Post-Internet
QoS Research
Jorg Liebeherr
Department of Computer Science
University of Virginia
IWQoS 2004 Panel
.
QoS Venues: First there was one …
IWQoS 96
Paris
IWQoS 95
Brisbane
IWQoS 94
Aachen
IWQoS
IWQoS04
94
Montreal
Montreal
IWQoS 03
Monterey
.
IWQoS 97
New York
IWQoS 98
Napa
QoS-IP 2005
Catania
QofIS’04
Barcelona
COQODS 04
Singapore
QShine 2004
Dallas
QofIS’03 QoS-IP 2003
Stockholm Milan
IWQoS 02
Miami
Beach
QofIS’02
Coibmra QoS-IP 2001 Rome
QofIS’01 Berlin
IWQoS 01 Karlsruhe
.. now there
are several
IWQoS 99
London
IWQoS 00
Pittsburgh
While QoS research has thrived …
• Many fundamental advances:
– Much improved understanding of packet
scheduling
– A new theory: network calculus
– Numerous proof-of-concept implementations
of even the most difficult systems problems
.
… QoS deployment has failed
• No QoS approach seems to be able to take hold:
–
–
–
–
.
Connection-oriented (ATM)
Flow-based reservation (IntServ)
Class-based differentiation (DiffServ)
Overlay approach (Service overlays)
Possible Reasons for Lack of QoS Deployment
• No applications/business cases
– VoD used to be the driver
• Botched standardization efforts
– Ignored analytical aspects of QoS service
– Problems in control path addressed late (e.g, policy)
• Naive implementations
– Software-only realization of QoS scheduling in core
routers
• No need for QoS
– E.g., in backbone networks, most applications are
elastic and enough capacity is available
.
QoS deployment
≠
QoS research
• Lack of deployed QoS infrastructure in the Internet
does not make QoS research less important
• However, QoS research needs to take into account
that there is no deployed infrastructure
.
Frontiers of QoS Research after the Internet
Apply QoS principles to
different contexts:
•
•
•
•
•
•
•
•
.
QoS in wireless LANs
QoS in sensor networks
QoS in wireless ad-hoc
QoS in P2P networks
QoS in access networks
QoS in VoIP
QoS in MPLS
QoS in QoS
Fundamentals:
1.
Provide new insights into
fundamentals of packet
networking
2. Develop system design
principles for QoS systems
3. Develop new analytical tools
Fundamentals: Statistical Multiplexing
• QoS research knows a lot about complex scheduling
• However, often it takes a worst-case view of the network
and ignores statistical multiplexing
• Opportunity: QoS research that considers statistical
characteristics of traffic can provide insights into
fundamental properties of packet networks
(Note: Statmux is the reason d’être of packet networks)
• Example problems:
– What is the impact of scheduling compared to statmux?
– How does this vary with type of traffic? (e.g., self.
similar traffic)
.
Expected
case
Worst-case
Probable worstcase
Impact of Statistical Multiplexing vs.
Scheduling
Thick lines: EDF Scheduling
Dashed lines: SP scheduling
Statistical multiplexing
makes a big difference
Scheduling
has small impact
.
Data from: IEEE SAC. 18(12):2651–2664, Dec. 2000
Example: MPEG videos with delay constraints at C= 622 Mbps
Deterministic service vs. statistical service (e = 10-6)
Comparisons of statistical service guarantees for
different schedulers and traffic types
Schedulers:
SP- Static Priority
EDF – Earliest
Deadline First
GPS – Generalized
Processor Sharing
Traffic:
Regulated – leaky
bucket
On-Off – On-off
source
FBM – Fractional
Brownian Motion
.
C= 100 Mbps, e = 10-6
Data from: Technical Report, Univ. of Virginia, CS Dept.,
No. CS-20-2003, 2003
Impact of traffic type on statistical multiplexing
Design principles: QoS systems
• Systems with QoS have separate design components:
– Admission control
– Traffic conditioning
– Scheduling
– Signaling
– Policy and Accounting
• Numerous trade-offs:
– Edge vs. endsystem vs. core implementations
– Soft state vs. hard-state signaling
– Centralized, distributed, user-based, or no admission control
• Opportunity: Exploit available know-how to develop guidelines for
choices in QoS design space for any given networking context
.
Search for “Toy Models”
• Learn from physics:
– Wide use of toy models that capture key characteristics of
studied system (without being an exact characterization)
– Look for models that permit back-of-the-envelope calculations
– Toy models are usable by non-theorists
Early days of networking used toy models: M/M/1 Queue
• Kleinrock’s PhD Dissertation (cited as laying the foundation for packet
networks) heavily uses M/M/1 type models
Today: ns-2 culture
• M/M/1 has lost appeal as toy model, and was replaced with ns-2
• Simulations are good to evaluate incremental changes to existing
systems, but not to evaluate radically different designs
• ns-2 may be partially responsible for incremental thinking in
networking
.
My proposal:
Develop network calculus into new “Toy Model”
Today, fundamental progress in networking is hampered by
the lack of methods to evaluate how radically new designs
will perform.
• Opportunity: Simple (`toy') models that permit fast
(`back-of-the-envelope') evaluations can become an
enabling factor for breakthrough changes in networking
research
• Approach: Probabilistic version of min-plus network
calculus (stochastic network calculus) is a candidate for a
new class of toy models for networking
.
Network Calculus
(Cruz, Chang, LeBoudec)
Sender
S1
2
SS
net
S3
Receiver
Network Calculus:
.
•
Arrivals are described by envelopes and service by
“service curves”.
•
If S1, S2 and S3 are service curves that describe
the service to a flow then Snet = S1 * S2 * S3
•
Many similarly elegant results
Stochastic Network Calculus
• State of the art:
– Effective bandwidth theory is integrated
– Envelope derived for numerous traffic models
– Various Snet formulations exist (some wrong)
• What is open:
– A lot of technical issues (but problems are difficult)
– No simple computational algorithms exist
– Relationship to other theories (queueing theory, control theory)
not clear
– How to reduce learning curve and complexity, to make it attractive
for non-theorists?
– Suitability of model to real problems (ie., “non toy problems”) is
untested
.
QoS is like World Peace
• It is a worthy goal,
• It is difficult to achieve,
• And progress is made in small steps
.