A service provider offers four classes of service for MPLS VPN customer with the following IPP/EXP values: i.
voice = 5 ii.
video = 4 iii.
priority data = 3 iv.
best-effort data = 0 The service-provider also supports multicast VPN services in the priority data and best-effort classes.
Multicast VPN is implemented as a draft-rosen profile.
On a P router, in the MPLS core, the ingress QoS policy-map must classify all incoming packets, so that different classes of traffic can be property handled in fabric queues and egress queues.
All types of packets in the core network must be covered.
Which ingress classification offers the optimal performance and provides the minimum number of actions that match the service provider QoS requirements?
Click on the arrows to vote for the correct answerA. B. C. D.
This question requires a thorough understanding of MPLS VPN QoS and classification. MPLS VPN is a technology that allows service providers to offer VPN services to their customers over a shared MPLS network. QoS is a critical aspect of any MPLS VPN service since it enables providers to offer differentiated services to their customers.
The IPP/EXP field in the MPLS header is used to mark different classes of traffic. It is a 3-bit field that can have a value from 0 to 7. A value of 0 indicates best-effort traffic, while a value of 7 indicates network control traffic. Different classes of traffic can be mapped to different IPP/EXP values to enable differentiated treatment of traffic in the network.
In this scenario, the service provider offers four classes of service for MPLS VPN customers, each with a specific IPP/EXP value. Voice traffic is assigned an IPP/EXP value of 5, video traffic an IPP/EXP value of 4, priority data traffic an IPP/EXP value of 3, and best-effort data traffic an IPP/EXP value of 0. The service provider also supports multicast VPN services in the priority data and best-effort classes, implemented as a draft-rosen profile.
The question requires the ingress QoS policy-map on a P router in the MPLS core to classify all incoming packets, so that different classes of traffic can be properly handled in fabric queues and egress queues. The policy-map should cover all types of packets in the core network and offer optimal performance while meeting the service provider's QoS requirements.
Option A: The ingress classification in option A offers the optimal performance and provides the minimum number of actions that match the service provider QoS requirements. The class-map matches the VOICE, VIDEO, PRIORITY, and CONTROL classes using the topmost MPLS experimental value. The PRIORITY and BEST-EFFORT classes are covered using a single CONTROL class. The policy-map then sets the GOS-group based on the matched class.
Option B: The ingress classification in option B uses the same class-map matching approach as option A but adds an additional match on the IP precedence value for some classes. This approach is unnecessary since MPLS experimental values already differentiate the traffic classes. This option also has a separate match for the CONTROL class, which covers both PRIORITY and BEST-EFFORT traffic.
Option C: The ingress classification in option C matches the VOICE and VIDEO classes using the topmost MPLS experimental value. However, it uses a separate class-map for the CONTROL class instead of covering both PRIORITY and BEST-EFFORT traffic in a single class-map. The PRIORITY class also has an unnecessary match on IP precedence.
Option D: The ingress classification in option D uses a different approach to classify traffic based on IP precedence instead of MPLS experimental values. This approach is not optimal since MPLS experimental values are the recommended way to classify traffic in an MPLS network. Additionally, the option has a typo, with the PRIOTIRY class instead of PRIORITY.
In conclusion, option A is the best choice for ingress classification in this scenario since it matches all traffic classes using MPLS experimental values, covers both PRIORITY and BEST-EFFORT traffic in a single class-map, and offers optimal performance with the minimum number of actions.