Resource discovery and fair intelligent admission control over scalable Internet
- Publication Type:
- Thesis
- Issue Date:
- 2004
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The Internet currently supports a best-effort connectivity service. There has been an increasing demand for the Internet to support Quality of Service (QoS) to satisfy stringent service requirements from many emerging networking applications and yet to utilize the network resources efficiently. However, it has been found that even with augmented QoS architecture, the Internet cannot achieve the desired QoS and furthermore, there are concerns about the scalability of any available QoS solutions. If the network is not provisioned adequately, the Internet is not capable to handle congestion condition. This is because the Internet is unaware of its internal network QoS states therefore it is not possible to provide QoS when the network state changes dynamically. This thesis addresses the following question: Is it possible to deliver the applications with QoS in the Internet fairly and efficiently while keeping scalability? In this dissertation we answer this question affirmatively by proposing an innovative service architecture: the Resource Discovery (RD) and Fair Intelligent Admission Control (FIAC) over scalable Internet. The main contributions of this dissertation are as follows:
1. To detect the network QoS state, we propose the Resource Discovery (RD) framework to provide network QoS state dynamically. The Resource Discovery (RD) adopts feedback loop mechanism to collect the network QoS state and reports to the Fair Intelligent Admission Control module, so that FIAC is capable to take resource control efficiently and fairly.
2. To facilitate network resource management and flow admission control, two scalable Fair Intelligent Admission Control architectures are designed and analyzed on two levels: per-class level and per-flow level. Per-class FIAC handles the aggregate admission control for certain pre-defined aggregate. Per-flow FIAC handles the flow admission control in terms of fairness within the class.
3. To further improve its scalability, the Edge-Aware Resource Discovery and Fair Intelligent Admission Control is proposed which does not need the core routers involvement. We devise and analyze implementation of the proposed solutions and demonstrate the effectiveness of the approach. For the Resource Discovery, two closed-loop feedback solutions are designed and investigated. The first one is a core-aware solution which is based on the direct QoS state information. To further improve its scalability, the edge-aware solution is designed where only the edges (not core)are involved in the feedback QoS state estimation. For admission control, FIAC module bridges the gap between 'external' traffic requirements and the 'internal' network ability. By utilizing the QoS state information from RD, FIAC intelligently allocate resources via per-class admission control and per-flow fairness control. We study the performance and robustness of RD-FIAC through extensive simulations. Our results show that RD can obtain the internal network QoS state and FIAC can adjust resource allocation efficiently and fairly.
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