Hierarchical packet fair queueing algorithms

Abstract

Hierarchical Packet Fair Queueing (H-PFQ) algorithms have the potential to simultaneously support guaranteed realtime service, rate-adaptive best-eort, and controlled linksharing service. In this paper, we design practical H-PFQ algorithms by using one-level Packet Fair Queueing (PFQ) servers as basic building blocks, and develop techniques to analyze delay and fairness properties of the resulted H-PFQ servers. We demonstrate that, in order to provide tight delay bounds in a H-PFQ server, it is essential for the onelevel PFQ servers to have small Worst-case Fair Indices (WFI). We propose a new one-level PFQ algorithm called WF 2 Q+ that is the rst to have all the following three properties: (a) providing the tightest delay bound among all PFQ algorithms; (b) having the smallest WFI among all PFQ algorithms; and (c) having a relatively low implementation complexity of O(log N). We show that practical H-PFQ algorithms can be implemented by using WF 2 Q+ as the basic building block and prove that the resulting H-WF 2 Q+ algorithms provide similar delay bounds and bandwidth distribution as those provided by a H-GPS server. Simulation experiments are presented to evaluate the proposed algorithm.

Profit-oriented resource allocation using online scheduling in flexible heterogeneous networks

“…The proper allocation between the reserved bandwidth and the excess bandwidth can be achieved by a generic scheduler, such as Hierarchical Packet Fair Queueing mechanism proposed by Bennett and Zhang [2].…”

Temporal fairness guarantee in multi-rate wireless LANs for per-flow protection

“…In previous work, per-flow protection has usually been provided by method of achieving max-min throughput fairness [5][6][7][8][9].…”

Performance analysis of a novel hybrid push–pull algorithm with QoS adaptations in wireless networks

“…There are many schemes [1][2][3][4][5][6][7][8][9][11][12][13][14]16] which have been developed to partition the data items into either the push-based set or the pull-based set. However, none exist so far which have studied at all these four factors in combination: multiple data set sizes, quality of service (QoS), spurious cases leading to inaccurate portrayals of the system dynamics, and doing implementation without previous knowledge of data access probabilities.…”

“…The stretch-optimal scheduling algorithm [16] does look at non-uniform data item sizes, however it is only intended for a pure broadcast system. While some authors [1][2][3]5,8,11,12,14] have assumed a priori knowledge of the data item access probabilities, others [6,7,9] have developed algorithms based on static analysis and the fact that there is only downward communication from the server to the clients.…”