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Agenda (Rev 2)

Week 1: Background (Basic Concepts; Internet History)Week 2: Routing vs. SwitchingWeek 3: Architecture and Topology TrendsWeek 4: Performance of LANs and Routers -LadnerWeek 5: Congestion vs. Quality of ServiceWeek 6: Routing part 1 (Intro, RIP, OSPF) -CorbatoWeek 7: Routing part 2 (BGP, state of Internet) -CorbatoWeek 8: ATM; IP QoS -MinshallWeek 9: Failure Modes and Fault DiagnosisWeek 10: Product evaluation criteria; Wrap-up

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Week 5: Congestion vs. QoSCoping with Rush Hour on the Info Highway

• Part 1: The Performing Arts

• Part 2: Congestion Obsession

• Part 3: Better than “Best Effort”

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But first… it’s time for moreGray’s Networking Nuggets

• The key to high-performance (and world peace) is reducing contention for shared resources.

• Failing that, you need a fair method of divvying up the spoils.

• Speed, Quality, Efficiency: Pick two

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Part 1: The Performing Arts

• Performance implies speed and quality

• Achieving the promise of fast hardware...

• Need finesse as well as brute force

• Balancing speed, quality, and efficiency

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Performance Metrics

• Lost or corrupted packets

• Effective Throughput

• Delay (Latency)

• Jitter (Delay variation)

• Power = Throughput/Delay

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Delay vs. Bandwidth

• Total delay = prop’n delay + buffering delay

• Independent of link speed (bandwidth)

• Can trade bandwidth to reduce latency (cache)

• As bandwidth increases, RTTs dominate performance equation...

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Measuring Throughput

• Packets vs. Bytes per second

• Raw vs. Effective rate (Gross vs. Net) – with/without factoring retransmissions

• Steady state vs. per transmission or RTT– Effective Throughput = XferSize/XferTime– Transfer Time = Delay + (TransferSize/BW)

• If BW >> TSize, ET = TransferSize/Delay

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Burstiness• How to characterize? Max rate over what interval?

• Strong law of large numbers… doesn’t hold– flows may be correlated– but even uncorrelated flows appear to be fractal!

• MAC Fairness… doesn’t hold– capture effect and super-tokening

• Longer paths mean less randomness– “Bunching”– Higher probability of buffer overrun– Special case: ACK compression

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Performance Elements

• Client Machine/Software– Computer-to-Closet– Closet-to-BDF– BDF-to-Router– Router-to-Router

• Server Machine/Software

Network CoreEnd-EndSystem

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Policies vs. Performance (Jain)• Transport

– Retransmission– Out-of-order caching– Acknowledgement– Flow control– Timeout determination

• Network– Use of Virtual circuits vs. Datagrams– Packet queuing and service– Packet discard– routing algorithm– Packet lifetime

• Data Link– Retransmission– Out-of-order caching– Acknowledgement– Flow control

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Other Performance Factors…

• Use of unicast vs. multicast• Policy routing overhead• Topology (esp. speed mismatches)• VLANs• Application efficiency• Desktop O.S. (esp. regarding Jitter)

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Part 2: Congestion Obsession

• Goal:– Avoid dropping packets on the floor

• Alternatives:– Increase capacity– Decrease load– Congestion collapse

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Decreasing Offered Load

• Congestion avoidance (before it happens)– Admission Control– Traffic Shaping (Open or Closed Loop)

• Congestion control (after it happens)– Tell sender to shut-up (or slow down)– Drop packets

• Different methods for datagrams vs. VCs

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Not a new problem…

“In the course of designing this current protocol, we have come to understand that flow control is more complex than we imagined. We now believe that flow control techniques will be one of the active areas of concern as the network traffic increases.”– RFC 54 (June 18, 1970), S. Crocker et al.

