Traditional EP-based networks provide a best-effort transport service that does not offer any service quality guarantees. IP service quality can be supported using the Internet Engineering Task Force's (IETF) DiffServ architecture and MPLS. The DiffServ architecture addresses supporting multiple traffic classes on a per node basis. Since DiffServ mechanisms alone control only per-hop rather than end-to-end performance, MPLS-based traffic engineering (TE) may be used in addition to efficiently distribute traffic along network paths.
Network traffic engineering and configuration tools can be used to support traffic measurement, admission control, and traffic allocation in traffic tunnels and DiffServ-based link scheduling. Network administrators typically have to adjust configurations of these traffic management component mechanisms in order to engineer network traffic such that QoS requirements are met and transported traffic, along with revenues, is maximized. This is an iterative procedure because of continuously changing network status and traffic conditions. To facilitate network management, the above TE components can be integrated in a policy-based architecture where the policies governing aspects of network behavior are pre-defined and stored in a policy repository, and used by the TE components.
In some cases, policies are easily programmed and maintained by the network administrator. Such examples are: a policy rule that assigns 80% of link bandwidth to “Gold” customer traffic between 9 am and 5 pm, and 50% at other times, or a policy rule that sets the bandwidth overbooking factor (or over-subscription ratio) for admission control at 120%. However, the overall policy scheme applied, as well as various specific policy actions, may depend on network dynamics such as network state and traffic conditions.
As a result, the network operator needs to perform dynamic resource allocation responsive to network status changes. However, because of the complexity of the dynamic resource allocation problem, human-driven resource management can result in an inefficient network configuration, due to time overheads and human errors. Automated dynamic resource management alleviates these effects by minimizing human involvement. Moreover, automating resource policy changes can further facilitate resource management by adjusting resource allocation policies in a dynamic fashion based on demand, resource level, and network performance. By using such a system, policy changes can rely on off-line tested algorithms instead of the administrators' best guess, and can avoid over-engineering the network for coping with all status changes. Overall, the automation approach yields a more efficient and economical network resource management.
One approach to automation of resource allocation is found in “TEAM: A Traffic Engineering Automated Manager for DiffServ-based MPLS Networks”, by Caterina Scoglio, Tricha Anjali, Jaudelice de Oliveira, Leonardo Chen, Ian Akyildiz, George Uhl, & Jeff Smith, IEEE Communications Magazine, October 2004, pp. 134-145. This article describes a set of algorithms to provide QoS and better resource utilization in an MPLS network, and further describes an architecture for integration in an automated network manager. The authors recognize the merit of combining the MPLS and DiffServ technologies to provide QoS in IP networks. TEAM encompasses algorithms for MPLS Label Switched Path (LSP) routing, dimensioning, capacity allocation and preemption. However, these algorithms operate in isolation and TEAM lacks an overall high-level scheme that adapts the combined enforcement of the algorithms in accordance with network status. Moreover, TEAM does not include any algorithms for adjusting the DiffServ Ratios (DSR) of network traffic classes, or OverBooking Factor (OBF) for traffic admission.
Another approach to automation of resource allocation is TEQUILA (Traffic Engineering for QUality of service in the Internet at LArge scale) as described in Engineering the Multi-Service Internet: MPLS and IP-based Techniques, by P. Trimintzios, L. Georgiadis, G. Pavlou, D. Griffin, C. F. Cavalcanti, P. Georgatsos & C. Jacquenet, Proceedings of IEEE International Conference on Telecommunications (ICT 2001), Romania, Bucharest, 4-7 Jun. 2001. This work also addresses traffic management in an MPLS network with DiffServ. A detailed overall policy adaptation scheme with specific methods for MPLS admission control, traffic trunk routing optimization, and dynamic (short-term) route and resource management is presented. In the TEQUILA design, the DiffServ Ratios (DSR) of network classes (DSR policy) are enforced on a per link basis, as opposed to a global policy that is applicable to all links. A problem with TEQUILA's DSR policy is that more frequent DSR policy changes are required, making the system less scalable to the number of traffic trunks and links in the network. Also in TEQUILA, a distinct multi-threshold severity scheme is applied on a per traffic trunk basis. The thresholds, which are the policy parameters corresponding to OBF, have values that are statically assigned by the network administrator, i.e., they are not automatically calculated.
USPTO Backgrounds
USPTO Backgrounds