactions on Information Theory
Dynamic power allocation and routing for time-varying wireless networks
IEEE Journal on Selected Areas in Communications
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We consider the problem of delay-efficient scheduling in general multihop networks. While the class of max-weight type algorithms are known to be throughput optimal for this problem, they typically incur undesired delay performance. In this paper, we propose the Delay-Efficient SCheduling algorithm (DESC). DESC is built upon the idea of accelerating queues (AQ), which are virtual queues that quickly propagate the traffic arrival information along the routing paths. DESC is motivated by the use of redundant constraints to accelerate convergence in the classic optimization context. We show that DESC is throughput-optimal. The delay bound of DESC can be better than previous bounds of the max-weight type algorithms which did not use such traffic information. We also show that under DESC, the service rates allocated to the flows converge quickly to their target values and the average total ''network service lag'' is small. In particular, when there are O(1) flows and the rate vector is of @Q(1) distance away from the boundary of the capacity region, the average total ''service lag'' only grows linearly in the network size.