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Flow vs. Congestion Control

• Flow control concerns one conversation– (unless we’re talking with switch vendors)– Tries to prevent buffer overrun in receiver

• Congestion control deals with network– Effect of multiple conversations– Tries to prevent buffer overrun in intermediate

systems

• Contention vs. Congestion

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Load Sheddingaka Discard Policy

• If you can’t get sender(s) to stop in time…need to throw some packets on the floor

• Which ones?– Random– Oldest first (video)– Newest first (files)

• Why not just have bigger queues??– Bigger buffers means more Latency– Nagle’s discovery: congestion w/infinite queues

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Queuing Disciplines(packet scheduling)

• First-In-First-Out (FIFO)– does not discriminate between traffic sources

• Fair Queuing (FQ)– explicitly segregates traffic based on flows– ensures no flow gets more than its share variation:

weighted fair queuing (WFQ)• Problem: packets not all the same length

– really want bit-by-bit round robin– not feasible to interleave bits (schedule on pkt basis)– simulate by determining when packet would finish

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Taxonomy #1 (Zhang)

• Router-centric vs. Host-centric

• Reservation-based vs. Feedback-based

• Window-based vs. Rate-based

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Taxonomy #2 (Yang & Reddy)

• Open Loop– Source action– Intermediate/Destination action

• Closed Loop– Implicit feedback– Explicit feedback

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Evaluation• Fairness• Applicability

– Datagram & VC?– Cooperating & Non-cooperating end-systems?

• Power (ratio of throughput to delay)

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Implicit Feedback

• Source notices congestion…by time out waiting for ACK

• Packets are rarely dropped,except in congestion

• Lost packet implies congestion– in a link, or switch, or both

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Explicit Feedback

• Source quench; choke packets, C-bit

• Per link or per flow?

• End-to-end or hop-by-hop?

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Congestion Avoidance

• Goal: slow sender(s) before buffer overrun

• Irony: some CA schemes involve droppingpackets intentionally

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Two Examples

• DECbit or C-bit– Switch/router monitors average queue length...– Above threshold, sets bit in header of each pkt– Source resets window based on % of pkts with C-bit– Requires cooperating transport layer

• RED = Random Early Detection– Switch/router monitors average queue length...– Above threshold, drop or mark random packets with

probability proportional to connection’s share of bandwidth

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TCP Flow/Congestion Control

• Idea– assumes best-effort network (FIFO or FQ routers)– each source determines network capacity for itself– uses implicit feedback– ACKs pace transmission (self-clocking)

• Problem: knowing when to time-out, retransmit– determining the available capacity in the first place– adjusting to changes in the available capacity

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TCP Performance vs. LossCurtis Villamizar

• Very high performance requires very low loss• Performance drops off above 1% loss• TCP can move bits under high loss, but

response may get very slow• TCP-dominated nets are *NOT* prone to

congestion collapse, but are more efficient with a smaller number of flows w/large windows than w/lots of smaller flows

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Layer Interactions

• Size of discard unit vs. retransmission unit

• TCP end-to-end congestion feedback vs. ATM hop-by-hop control

• MAC retransmission and LLC flow controlvs. upper-layer congestion control

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Part 3: Better than “Best Effort”Integrated Services & Bandwidth-on-Demand

• Hopes: Making little pipes from big pipes

• Promises: Class of Service (CoS)

• Guarantees: Quality of Service (QoS)– But at what cost?

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Making little pipes from big ones-Bandwidth sharing, but how dynamic?

• Per contract (e.g. two DS3s)

• Per device configuration (e.g. Frac. DS3)

• Per s/w configuration (e.g. PVC)

• Per VC setup (e.g. SVC)

• Per flow, changeable (e.g. ABR)

• Per packet, no guarantees (e.g. UBR)

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Switching Techniques redux

• Circuit (SDM or TDM or FDM)• Message (Store-and-forward)• Packet (Frame, FPS, Cell)

– Datagram: connectionless (no state)– Flow Switching (soft state)– Virtual Circuit: connection-oriented (hard state)

• Does TCP provide a virtual circuit??

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Real-Time Applications

• Cases where delayed packets are worthless

• Examples: audio, video

• S. Crocker: “All apps are real-time... There always exists some interval after which no one cares about the result.”

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QoS Application Examples

• Live/scheduled “Broadcast”/Multicast

• On Demand: Audio and/or Video

• Two people wanting ad hoc desktop conf

• N-person collaboration

• Real-time/High-bandwidth applications– Medical imaging– Virtual Reality

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QoS Goals

• Keep net-hostile apps from killing baseline services (e.g. telnet).

• Allow net-hostile apps to reserve what they need.

• Manage resource scarcity.

• Easy for end-users to live with.

