1. Corelite Architecture: Achieving Rated Weight Fairness
Rigoberto Núñez
Kevin Sinha

2. Introduction
Corelite is a QoS architecture that provides weighted max-min fairness for rate among flows in a network without maintaining any per-flow state in the core routers
3 key mechanisms:
Introduction of markers in a packet flow to reflect the normalized rate of flow
Weighted fair marker feedback at the core routers upon incipient congestion detection
Linear increase/multiplicative decrease based on marker feedback
Chris

3. Background – QoS Platforms
Currently only simple best effort service
Need for QoS platforms -> being developed:
Integrated Services (Intserv): supports absolute per-flow quality of service measures
Problems: requires substantial amount of per-flow state to be maintained in routers, so due to the 100,000s of flows in large networks, this is not a good solution
Differentiated Services (Diffserv): scalable service with *no* per-flow state management needed at core routers
Craig

4. What is Corelite?
Based broadly on Diffserv – among the first approaches to achieve weighted rate fairness in a core stateless network
Key parts of Corelite design:
No per-flow state in core of the network
Simple forwarding behavior for core routers
Low overhead (weighted) fair-feedback scheme at core routers to provide early congestion feedback to the edge
Rate adaptation at the edges without network packet loss
Each flow belongs to a rate class and receives the correlated weight
Craig

5. Weighted Rate Fairness
Defined as a version of max-min fairness
Fairness defined as:
Any flow is entitled to as much network usage as any other
An increase in the allocation of one flow is allowed if it does not hurt other flows

6. Mechanisms Shaping and Marking at the Edge Routers
The flow’s traffic is shaped according to its current
The markers reflect the normalized rate of the flow
Marking Caching and Feedback at the Core Router
Weighted fair marker feedback on incipient congestion detection
Rate Adaptation at the Edge Router
Based on linear-increase-multiplicative-decrease (LIMD)
Question: What makes Corelite achieve weighted rate fairness without maintaining any per-flow state?
Solution: The core router generates feedback in proportion to the normalized rate of a flow because edge routers insert markers to reflect the normalized rate of the flow or the edge router throttles the rate in proportion to the number of received markers.

7. Mechanisms Shaping and Marking at the Edge Routers
The flow’s traffic is shaped according to its current
The markers reflect the normalized rate of the flow
Marking Caching and Feedback at the Core Router
Weighted fair marker feedback on incipient congestion detection
Rate Adaptation at the Edge Router
Based on linear-increase-multiplicative-decrease (LIMD)
Question: What makes Corelite achieve weighted rate fairness without maintaining any per-flow state?
Solution: The core router generates feedback in proportion to the normalized rate of a flow because edge routers insert markers to reflect the normalized rate of the flow or the edge router throttles the rate in proportion to the number of received markers.

8. Achieving Weighted Rate Fairness
Basic operation of corelite:
Step 1: The edge routers send markers in proportion to assigned weight of flow
Step 2: The core router forwards markers and also caches them in marker cache. If incipient congestion is detected, it selects markers and sends them back to their corresponding edge routers
Step 3: If markers received from step 2, the edge routers periodically throttle back the rate of flow proportional to the maximum number of markers received for the flow because the goal is to decrease the rate in response to the bottleneck link

9. Incipient Congestion Detection
Detected by monitoring the length of packet queues
If at end of every epoch , then incipient congestion exists and markers are sent back
How many markers?
This is calculated by the formula

10. Marker Selection Weighted fair feedback
When incipient congestion is detected and the link decides to send back markers, it sends
Selective marker feedback
Motivated by CSFQ
Calculate running average of normalized packet transmission rate
Probability of marker selection
Deficit Variable
If marker selected with rn < ravg, then it is swapped with a future marker with rn > ravg

11. Simulations Network Topology
Three congested links
Flows have RTT from 240ms-400ms
Packet size = 1KB, queue size = 40 packets, links have BW = 4Mbps, latency = 2ms
Craig

12. Weighted Rate Fairness with Network Dynamics – 20 flows
1-5, 11-12, one congested link
6-8, 13-5 two congested links
9-10 three congested links
Instantaneous Rate
5, 15 – weight of 3
1, 11, 16 – weight of 1
All others – weight of 2

13. Corelite vs. CSFQ Corelite CSFQ
Flows 1, 2 have weight 1; 3, 4 have weight 2, etc.
Corelite converges faster because no packet drops since flows sending at a lower rate than their fair share don’t receive congestion notifications
CSFQ: simultaneous startup of many flows cause deviation from estimated fair rate because CSFQ can’t keep up with the changing fair rate -> if the share is underestimated, flows drop packets; if overestimated, extra packets are accepted causing queue buildups and potential overflows
Chris

14. Corelite vs. CSFQ with Network Dynamics
Chris
Corelite converges faster because:

15. Corelite vs. CSFQ with Network Dynamics
Chris
Corelite converges faster because: all flows move to the linear increase phase only after reaching a point close to their final rate, but CSFQ flows observe losses earlier in their lifetime (as described in the last simulation) resulting in slower convergence

16. Summary and Conclusions
Corelite provides per-flow rate contracts and weighted fair allocation of bandwidth without any per-flow state in the core
Corelite performs better than CSFQ when the fair share at the core router varies rapidly
Markers are used to
Normalize the rate of the flow according to its rate weight
Enable the core router to notify the edge routers in case of congestion

17. Questions ?