Edge-based Traffic Management Building Blocks
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Transcript of Edge-based Traffic Management Building Blocks
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
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Edge-based Traffic Management Building Blocks
David Harrison, Shiv Kalyanaraman, Sthanu Ramakrishnan
Rensselaer Polytechnic Institute
http://www.ecse.rpi.edu/Homepages/shivkuma
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Logical FIFO
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
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Private Networks vs Public Networks QoS vs Congestion Control: the middle ground ?
Overlay Bandwidth Services: Key: deployment advantages A closed-loop QoS building block
Services: Better best-effort services, Assured services, Quasi-leased lines…
Overview
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Shivkumar KalyanaramanRensselaer Polytechnic Institute
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Motivation: Site-to-Site VPN Over a Multi-Provider Internetwork
International Linkor
International Linkor
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Private networks over Public networks Can we reduce (not eliminate !) coordination requirements
for QoS deployment? Tolerate heterogeneity Incremental deployment Faster deployment cycles Dynamically provisioned services
Complexity Issues: Design: int-serv, RSVP, RTP … Implementation: diff-serv, CSFQ… Upgrades Configuration Management
Focus of this talk!
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Problem: Inter-domain QoS Deployment Complexity
Solutions: Enable incrementally deployable edge-based QoS New closed-loop building blocks for efficiency Reduce (not eliminate!) coordination reqts. No upgrades!Tradeoffs: limited service spectrum
Today’s solutions require upgrade of multiple potential bottlenecks, and complex multi-provider coordination
Internetwork or WANWorkstationRouter
Router
RouterWorkstation
Router
Router
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Our Model: Edge-based building blocks
New: Closed-loop control !Policy/Bandwidth Broker
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Logical FIFO
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Model: Inspired by diff-serv; Aim: further interior simplification
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Closed-loop BB: Take-Home Ideas
FIFO
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Loops: differentiate service on an RTT-by-RTT basis using edge-based policy configuration.
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Priority/WFQ
Scheduler: differentiates service on a packet-by-packet basis
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Queuing Behavior: Without Closed-loop Control
End system
Bottleneckqueue
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Queuing Behavior: With Overlay Edge-Edge Control
edge devices
Results: efficient core operation, rate adaptation in O(RTT)
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Edge-based Performance Customization Key idea: bottlenecks consolidated at edges, closer to application => incorporate application-awareness in QoS
Eg: L4-L7 aware buffer management. For TCP traffic: dramatically reduce timeouts:
Do not drop retransmissions or small window packets
Potential: application-level QoS services, active networking, edge-based diff-serv PHB emulation etc
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Closed-loop Building Block Reqts
#1. Edge-to-edge overlay operation, #2. Robust stability #3. Bounded-buffer/zero-loss,
#4. Minimal configuration/upgrades + incremental deployment
#5. Rate-based operation: for bandwidth services
Not available in any congestion control scheme… Related work: NETBLT, TCP Vegas, Mo/Walrand, ATM
Rate/Credit approaches
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Overlay Control: Concepts
Load: = i ; Capacity: ; Output Rates: i At all times: >= i (single bottleneck)
During congestion epochs, set i < i, Eg: i = min{i , i} Single bottleneck: Reverse queue growth within 1 RTT
Key: detect congestion: a) purely at the edges => overlay technology b) detect in a loss-less manner!
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Implementation model
Overlay state: Edge-to-edge VL association at ingress and egress One token-bucket shaper (LBAP) per VL loop: Rate = i(t) ; burstiness:
Ingress Edge(Shapes edge-to-edge aggregate at rate i)
Egress Edge (feeds back measured rate i during congestion epochs)
Interior Node(modeled to identify congestion epochs)
i(t)
Ingress shaper
End-to-end traffic
ri(t)
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Congestion Detection: Hypothetical Model
Mark all packets if the interior queue crosses N N is upper bound on transient burstiness during underload (I.e.
when i < )
If any marked packets seen by egress edge during measurement interval , i is fed back: begin epoch
If marked packets are not seen for a full interval , declare end of epoch and stop feedback of i.
Ingress Edge(Shapes edge-to-edge aggregateat rate i)
Egress Edge (feeds back measured rate i during congestion epochs)
Interior Node(helps identify congestion epochs)
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Impln: Overlay Congestion Detection Emulate prior model, albeit without Interior assist
N: aggregate burstiness bound : per-VL accumulation bound.
