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Scalable Network Architectures for Providing Per-flow Service Guarantees Jasleen Kaur

Scalable Network Architectures for Providing Per-flow Service Guarantees Jasleen Kaur Department of Computer Science University of North Carolina at Chapel Hill. The trend: richer network services. Basic Internet service providing is commoditized Last decade: network connectivity

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Scalable Network Architectures for Providing Per-flow Service Guarantees Jasleen Kaur

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  1. Scalable Network Architectures for Providing Per-flow Service Guarantees Jasleen Kaur Department of Computer Science University of North Carolina at Chapel Hill

  2. The trend: richer network services • Basic Internet service providing is commoditized • Last decade: network connectivity • Next decade: value-added services • Value-added services Quality of Service, Virtual Private Networks, Intrusion detection, Transcoding services Focus: providing QoS guarantees in networks

  3. The opportunity: QoS • New applications with stringent timeliness requirements • Live and on-demand video streaming, real-time stock quote • VPNs for mission-critical enterprise applications • Requirements • Delay guarantees: upper bound on network delay • Throughput guarantees: sustained throughput even at short time-scales • Fairness guarantees: throughput in proportion to reserved rate Need to provide per-flow network service guarantees

  4. The challenge: growth • Link capacities are increasing rapidly (double every year) • Less time available to routers for per-packet processing Internet traffic demands are increasing at similar rate • Requirements • Minimize # of instructions, memory accesses, amount of memory • Utilize resources efficiently Networks need to be scalable and efficient

  5. Requirements summary A network architecture should: • Provide per-flow guarantees on delay, throughput, fairness • Scale to high capacity links • Use efficiently available resources Design network architectures that meet these requirements

  6. Outline • State of the art • Research directions and methodology • Core-stateless Guaranteed Services networks • Scalability evaluation • Summary • Current research directions

  7. Routers Link Scheduler Input links Outgoing link Packet Queue Network model

  8. State of the art • FIFO networks + Are simple and scalable - Do not provide service guarantees in presence of bursty traffic • Integrated Services (IntServ) networks [Shenker95] + Provide per-flow guarantees: use sophisticated scheduling algorithms - Do not scale: require per-flow state and packet classification • Differentiated Services (DiffServ) networks [Nichols97] + Are scalable: only per-aggregate processing in core routers - Do not provide per-flow guarantees within an aggregate

  9. Two research directions • Can scalable mechanisms be added to enable FIFO networks to provide per-flow service guarantees? • Can complexity of IntServ mechanisms be eliminated, while retaining per-flow guarantees? Performance of FIFO networks with CBR traffic-shaping [NOSSDAV-99] • Analytical model: heavy-tails at high utilization in large-scale networks • Simulations: heavy-tails even at moderate utilization and small networks Network architectures that provide per-flow service guarantees without maintaining or using per-flow state in core routers

  10. Core-stateless networks • Core routers do not maintain per-flow state • Scalable: no state maintenance or classification complexity • Edge routers maintain state • Scalable: small number of flows and low-speed links Edge Routers Core Routers

  11. Work-conserving core-stateless networks that provide deterministic guarantees similar to core-stateful networks Core-stateless schemes Type of service guarantees in core-stateless schemes Statistical Deterministic • CSFQ [Stoica98], RFQ [Cao00], • CHOKe [Pan00], TUF [Clerget01] • Approximate fairness over long time-scales • No guarantees for short-lived flows • CJVC [Stoica99] • End-to-end delay guarantees • Non work-conserving

  12. Research methodology Careful blend of theory and practice • Theory • Understand end-to-end guarantees in core-stateful networks • Design core-stateless networks to provide similar guarantees First tight lower bound on end-to-end fairness Exactly samedelay guarantees Throughputguarantees within an additive constant Fairness guarantees even better • Practice Design, implement and evaluate • Scalability of edge and core routers • Feasibility of deploying the core-stateless network

  13. Delay guarantees are fundamental Theorem 1: (throughput  delay) A network that provides throughput guarantees also provides delay guarantees Theorem 2: (fairness  throughput) A network that provides fairness guarantees also provides throughput guarantees A network that does not provide delay guarantees, can not provide throughput or fairness guarantees

  14. Guaranteed Rate (GR) scheduling algorithms • GR Algorithms • Class of algorithms that provide delay guarantees to flows • Basic operation • Reserve a rate for each flow • Associate with packet k, a Guaranteed Rate Clock GRC(k) value • GRC(k): Transmission deadline for packet based on reserved rate • Scheduling algorithm belongs to class GR if it guarantees transmission of packet k by GRC(k) +  • Examples: • Virtual Clock, Delay-EDD, SCFQ, SFQ, WF2Q+, …

  15. Virtual Clock: need for per-flow state • Assign a transmission deadline (VC) to packet k: EAT(k) = max{ VC(k-1), AT(k) } VC(k) = EAT(k) + lk/r Transmit packets in increasing order of their VC values • If flow r  C, packet gets transmitted by VC(k) + lmax/C • End-to-end delay bound = f(upper bound on VC(k) at last node) Delay bound = f(upper bound on transmission deadline) Transmission deadline of packet k = f(state of packet k-1)  Need to maintain state of previous packet! How to compute deadlines without maintaining state?

