German Sviridov - PhD defense

Traffic Optimization in Data Center and
Software-Defined Programmable Networks
GERMAN SVIRIDOV
Doctoral Program in Electrical, Electronics and Communications
Engineering
(XXXIII cycle)
Supervisors:
Prof. Paolo Giaccone
Prof. Andrea Bianco

2
Stateful Software-Defined
Networking: enabling replicated
network applications in
programmable data planes
Flow scheduling in data center
networks: optimizing flow
performance by minimizing flow
completion time
Cloud gaming and game engines:
Quality of Experience improvement in
cloud gaming
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV
Blockchains for vehicular
applications
2

3
completion time
cloud gaming
applications
3

Enabling replicated network applications in
03/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 4

Classic Software Defined Networks (SDN)
Control
Plane
Data
plane
Physical separation of control and data plane
A single entity (controller) for the control plane
Data plane operations are now centrally managed
Key enabler for novel applications

Issues with classic SDN
Control
Plane
Data
plane
• Physical latency between switches
and the controller
• Software processing latency
Added latency
• Per flow processing only
• Support of legacy protocols only
Inflexible
commercial
hardware
• Local in-switch processing
• Simplified switch-local control logic
for latency-sensitive application
Need to take a
step back

Programmable data planes
• User-defined packet parsing
• Support for virtually any data protocol
• Modification to any packet field at line rate
Programmable
parsers
• Programmable match-action rules
• Access to persistent states 𝒔𝒊
• Packet recirculation/cloning
Programmable
packet
processing
𝑠𝑖

Enabling new applications and
accelerating old ones
𝑠𝑖
Financial applications
Machine learning
MQTT brokers
DBMS Acceleration
Content caching
Overlay protocols
Load balancers
Packet scheduling
Network telemetry
Firewall/IDS

Limits of data plane-assisted network
applications
𝑠1
𝑠2
𝑠3
𝑠4
•Network applications are local to a single switch
•Independent behavior in the network
Locality constraints
•DDoS detection
•Distributed application/link-aware load balancers
•Network-wide rate limiters
•...
Precluded applications

Limits of data plane-assisted network
applications
𝑠1
𝑠2
𝑠3
𝑠4
•Network applications are local to a single switch
•Independent behavior in the network
Locality constraints
•DDoS detection
•Distributed application/link-aware load balancers
•Network-wide rate limiters
•...
Precluded applications
How to implement logically centralized network-
wide network applications?

Logically centralized network applications
𝑠1
𝑠2
𝑠3
𝑠4
Unique network application
Centralized
state with
global view
of the
network
Traffic
reroute
Data
overhead
Bandwidth
constraints

𝑠1
𝑠2
𝑠3
𝑠4
𝑠2
1
𝑠3
1
State replication in programmable data
planes
12
Replicated network application
Centralized
state with
global view
of the
network
No traffic
reroute
No data
overhead
Sync
overhead

𝑠1
𝑠3
𝑠4
𝑠2
1
State replication in programmable data
planes
13
Replicated network application
Centralized
state with
global view
of the
network
No traffic
reroute
No data
overhead
Sync
overhead

A recipe for state replication in
LODGE
Suitable
replication
algorithm
Need to convey
state-updates to
other replicas
Need to define a
suitable
transport
protocol
Need to be able
to generate new
packets
𝑠2
𝑠3
𝑠2
1
𝑠3
1

LODGE
Eventual
consistency
Need to convey
state-updates to
other replicas
Need to define a
suitable
transport
protocol
Need to be able
to generate new
packets
𝑠2
𝑠3
𝑠2
1
𝑠3
1

LODGE
Eventual
consistency
Controller-
constructed
distribution tree
Need to define a
suitable
transport
protocol
Need to be able
to generate new
packets
𝑠2
𝑠3
𝑠2
1
𝑠3
1

LODGE
Eventual
consistency
Controller-
constructed
distribution tree
Tailored
P4/OPP-enabled
transport
protocol
Need to be able
to generate new
packets
𝑠2
𝑠3
𝑠2
1
𝑠3
1

LODGE
Eventual
consistency
Controller-
constructed
distribution tree
Tailored
P4/OPP-enabled
transport
protocol
Traffic-triggered
packet cloning
and
recirculation
𝑠2
𝑠3
𝑠2
1
𝑠3
1
D
S

