Corelite Architecture: Achieving Rated Weight Fairness Rigoberto Núñez Kevin Sinha

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

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

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

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

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.

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.

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

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

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

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

Weighted Rate Fairness with Network Dynamics – 20 flows 1-5, 11-12, 16-20 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

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

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

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

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

Questions ?