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Measurement-based admission control for a flow-aware network

To provide statistical service guarantee and achieve high network utilization, measurement-based admission control (MBAC) has been studied for over one decade. Many MBAC algorithms have been proposed in the literature. However, most of them belong to aggregate MBAC algorithms which assume or require that (1) first-in-first-out (FIFO) is used for aggregating flows; (2) statistical service guarantees are provided to the aggregate of admitted flows; (3) each flow requires and experiences the same statistical service guarantees as the aggregate. In this paper, we focus on per-flow MBAC that aims to provide possibly different statistical service guarantees to individual flows in an aggregate. Particularly, we propose a simple per-flow MBAC algorithm in which dynamic priority scheduling (DPS) is adopted to aggregate flows. With this DPS-based per-flow MBAC algorithm, a newly admitted flow is always given a lower priority level than all existing flows, and its priority level is improved if an existing flow leaves the system. Consequently, once a flow is admitted, its received service will not be adversely affected by other flows admitted after it. Because of this, there is no need to re-check or adjust network resources allocated to existing flows due to the admission of a new flow.