LODGE-Enabled Applications
server cluster 1
server cluster 3
server cluster 2
server cluster 4
ASN1 ASN2
ASN3
ASN4
R2
R1
R3
R4
SW1
SW2
SW3
SW4
Realistic scenario
Emulated
testbed
LODGE
implementation
•WAN Autonomous System
(AS) with 4 neighbours
•Incoming traffic monitoring
and reaction
•V1 Model with BMV
•Emulated network
•P4 14/16
•Open Packet Processor (OPP)

• Distributed DDoS from ASN#
• LODGE logic embedded at
SW1 and SW3
• Simultaneous detection of the
DDoS at all switches without
traffic rerouting
• Distributed rate limiting
• LODGE application embedded
at border routers
• Simultaneous drop of the rate
for both flows
server cluster 1
server cluster 3
server cluster 2
server cluster 4
ASN1 ASN2
ASN3
ASN4
R2
R1
R3
R4
SW1
SW2
SW3
SW4

• Distributed DDoS from ASN#
• LODGE logic embedded at
SW1 and SW3
• Simultaneous detection of the
DDoS at all switches without
traffic rerouting
Why SW1 & SW3 or R# in
particular?
22
server cluster 1
server cluster 3
server cluster 2
server cluster 4
ASN1 ASN2
ASN3
ASN4
R2
R1
R3
R4
SW1
SW2
SW3
SW4
• Distributed rate limiting
• LODGE application embedded
at border routers
• Simultaneous drop of the rate
for both flows

Where to place the state replicas?

State replicas placement: data vs
synchronization traffic
Data traffic
Sync traffic
𝑠1
1
and 𝑠1
2
: replicas of the same state 𝑠1
X2
X1
s s
s s
- More data traffic overhead
- Less sync traffic overhead
- Less data traffic overhead
- More sync traffic overhead
Number of state replicas
𝑠1
1
𝑠1
2
𝑠1

State replicas placement: data vs
synchronization traffic
Number of state replicas • Data vs synchronization traffic trade-off?
• How many replicas to use?
• Where to place the replicas?
Data traffic
Sync traffic
𝑠1
1
and 𝑠1
2
: replicas of the same state 𝑠1
X2
X1
s s
s s
𝑠1
1
𝑠1
2
𝑠1
- More data traffic overhead
- Less sync traffic overhead
- Less data traffic overhead
- More sync traffic overhead

Optimal state replication in stateful data
planes
MILP formulation for optimal state replication
Input
• Network topology
• Traffic matrix
• Network applications
• Per-flow application
requirements
• Amount of sync traffic ෡
𝜆𝑠
Constrained by
• Bandwidth availability
• Switch resources
availability
• Traffic matrix
Objective
• Minimize maximum
congestion
• Minimize total traffic
Output
• Number of replicas
• Replicas placement
• Data traffic routing
• Sync traffic routing

Optimal state replication in stateful data
planes
Difficult to apply to big topologies!
MILP formulation for optimal state replication
Input
• Network topology
• Traffic matrix
• Network applications
• Per-flow application
requirements
• Amount of sync traffic ෡
𝜆𝑠
Constrained by
• Bandwidth availability
• Switch resources
availability
• Traffic matrix
Objective
• Minimize maximum
congestion
• Minimize total traffic
Output
• Number of replicas
• Replicas placement
• Data traffic routing
• Sync traffic routing

Optimal state replication –
approximated solution
28
Algorithm for replica placement
• Based on betweenness centrality
• Solution perturbation
Optimal number of replicas
• Analytical expression
• Asymptotic optimal for Manhattan topology

How to put everything together?
Network application
abstracion
Application
decompositi
on
Consistency
constraints
LODGE dataplane
implementation
P4 OPP
Optimal state
placement
Number of
states
State
position
Traffic routing
Data traffic
Synchroniza-
tion traffic
How to define distributed
network applications?

