cs 268: lecture 17 (dynamic packet state) ion stoica april 15, 2002
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CS 268: Lecture 17(Dynamic Packet State)
Ion Stoica
April 15, 2002
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What is the Problem?
Internet has limited resources and management capabilities- Prone to congestion, and denial of service
- Cannot provide guarantees
Existing solutions- Stateless – scalable and robust, but weak network services
- Stateful – powerful services, but much less scalable and robust
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Stateless vs. Stateful Solutions
Stateless solutions – routers maintain no fine grained state about traffic scalable, robust weak services
Stateful solutions – routers maintain per-flow state powerful services
• guaranteed services + high resource utilization
• fine grained differentiation
• protection much less scalable and robust
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Existing Solutions
Stateful Stateless
QoS
Tenet [Ferrari & Verma ’89] Intserv [Clark et al ’91] ATM [late ’80s]
Diffserv - [Clark & Wroclawski ‘97] - [Nichols et al ’97]
Network support for congestion control
Round Robin [Nagle ’85] Fair Queueing [Demers et al ’89] Flow Random Early Drop (FRED) [Lin & Morris ’97]
DecBit [Ramkrishnan & Jain ’88] Random Early Detection (RED) [Floyd & Jacobson ’93] BLUE [Feng et al ’99]
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Stateful Solution: Guaranteed Services
SenderReceiver
Achieve per-flow bandwidth and delay guarantees- Example: guarantee 1MBps and < 100 ms delay to a flow
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Stateful Solution: Guaranteed Services
SenderReceiver
Allocate resources - perform per-flow admission control
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SenderReceiver
Challenge: maintain per-flow state consistent
Stateful Solution: Guaranteed Services
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Stateful Solution: Guaranteed Services
SenderReceiver
Per-flow buffer management
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Stateful Solution Complexity
Data path- Per-flow classification
- Per-flow buffer
management
- Per-flow scheduling
Control path- install and maintain
per-flow state for
data and control pathsClassifier
Buffermanagement
Scheduler
flow 1
flow 2
flow n
output interface
…
Per-flow State
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Stateless vs. Stateful
Stateless solutions are more- scalable
- robust
Stateful solutions provide more powerful and flexible services
- guaranteed services + high resource utilization
- fine grained differentiation
- protection
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Question
Can we achieve the best of two worlds, i.e., provide services implemented by stateful networks while maintaining advantages of stateless architectures?
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Answer
Yes, at least in some interesting cases:- guaranteed services [Stoica and Zhang, SIGCOMM’99]
- network support for congestion control: Core-Stateless Fair Queueing [Stoica et al, SIGCOMM’98]
- service differentiation [Stoica and Zhang, NOSSDAV’98]
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Solution: SCORE architecture and DPS technique Example: providing guaranteed services Conclusions
Outline
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Scalable Core (SCORE)
A trusted and contiguous region of network in which - edge nodes – perform per flow management
- core nodes – do not perform per flow management
core nodes edge nodesedge nodes
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The Approach
1. Define a reference stateful network that implements the desired service
Reference Stateful Network
SCORE Network
2. Emulate the functionality of the reference network in a SCORE network
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The Idea
Instead of having core routers maintaining per-flow state have packets carry per-flow state
Reference Stateful Network SCORE Network
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The Technique: Dynamic Packet State (DPS)
Ingress node: compute and insert flow state in packet’s header
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The Technique: Dynamic Packet State (DPS)
Ingress node: compute and insert flow state in packet’s header
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The Technique: Dynamic Packet State (DPS)
Core node: - process packet based on state it carries and node’s state
- update both packet and node’s state
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The Technique: Dynamic Packet State (DPS)
Egress node: remove state from packet’s header
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Solution: SCORE architecture and DPS technique Example: providing guaranteed services Conclusions
Outline
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Why Guaranteed Service Example?