  16. Upper bounds on deadline at any node = f (deadline of same packet at previous node) . . . = f (deadline of same packet at first node) 1 2 Core routers Ingress router Key insight • Ingress router does maintain per-flow state  can compute upper bounds on deadlines for all nodes Using upper bounds on deadlines results in same network delay guarantee

  17. Computes deadlines Sorts and transmits packet Sorts and transmits packets Core-stateless Guaranteed Rate networks • Ingress router maintains per-flow state • Computes and encodes deadlines for all nodes • Core routers do not maintain per-flow state • Use deadline encoded by ingress router 1 2 Core routers Ingress router

  18. CSGR: properties Theorem: End-to-end delay guarantee of a CSGR network is same as corresponding GR network • Salient features: • Methodology for deriving core-stateless version of any GR network • Leads to design of work-conserving core-stateless networks • Core-stateless Delay-EDD: decouples delay and rate guarantees • Same bound on end-to-end delay as core-stateful version • Simple computations • Caveat: • Do not preserve short time-scale throughput or fairness guarantees Flows that use idle capacity to send at more than their reserved rate accumulate “debit” and may be penalized in the future !

  19. CSGS networks: properties • CSGR [Infocom-01]: Delay • Provide exactly same delay guarantees as core-stateful networks • CSGT [Infocom-03]: Throughput • Provide throughput guarantees within an additive constant of core-stateful networks • First work-conserving core-stateless network that provides deterministic throughput guarantees • CSGF [IWQoS-03]: Fairness • Provide better fairness guarantees than core-stateful networks • First work-conserving core-stateless network that provides deterministic fairness guarantees

  20. Research methodology Careful blend of theory and practice • Theory • Understand end-to-end guarantees in core-stateful networks • Design core-stateless networks to provide similar guarantees First tight lower bound on end-to-end fairness Exactly same delay guarantees Throughput guarantees within an additive constant Fairness guarantees even better • Practice Design, implement and evaluate • Scalability of edge and core routers • Feasibility of deploying the core-stateless network

  21. Scalability evaluation of network architectures • Constraints in high-speed routers • Time: Per-packet processing time budget is limited • Space: Total fast-path memory is limited • Key question: What are the performance limits of routers in different network architectures? Specific values depend on router platform ! Our Approach: Implement a CSGS, FIFO, and IntServ router on common platform and measure relative performance

  22. Router throughput in different architectures Source routing + core-stateless architecture A network architecture that provides end-to-end per-flow service guarantees with scalability close to conventional IP routers

  23. Summary • Goal: design network architectures that provide per-flow guarantees, are scalable, and efficient • FIFO inadequate if premium traffic occupies a large fraction of capacity [NOSSDAV-99] • Core-stateless networks: theory • First end-to-end fairness analysis of fair queuing networks [RTSS-02] • Design of core-stateless networks Exactly same delay guarantees [Infocom-01] Throughput guarantees within a constant [Infocom-03] Fairness guarantees even better [IWQoS-03] • Core-stateless networks: practice • Routers in core-stateless networks, with source routing, have performance similar to conventional IP routers

  24. Some challenges and open questions • CSGS networks still require modifications to all routers Is it possible to provide end-to-end service guarantees using mechanisms instantiated only at the edges of a network? • [Zhang-Sigcomm02]: Throughput of many TCP flows is limited due to default parameter settings ! How suitable for today’s Internet are traditional end-host mechanisms for flow control? • Does congestion occur at all? If so, where does it occur? At end-hosts? At the edge? At the core?

  25. Variability in TCP round-trip times Max, median, and min RTTs may differ by several orders of magnitude within individual TCP connections !!

  26. Current research directions • Detecting congestion • Where does congestion occur? • What mechanisms help detect it quickly and non-intrusively? • How to design a large-scale, distributed congestion-monitoring infrastructure? • Designing edge-based services • Designing end-host flow control mechanisms • Efficacy of overlay-based alternate path routing • Availability of ‘‘parallel’’ bandwidth • Does the ‘‘single-bottleneck’’ assumption hold? • Does traditional flow control work well in high bandwidth networks?

  27. More details being made available at… URL: http://www.cs.unc.edu/~jasleen/ Email: jasleen@cs.unc.edu

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