LOADER: An abstraction for replicated network
applications

31
The goals of LOADER
Define an abstraction model for replicated
network applications
Enable efficient translation to basic network
primitives
Combine efforts of optimal state replication
and LODGE into a unified framework

32
Programming
abstraction for
network applications:
•Exposure of
replicated states to
the programmer
•Management of state
inconsistency
•Definition of 𝜆𝑠
Compiler
•Automatic policy
decomposition and
translation to target-
specific language
Embedder
•Automatic state
embedding
•Based on heuristic for
optimal state
replication

33
Programming
abstraction for
•Exposure of
the programmer
inconsistency
Compiler
•Automatic policy
decomposition and
specific language
Embedder
•Automatic state
embedding
optimal state
replication

34
Programming
abstraction for
•Exposure of
the programmer
inconsistency
Compiler
•Automatic policy
decomposition and
specific language
Embedder
•Automatic state
placement
optimal state
replication
𝑠1
𝑠2
𝑠3
𝑠4
𝑠2
1
𝑠3
1

35
Programming
abstraction for
•Exposure of
the programmer
inconsistency
Compiler
•Automatic policy
decomposition and
specific language
Embedder
•Automatic state
placement
optimal state
replication
𝑠1
𝑠2
𝑠3
𝑠4
𝑠2
1
𝑠3
1

LOADER
Network application
abstracion
Application
decompositi
on
Consistency
constraints
LODGE dataplane
implementation
P4 OPP
Optimal state
placement
Number of
states
State
position
Traffic routing
Data traffic
Synchroniza-
tion traffic
[1] Sviridov, German; Bonola, Marco; Tulumello, Angelo; Giaccone, Paolo; Bianco, Andrea; Bianchi, Giuseppe, LODGE: LOcal Decisions on
Global statEs in programmable data planes, in: IEEE NetSoft, 2018
[2] Muqaddas, Abubakar Sidique; Sviridov, German; Giaccone, Paolo; Bianco, Andrea, Optimal state replication in stateful dataplanes, in:
IEEE JSAC, 2020
[3] Sviridov, German; Bonola, Marco; Tulumello, Angelo; Giaccone, Paolo; Bianco, Andrea; Bianchi, Giuseppe, LOcAl DEcisions on Replicated
States (LOADER) in programmable data planes: programming abstraction and experimental evaluation, in: Computer Networks 2021
[1] [2]
[3]

37
completion time
cloud gaming
applications
37

Automating game QoE assessment in cloud gaming

The next generation of gaming:
Cloud Gaming
• Games run on a remote server
• Video stream of the game is sent to the users
Remote game
rendering
• Users buy the game and pay the subscription fee
• Play directly on a TV or phone
No need for
hardware

The next generation of gaming:
Cloud Gaming
• Games run on a remote server
• Video stream of the game is sent to the users
Remote game
rendering
• Users buy the game and pay the subscription fee
• Play directly on a TV or phone
No need for
hardware
Video streaming
(High BW, High Delay)
Online gaming
(Low BW, Low Delay)
Input lag
(High BW, Low Delay)

Not all games are made equal
Different impact
of latency
Different genres
Multiple stages
•Action/FPS
•Strategy, card,
interactive fiction
•Action stages
•Exploration/narration
stages

Different impact
of latency
Different genres
Multiple stages
•Accuracy decrease in
FPS
•Smaller crop yield in
farming simulator?
•Action/FPS
•Strategy, card,
interactive fiction
•Action stages
stages
Different network requirements

Different impact
of latency
Different genres
Multiple stages
•Accuracy decrease in
FPS
•Smaller crop yield in
farming simulator?
•Action/FPS
•Strategy, card,
interactive fiction
•Action stages
stages
Need for fine-grained game QoE
assessment

Game Quality of Experience (QoE) assessment in
practice

Game
- 25 new game releases
per day on Steam
- Impossible to keep up
with new releases
WAN DC
379 565
1771
2964
4207
7049
9050
0
2000
4000
6000
8000
10000
2012 2013 2014 2015 2016 2017 2018
Number of games released on steam
Human subjects
- Difficult and
expensive to find
enough subjects
- No diversity
guarantee
Controlled environment
- Need to bring subjects to
the lab
- Difficult to achieve in the
wild
Outcome
- Noisy experimental data
- Coarse grained
information
- No repeatability
- Outdated information