Illustrate power and flexibility of our solution- guaranteed service - strongest semantic service proposed in
context of stateful networks
guaranteedservices
statisticalservices
differentiatedservices
congestioncontrol supportbest-effort
service quality betterworse
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Example: Guaranteed Services
Goal: provide per-flow delay and bandwidth guarantees How: emulate ideal model in which each flow traverses
dedicated links of capacity r
Per-hop packet service time = (packet length) / r
r r rflow(reservation = r )
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Guaranteed Services
Define reference network to implement service - control path: per-flow admission control, reserve capacity r on each
link
- data path: enforce ideal model, by using Jitter Virtual Clock (Jitter-VC) scheduler
Reference Stateful Network
Jitter-VC Jitter-VC Jitter-VCJitter-VC
Jitter-VCJitter-VCJitter-VC
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Guaranteed Services
Use DPS to eliminate per-flow state in core - control path: emulate per-flow admission control
- data path: emulate Jitter-VC by Core-Jitter Virtual Clock (CJVC)
Reference Stateful Network SCORE Network
Jitter-VC Jitter-VC Jitter-VCJitter-VC
Jitter-VCJitter-VCJitter-VC
CJVCCJVC
CJVC
CJVC
CJVC
CJVC
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Solution: SCORE architecture and DPS technique Example: providing guaranteed services
Eliminate per-flow state on data path
- Eliminate per-flow state on control path
- Implementation and experimental results
Conclusions
Outline
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Data Path
Ideal Model
Stateful solution: Jitter Virtual Clock
Stateless solution: Core-Jitter Virtual Clock
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Ideal Model: Example
time
1
2
3
4
packet arrival timepacket transmission time (service) in ideal model
p1 arrival p2 arrival
length(p2) / rlength(p1) / r
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Stateful Solution: Jitter Virtual Clock (Jitter-VC)
time
• With each packet associate – eligible time – start time of serving packet in ideal model– deadline – finish time of serving packet in ideal model
eligible times
deadlines
1
2
3
4
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Jitter-VC
time
• Algorithm: schedule eligible packets in increasing order of their deadlines
• Property: guarantees that all packets meet their deadlines
eligible times
deadlines
1
2
3
4
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Jitter-VC: Eligible Time Computation
Minimum between- arrival time
- deadline at previous node + propagation delay
- deadline of previous packet
timeeligible time = packet deadline at previous node
eligible time = arrival time
1
2
3
4
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Jitter-VC: Eligible Time Computation
time
eligible time = arrival time
eligible time = packet deadline at prev. node
eligible time = prev.packet deadline
using previous packet’s deadline per flow state
Minimum between- arrival time
- deadline at previous node + propagation delay
- deadline of previous packet
1
2
3
4
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Stateless Solution: Core-Jitter Virtual Clock (CJVC)
time
Goal: eliminate per-flow state- eliminate dependency on previous packet deadline
1
2
3
4
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Core-Jitter Virtual Clock (CJVC)
Solution: make eligible time greater or equal to previous packet deadline
time
1
2
3
4
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Core-Jitter Virtual Clock (CJVC)
time
How: associate to each packet a slack variable s Delay eligible time at each node by s
1
2
3
4
s
eligible time = packet deadline at prev. node + s
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Theorem: CJVC and Jitter-VC provide the same end-to-end delay bounds
s can be computed at ingress: depends on- current and previous packet eligible times (e and ep)
- current and previous packet lengths (lp and l)
- slack variable associated to previous packet (sp)
- flow reservation (r)
- number of hops (h) – computed at admission time
CJVC Properties
1
/,0max
h
rlee
r
llss ppp
p
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CJVC Algorithm
Each packet carries in its header three variable- slack variable s (computed and inserted by ingress)
- flow’s reserved rate r (inserted by ingress)
- ahead of schedule a (inserted by previous node)
Eligible time = arrival time + a + s Deadline = eligible time + (packet length) / r NOTE:
- using a instead of the deadline at previous node no need for synchronized clocks
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Jitter-VC: Core Router
Data path- Per-flow classification
- Per-flow buffer
management
- Per-flow scheduling
Control path- install and maintain
per-flow state for
data and control pathsClassifier
Buffermanagement
Scheduler
flow 1
flow 2
flow n
…
Per flowl State
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Data path- Per-flow classification
- Per-flow buffer
management
- Per-packet scheduling
Control path- Install and maintain
per-flow state for
data and control paths
CJVC: Core Router
Buffermanagement
Scheduler
…
Control State
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Motivations: what is the problem and why it is important? Existing solutions Solution: SCORE architecture and DPS technique Example: providing guaranteed services
- Eliminate per-flow state on data pathEliminate per-flow state on control path
- Implementation and experimental results
Conclusions
Outline
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Control Path: Admission Control
Goal: reserve resources (bandwidth) for each flow along its path
Approach: light-weight protocol that does not require core nodes to maintain per-flow state
yes yes yes
yes
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Per-hop Admission Control
A node admits a reservation r, if - C – output link capacity