Congestion epoch beginning: Measure per-VL accumulation qi = (i i) Per-VL accumulation qi exceeds 2 => epoch begins
Congestion epoch end: qi <= epoch ends Hysteresis helps ensure that queue drains
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Increase/Decrease Dynamics Increase:
Additive increase of 1 pkt/ every interval ( >= RTT) Decrease:
i = min{i, i } every interval during the congestion epoch Properties (single bottleneck):
queue guaranteed to reduce within of feedback The lower rate is held till queue is drained
Input Rate Dynamics
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can be set larger than 0.5
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Bottleneck { j }, Q ueue = q, Q ueue contribution of flow i = q
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i F low { i}, Accum ulation = a , Input R ate = , O utput R ate =i i
D elay
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Multi-bottleneck stability
Incremental drain provisioned for incremental accumulation Sum of input rates upper bounded; output rates lower bounded
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Multiple Bottleneck FairnessThroughput versus Number of Bottlenecks
Linear Network: 1 flow (VL) crosses k bottlenecks. Each bottleneck has 4 cross flows (VLs).
Min-potentialdelay fairness
Proportionalfairness
Edge-to-edgeControl
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Overlay Bandwidth Services
Basic Services: no admission control “Better” best-effort services Denial-of-service attack isolation support Weighted proportional/priority services
Advanced services: edge-based admission control Assured service emulation “Quasi-leased-line” service
Key: no upgrades; only configuration reqts…
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Scalable Best-effort TCP Service
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Isolation of Denial of Service/Flooding
TCP starting at 0.0s UDP flood starting at 5.0s
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Backoff Differentiation Policy:
Backoff little (as) when below assurance (a), Backoff (as) same as best effort when above assurance (a) Backoff differentiation quicker than increase differentiation
Service could be potentially oversubscribed (like frame-relay) Unsatisfied assurances just use heavier weight.
Edge-based Assured Service Emulation
1 > AS >BE >> 0
r =r + min(r, AS aa
if no congestion
if congestion
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Bandwidth Assurances
Flow 1 with 4 Mbps assured + 3 Mbps best effort
Flow 2 with 3 Mbps best effort
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Assume admission control and route-pinning (MPLS LSPs). Provide bandwidth guarantee. Key: No delay or jitter guarantees!
Adaptation in O(RTT) timescales Average delay can be managed by limiting total and per-
VL allocations (managed delay) Policy:
Quasi-Leased Line (QLL)
1 > BE >> 0
r =r + if no congestion
if congestionmax(aaa
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Quasi-Leased Line Example
Background QLL starts with rate 50Mbps
Best-effort VL quickly adapts to new rate.
Best-effort rate limit versus time
Best-effort VL starts at t=0 and fully utilizes 100 Mbps bottleneck.
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Quasi-Leased Line Example (cont)
Bottleneck queue versus time
Starting QLL incurs backlog.
Unlike TCP, VL traffic trunks backoff without requiring loss and without bottleneck assistance.
Requires more buffers: larger max queue
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Quasi-Leased Line (cont.)
Worst-case queue vs Fraction of capacity for QLLs
Single bottleneck analysis:
q < b
1-bB/w-delay products
For b=.5, q=1 bw-rtt
Simulated QLL w/edge-to-edge control.
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Signaling/Configuration Issues Simple: Each edge-box independently sets up loops only with
other edges it intends to communicate Address-prefix list based configuration for VPN application Minimal overhead to maintain the loop: a leaky bucket, 8-
bytes every 250 ms or so of overhead
ISP configures ONE separate class at potential bottlenecks for overlay controlled traffic
Scalable to inter-domain VPNs as long as each edge does not have to manage > 100s of loops
Properties: Bounded scalability, simplified interior configuration, incremental deployment, simple set of overlay services.
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Edge-to-Edge Principle ? Tradeoff between public and private network philosophies: Private network characteristics:
Differentiated Svcs, simple forms of overlay QoS Bounded scalability and heterogeneity
Edge-to-edge loops, queue bounds, policy/BB scalability, bridging approach to inter-domain QoS
Public network characteristics: Incremental deployment. O(1) complexity. Stateless interior inter-network Minimal interior upgrades, configuration support. Use of robust, stable closed-loop control for efficiency and
adaptation in O(RTT) timescales.
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Current Work With bottlenecks consolidated at the edge:
What diff-serv PHBs or remote scheduler functionalities can be emulated from the edge ?
What is the impact of congestion control properties and rate of convergence on attainable set of services ?
Areas: Application-level QoS: edge-to-end problem Dynamic (short-term) services Congestion-sensitive pricing: congestion info at the edge
Edge-based contracting/bidding frameworks Point-to-set svcs: more economic value than pt-to-pt svcs
Dynamic provisioning for statistical muxing gains
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Summary
Private Networks vs Public Networks QoS vs Congestion Control vs Throwing bandwidth
QoS DeploymentL Simplified overlay QoS architecture
Intangibles: deployment, configuration advantages Edge-based Building Blocks & Overlay services:
A closed-loop QoS building block Basic services, advanced services