Game
per day on Steam
with new releases
WAN DC
379 565
1771
2964
4207
7049
9050
0
2000
4000
6000
8000
10000
2012 2013 2014 2015 2016 2017 2018
Guilty
Human subjects
- Difficult and
expensive to find
enough subjects
- No diversity
guarantee
- Need to bring subjects to
the lab
- Difficult to achieve in the
wild
Outcome
- Noisy experimental data
- Coarse grained
information
- No repeatability

WAN DC
379 565
1771
2964
4207
7049
9050
0
2000
4000
6000
8000
10000
2012 2013 2014 2015 2016 2017 2018
Game
per day on Steam
with new releases
Bots
- Diversity and
experience can be
programmed
- Still expensive
- Can be easily simulated
- Fine tuning is easily
achieved
Outcome
+ Deterministic results
+ Fine grained information
+ Repeatability

WAN DC
379 565
1771
2964
4207
7049
9050
0
2000
4000
6000
8000
10000
2012 2013 2014 2015 2016 2017 2018
Game
per day on Steam
with new releases?
Bots
- Diversity and
experience can be
programmed
- Still expensive?
- Can be easily simulated
- Fine tuning is easily
achieved
Outcome
- Deterministic results
- Fine grained information
- Repeatability
AI?

Removing humans from the loop:
Putting the AI to work for QoE assessment

Deep reinforcement learning (DRL) for
video games
Learning to play from raw video pixels
Trial and error approach
No supervision Good adaptability
Active exploration
Reward shaping
Tunable behavior

Learning to play Atari
• Deep Q Networks
• Simple, yet powerful model for DRL
One
algorithm
• Seaquest, Beam Rider and Breakout
• Taken from the Atari game catalogue
Three
games
• (Super) human level achieved in less
than 5 hours of training
• Ready to be deployed in the testbed
Three AI
agents

Measuring the gaming performance
• Per-frame lag 𝑙
• Per-frame lag probability
𝑝𝑙𝑎𝑔
• Per-keystroke drop
probability 𝑝𝑑𝑟𝑜𝑝
Emulated
network

Maximizing average game score
Need for fine-grained game QoE
assessment
Without traffic
prioritization
With traffic
prioritization

Moving to more complex games
Moving to more complex games
55
Classic Doom
Similar algorithm for training
Different
maps
Different
game modes
Network
parameter
perturbation

Automating game QoE assessment
Cloud gaming
scenario
Emulated network
with perturbation
Artificial players trained with DRL
Atari Doom
[4] Sviridov, German; Beliar, Cedric; Bianco, Andrea; Giaccone, Paolo; Rossi, Dario, Removing human players from the loop: AI-assisted
assessment of Gaming QoE, in: IEEE Infocom NI Workshop, 2020
[5] Sviridov, German; Beliar, Cedric; Simon, Gwendal; Bianco, Andrea; Giaccone, Paolo; Rossi, Dario, Leveraging AI players for QoE estimation
in cloud gaming, in IEEE Infocom Demo Session, 2020
[5]
[4-5]
Per-game QoS mechanism which
maximizes average game QoE

Past works
57
•Is centralized flow scheduling feasible in practice?
•Can we reach close to state of the art performance
while employing commodity switches for flow
scheduling?
Flow scheduling
in data center
networks
•Blockchain as an enabler for mobility verification
•How to manage trust for sensitive data?
•Which scale private blockchains can reach?
Blockchains for
vehicular
applications
[6-7]
[8]
[6] Sviridov, German; Giaccone, Paolo; Bianco, Andrea, Low-Complexity Flow Scheduling for Commodity Switches in Data Center Networks, in: IEEE Globecom,
2019
[7] Sviridov, German; Giaccone, Paolo; Bianco, Andrea, To Sync or Not to Sync: Why Asynchronous Traffic Control Is Good Enough for Your Data Center, in: IEEE
Globecom, 2018
[8] Chiasserini, Carla Fabiana; Giaccone, Paolo; Malnati, Giovanni; Macagno, Michele; Sviridov, German, Blockchain-based mobility verification of connected cars,
in IEEE CCNC, 2020
57

Current work – (Cloud) Gaming
58
•Games are made of different stages
•Each stage requires different QoS
•Can we achieve fine-grained QoS control?
Game stage
classification
•Training time of a given game is typically
unknown
•Can we understand the training difficulty before
even training?
Estimating
training
complexity
•Game engines are monolithic
•Opposite of the cloud-based applications
•Can we make cloud gaming truly cloud-based?
Distributed
gaming
engines
Graphics
Front-end
A.I. Physics