- R – aggregate reservation:
Need: maintain aggregate reservation R Problem: it requires per flow state to handle partial
reservation failures and message loss
i
irR
RCr
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Solution
1. Estimate aggregate reservation Rest
2. Account for approximations and compute an upper bound Rbound , i.e., Rbound >= R
3. Use Rbound , instead of R, to perform admission control, i.e., admit a reservation r if
boundRCr
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Observation: If all flows were sending at their reserved rates, computing Rest is trivial:
- just measure the traffic throughput, e.g.,
where S(a, a+T) contains all packets of all flows received during [a, a+T)
Estimating Aggregate Reservation (Rest)
T
ilength
R TaaSiest
),(
)(
Mbpsr 21
Mbpsr 32
MbpsRest 5
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Virtual Length
• Problem: What if flows do not send at their reserved rates ?• Solution: associate to each packet a virtual length such that
– if lengths of all packets of a flow were equal to their virtual lengths, the flow sends at its reserved rate
• Then, use virtual lengths instead of actual packet lengths to compute Rest
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Virtual Length
Definition:
- r – flow reserved rate
- crt_time – transmission time of current packet
- prev_time – transmission time of previous packet
Example: assume a flow with reservation r = 1 Mbps sending 1000 bit packets
)__( timeprevtimecrtrgthvirtualLen
100012001300
1000
1.2 ms
1000
1.3 ms
1000 length
1900
1.9 ms
1000
virtuallength
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Estimating Aggregate Reservation (Rest)
Use Dynamic Packet State (DPS) Ingress node: upon each packet departure computes the
virtual length and inserts it in the packet header Core node: Estimate Rest on each output link as
- where S(a, a+T) contains of all packets of all flows received during [a, a+T)
T
igthvirtualLen
R TaaSiest
),(
)(
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Aggregate Reservation Estimation: Discussion
The estimation algorithm is robust in presence of control message loss and duplication
- their effect is “forgotten” after one estimation interval
If no packet of a flow departs during a predefined interval (i.e., maximum inter-departure time), ingress node generates a dummy packet
Utilization <= 1 – f ,- where f = (max. inter-departure time) / (estimation int.)
- e.g.: max. inter-departure time = 5s; estimation int. = 30s utilization <= 0.83
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Data path- Per-flow classification
- Per-flow buffer
management
- Per-packet scheduling
Control path- Install and maintain
per flow state for
data and control paths
Core Router
Buffermanagement
Scheduler
…
Control State
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Data path- Per-flow classification
- Per-flow buffer
management
- Per-packet scheduling
Control path- Install and maintain
per flow state for
data and control paths
Core Router
Buffermanagement
Scheduler
Control State
no need to maintain consistency of per-flow state
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Motivations: what is the problem and why is it important? Existing solutions Solution: SCORE architecture and DPS technique Example: providing guaranteed services
- Eliminate per-flow state on data path
- Eliminate per-flow state on control pathImplementation and experimental results
Conclusions
Outline
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Implementation: State Encoding
Problem: Where to insert the state ? Possible solutions:
- between link layer and network layer headers
- as an IP option (IP option 23 allocated by IANA)
- find room in IP header
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Implementation: State Encoding
Current solution- 4 bits in DS field (belong to former TOS)
- 13 bits by reusing fragment offset
Encoding techniques- Take advantage of implicit dependencies between state
values
- Temporal multiplexing: use one field to encode two states, if these states do not need to be simultaneously presented in each packet
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Implementation
FreeBSD 2.2.6 Pentium II 400 MHz ZNYX network cards 10/100 Mbps Ethernet Fully implements control and data path functionalities Management and monitoring infrastructure
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Monitoring Infrastructure
Light weight mechanism that allows continuous monitoring at packet level
Implementation- Record each packet (28 bytes)
• IP header and port numbers
• arrival, departure or drop times
- Use raw IP to send this information to a monitoring site
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A Simple Experiment Three flows sharing a 10 Mbps link
- Flow 1: 1 Mbps reservation
- Flow 2: 3 Mbps reservation with ON/OFF traffic
- Flow 3: best-effort UDP sending at > 8 Mbps
aruba(ingress)
cozumel(core)
Monitoringmachine
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Aggregate Reservation Computation
0.5 Mbps reservation active during entire interval 0.5 Mbps reservation starting at 18 sec; ending at 39 sec
0
0.2
0.4
0.6
0.8
1
1.2
15 25 35 45Time (sec)
Rat
e (M
bp
s)
Aggregate
Rbound
R
acceptreservation(0.5 Mbps)
terminatereservation(0.5 Mbps)
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Conclusions Propose a network architecture (SCORE) and a technique
(DPS) that bridge longstanding gap between stateless and stateful solutions
Key ideas- Instead of core routers maintain per-flow state have packets carry this
state
- Use state to coordinate edge and core router actions
Reference Stateful Network
SCORE Network
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Conclusions
Develop first scalable solutions to provide: - Service guarantees
- Network support for congestion control
- Service differentiation
DPS compatible with Diffserv: can greatly enhances the functionality while requiring minimal changes