scalable network architectures for providing per-flow service guarantees jasleen kaur
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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 - PowerPoint PPT PresentationTRANSCRIPT
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Scalable Network Architectures for Providing
Per-flow Service Guarantees
Jasleen Kaur
Department of Computer ScienceUniversity of North Carolina at Chapel Hill
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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 networksFocus: providing QoS guarantees in networks
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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
Need to provide per-flow network service guaranteesNeed to provide per-flow network service guarantees
Delay guarantees: upper bound on network delay Throughput guarantees: sustained throughput even at
short time-scales Fairness guarantees: throughput in proportion to reserved
rate
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The challenge: growth
Link capacities are increasing rapidly (double every year) Less time available to routers for per-packet processing
Networks need to be scalable and efficientNetworks need to be scalable and efficient
Capacity Per-packet Time
100 Mbps Ethernet
38 s
2.45 Gbps (OC48) 1.5 s
9.6 Gbps (OC192) 0.38 sInternet traffic demands are increasing at similar rate
Requirements Minimize # of instructions, memory accesses, amount of
memory Utilize resources efficiently
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Requirements summary
A network architecture should:
1. Provide per-flow guarantees on delay, throughput, fairness
2. Scale to high capacity links
3. Use efficiently available resources
Design network architectures that meet these requirementsDesign network architectures that meet these requirements
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Outline
State of the art
Research directions and methodology
Core-stateless Guaranteed Services networks
Scalability evaluation
Summary
Current research directions
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Network model
Routers
Outgoinglink
LinkScheduler
Inputlinks
PacketQueue
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State of the artFIFO networks + Are simple and scalable - Do not provide service guarantees in presence of bursty traffic
Architecture
Per-flow Guarantees
Scalability
Efficiency
FIFO X X
DiffServ X X
IntServ X X
Integrated Services (IntServ) networks [Shenker95] + Provide per-flow guarantees: use sophisticated scheduling
algorithms - Do not scale: require per-flow state and packet classificationDifferentiated Services (DiffServ) networks [Nichols97] + Are scalable: only per-aggregate processing in core routers - Do not provide per-flow guarantees within an aggregate
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Two research directions
1. Can scalable mechanisms be added to enable FIFO networks to provide per-flow service guarantees?
2. 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 networksSimulations: 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
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Core-stateless networksCore 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
Core Routers
Edge Routers
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Core-stateless schemes
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
Type of service guaranteesin core-stateless schemes
Type of service guaranteesin core-stateless schemes
StatisticalStatistical DeterministicDeterministic
Work-conserving core-stateless networks that provide deterministic guarantees similar to core-stateful networks
Work-conserving core-stateless networks that provide deterministic guarantees similar to core-stateful networks
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Theory1. Understand end-to-end guarantees in core-stateful
networks
2. Design core-stateless networks to provide similar guarantees
Research methodology
First tight lower bound on end-to-end fairness
Exactly same delay guaranteesThroughput guarantees within an additive
constantFairness guarantees even better
Practice Design, implement and evaluate
Scalability of edge and core routers Feasibility of deploying the core-stateless network
Careful blend of theory and practice
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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
A network that does not provide delay guarantees,can not provide throughput or fairness guarantees
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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+, …
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Virtual Clock: need for per-flow stateAssign 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)
Transmission deadline of packet k = f(state of packet k-1) Need to maintain state of previous packet!
Delay bound = f(upper bound on transmission deadline)
How to compute deadlines without maintaining state?How to compute deadlines without maintaining state?
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Key insight
Ingress router does maintain per-flow state can compute upper bounds on deadlines for all nodes
Ingressrouter
21 Core routers
Upper bounds on deadline at any node = f (deadline of same packet at
previous node)
= f (deadline of same packet at first node)
...
Using upper bounds on deadlines results in same network delay guarantee
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Core-stateless Guaranteed Rate networks
Ingressrouter
21Core routers
Computes deadlinesSorts and transmits packetSorts and transmits packets
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
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CSGR: properties
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
guaranteesFlows that use idle capacity to send at more than their reserved rate accumulate “debit” and may be penalized in the future !
Theorem:End-to-end delay guarantee of a CSGR network is same as corresponding GR network
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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
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Theory1. Understand end-to-end guarantees in core-stateful
networks
2. Design core-stateless networks to provide similar guarantees
Research methodology
First tight lower bound on end-to-end fairness
Exactly same delay guaranteesThroughput guarantees within an additive
constantFairness guarantees even better
Practice Design, implement and evaluate
Scalability of edge and core routers Feasibility of deploying the core-stateless network
Careful blend of theory and practice
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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
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Router throughput in different architectures
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0.2
0.4
0.6
0.8
1
1.2
FIFO/IP CSGS/src CSGS/IP IntServ/src
Worst
Best
Source routing + core-stateless architecture A network architecture that provides end-to-end per-flow service guarantees
with scalability close to conventional IP routers
Source routing + core-stateless architecture A network architecture that provides end-to-end per-flow service guarantees
with scalability close to conventional IP routers
0
0.2
0.4
0.6
0.8
1
1.2
CSGS/src
Worst
Best
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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
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Some challenges and open questions
CSGS networks still require modifications to all routersIs 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?
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Variability in TCP round-trip times
Max, median, and min RTTs may differ by several orders of magnitude within individual TCP connections !!
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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?
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More details being made available at…
URL: http://www.cs.unc.edu/~jasleen/
Email: [email protected]