References
59
[1] Sviridov, German; Bonola, Marco; Tulumello, Angelo; Giaccone, Paolo; Bianco, Andrea; Bianchi, Giuseppe, LODGE: LOcal Decisions on Global statEs in
programmable data planes, in: IEEE NetSoft, 2018
[2] Muqaddas, Abubakar Sidique; Sviridov, German; Giaccone, Paolo; Bianco, Andrea, Optimal state replication in stateful dataplanes, in: IEEE JSAC, 2020
[3] Sviridov, German; Bonola, Marco; Tulumello, Angelo; Giaccone, Paolo; Bianco, Andrea; Bianchi, Giuseppe, LOcAl DEcisions on Replicated States
(LOADER) in programmable data planes: programming abstraction and experimental evaluation, in: Computer Networks 2021
[4] Sviridov, German; Beliar, Cedric; Bianco, Andrea; Giaccone, Paolo; Rossi, Dario, Removing human players from the loop: AI-assisted assessment of
Gaming QoE, in: IEEE Infocom NI Workshop, 2020
[5] Sviridov, German; Beliar, Cedric; Simon, Gwendal; Bianco, Andrea; Giaccone, Paolo; Rossi, Dario, Leveraging AI players for QoE estimation in cloud
gaming, in IEEE Infocom Demo Session, 2020
[6] Sviridov, German; Giaccone, Paolo; Bianco, Andrea, Low-Complexity Flow Scheduling for Commodity Switches in Data Center Networks, in: IEEE
Globecom, 2019
[7] Sviridov, German; Giaccone, Paolo; Bianco, Andrea, To Sync or Not to Sync: Why Asynchronous Traffic Control Is Good Enough for Your Data Center, in:
IEEE Globecom, 2018
[8] Chiasserini, Carla Fabiana; Giaccone, Paolo; Malnati, Giovanni; Macagno, Michele; Sviridov, German, Blockchain-based mobility verification of
connected cars, in IEEE CCNC, 2020
59

Backup slides

Centralized explicit rate assignment
Taking inspiration from Faspass:
◦ Centralized per packet DC-wide scheduling
Per-flow rate control:
◦ Given the actual number of flows...
◦ ...allocate a rate to transmit data for each pair of
source/destination servers
◦ Rate re-computation occurs whenever a flow starts/ends
Two step algorithms
1. Rate assignment
2. Routing
Grant:
SRCi -> DSTj
Through Sk
With rate Rij
Centralized Controller
At each
new/ended
flow

Example of ASY algorithms
Rate assignment algorithm
◦ rij = number of flows from server i to server j
◦ Normalize the matrix between pairs of servers in order
to be double-stochastic
◦ Avoid to overload servers
◦ Possible parallel implementation
Routing
◦ Exploit the available parallel paths to reduce output
contentions
◦ Randomly assign a different path for each flow (e.g.,
ECMP)
r11 r12 r13 . . . r1N
r21 . . . .
r31 . . . .
. . . . .
. . . .
rN1 rN2 . . . rNN
Normalize
Normalize

SYN vs ASY - schedulers
Scenario
◦ Realistic protocol stack
◦ incast + fixed packet size
SYN schedulers
◦ MMF: Max-Min Fair
◦ OCF: Oldest Cell First
◦ SRJF: Shortest Remaining Job First
Average Flow Completion Time (AFCT)

Minimizing FCT in DCN
Optimal flow scheduling in DCN requires knowledge about
individual flow length.
◦ Difficult (impossible) to know in advance
◦ Fully information-agnostic schedulers penalize long flows
◦ Knowledge about flow length distribution may help in improving the
scheduling

Minimizing FCT
with average flow
length distribution
Flow prioritization flows without knowledge about
their length.
◦ Use of average flow length distribution
◦ Fine grained scheduling at host
◦ Coarse scheduling at switches

Minimizing FCT with average flow length
distribution
Mice Medium Elephant
Average
Realistic packet-level network simulator
◦ Real implementation of TCP and DCTCP
◦ Realistic flow length distributions
◦ Mixed and uniform flow length scenarios
◦ Robustness analysis to CDF underestimation

German Sviridov - PhD defense

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