Competitive queueing policies for QoS switches
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Buffer Overflow Management in QoS Switches
SIAM Journal on Computing
An optimal online algorithm for packet scheduling with agreeable deadlines
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Competitive queue policies for differentiated services
Journal of Algorithms
Better online buffer management
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Considering suppressed packets improves buffer management in QoS switches
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Competitive Buffer Management with Stochastic Packet Arrivals
SEA '09 Proceedings of the 8th International Symposium on Experimental Algorithms
Buffer management for colored packets with deadlines
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
A survey of buffer management policies for packet switches
ACM SIGACT News
Non-preemptive buffer management for latency sensitive packets
INFOCOM'10 Proceedings of the 29th conference on Information communications
The loss of serving in the dark
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
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We consider the online problem of non-preemptive queue management. An online sequence of packets arrive, each of which has an associated intrinsic value. Packets can be accepted to a FIFO queue, or discarded. The profit gained by transmitting a packet diminishes over time and is equal to its value minus the delay. This corresponds to the well known and strongly motivated Naor's model in operations research. We give a queue management algorithm with a competitive ratio equal to the golden ratio (φ ≈ 1.618) in the case that all packets have the same value, along with a matching lower bound. We also derive Θ(1) upper and lower bounds on the competitive ratio when packets have different intrinsic values (in the case of differentiated services). We can extend our results to deal with more general models for loss of value over time. Finally, we re-interpret our online algorithms in the context of selfish agents, producing an online mechanism that approximates the optimal social welfare to within a constant factor.