• Easy for network managers to live with.

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QoS: How much is enough?

• No one really knows…

• Clearly it depends…

• So far, limited market for high QoS.– Guaranteed services are expensive– Administration issues are non-trivial– Many will opt for less expensive compromises

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QoS: There is no Free Lunch

• Deterministic/Guaranteed Behavior– Reserve BW for worst-case burstiness– Inefficient

• Probabilistic/Statistical– Reserve BW for typical burstiness– Be prepared for some packet loss

• Traffic Shaping and Admission Control– Essential for either case

• Do we need QoS if BW is adequate?

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QoS Elements/Strategies

• Flow Specification

• Traffic Shaping

• Routing

• Resource Reservation

• Admission Control

• Packet Scheduling/Discard

• Feedback

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Flow Specifications

• Characterize sender’s traffic pattern (Tspec)– e.g. average and peak rates, packet size

• Characterize requested network behavior (Rspec)– Delay– Delay variance– Packet loss

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Example Flow Spec (RFC1363)• Version• Max Transmission Unit• Token Bucket Rate• Token Bucket Size• Max Transmission Rate• Min Delay Noticed• Max Delay Variation• Loss Sensitivity• Burst loss sensitivity• Loss Interval• Quality of Guarantee

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Traffic Shaping• Goal: sender moderates burstiness

• Deterministic vs. Statistical

• Common Methods– Leaky Bucket (Turner)– Token Bucket --allow some burstiness

• Enforcement (Policing)– Intermediate systems monitor– Excessive packets may be discarded

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ATM Service Classes

• Constant Bit Rate (CBR)– Reserve BW for Peak Cell Rate (PCR)

• Variable Bit Rate (VBR)– Reserve for Sustainable Cell Rate (SCR)

• Available Bit Rate (ABR)– Reserve for Min Cell Rate (MCR)– Get more if RM cells say OK

• Unspecified Bit Rate (UBR) – aka “Best Effort” (no reservation)

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RSVP Service Classes• Guaranteed• Predictive Delay

– Net reports typical delay to application– Application adapts to reported delay

• Controlled Delay– Application requests bounds on delay

• Best Effort– “Ask me no questions & I’ll tell you no lies”

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RSVP Design Goals

• Allow heterogeneous receivers

• Adapt to changing mcast membership

• Net efficiency: exploit diffs in apps needs

• Allow receivers to switch channels

• Adapt to routing changes

• Keep overhead growth sublinear

• Modular design

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RSVP Design Principles

• Receiver-initiated reservations

• Separating reservation from pkt filtering

• Provide for different reservation styles

• Use soft-state in the network

• Protocol overhead control

• Modularity

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Alternative Transport: RTP

• UDP + new header

• RTP header:– Sequence number– timestamp– Synchronization Src packet ID

• Followed by add’l header, e.g. H.261

• Companion control protocol (RTCP)

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ATM “Flow” Control

• Huge debate in ATM Forum!

• Two choices:– Rate based– Credit based

• Rate-based won– Periodic Resource Manager (RM) cells

allow sender to increase rate if no congestion– Used for Available Bit Rate (ABR) service

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Peterson&Davie Comparison of RSVP & ATM

• Receiver initiates reservations

• Soft state (refresh/timeout)

• Separate from route setup

• QoS can change dynamically

• Receiver heterogeneity

• Sender initiates reservations

• Hard state (explicit delete)

• Concurrent w/route setup

• QoS static for life of connection

• Uniform QoS to all receivers

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Open QoS Issues• On-campus vs. off-campus: costs, mechanisms, capacity• Will RSVP scale?• Security: Authorization, Private conferences • What about net-hostile apps that don’t request high QoS?• Migration/interaction: tunnels, DVMRP, PIM, 802.1p• Privileges or quotas or recharge?• By user or by device?• Relationship to campus authorization & billing DBMS• How will NOC know when QoS is working?• Is there ANY hope for administering QoS?

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QoS Alternatives

• Over-provision best-effort infrastructure– Locally, may be cheaper than managing QoS!

• CoS: multiple queues, no guarantees

• Multiple best-effort services– Varying over-capacity– Varying price

• Combinations

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Questions?

• Next two weeks: routing• 5/22 More on QoS, ATM