Cost-Efficient Rule Management and Traffic Engineering for Software Defined Networks

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Cost-Efficient Rule Management and
Traffic Engineering for Software Defined
Networks
Huawei Huang
Supervisor: Prof. Song Guo
University of Aizu
Sep. 8, 2016
Presentation slides for Ph.D dissertation

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Outline
Joint Optimization of Rule Placement and Traffic Engineering
for QoS Provisioning in SDN [1]
Cost Minimization for Rule Caching in Software Defined
Networking [2]
Near-Optimal Routing Protection for Software-Defined
Networks [3]
Threads of dissertation
Introduction and background

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SDN is
an emerging network architecture / paradigm
where the
control plane
is decoupled from data forwarding plane (data-plane)
and
can be directly programmable.
Software Defined Networking ( SDN )
 Control plane
alg, protocols
 Data plane:
hardware,
Packet forwarding

SDN decouplesthecontrolplane& dataplane
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 Data plane:
hardware,
Packet forwarding
 Control plane
alg, protocols

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3-layred SDN Architecture
Agile provisioning
Simplify management
Automation service
Benefits:
With SDN, operators, researchers, users, 3rd parties developers:
New
function

Explanation of Basic Concepts
• What is Traffic Engineering (TE) ?
• Control and optimization of routing, to steer traffic through the
network in the most effective way
• Traffic oriented performance, e.g.,
• Max (throughput)
• Min ( packet transfer delay )
• Min ( packet loss )
• How? -- Approaches
• Collect measurements of traffic and topology
• Compute paths based on load, and requirements
• Optimize the setting of the “static” parameters
• With SDN, these are easy.
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Flow Table Entry
(also called Forwarding Rule,
which is installed in Flow-Table of a switch)
Controller
Explanation (cont.)

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How a Packet is processed in a switch / router ?
Flow table stores Flow Table Entry.
Explanation (cont.)

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Structure of a Rule:
Example:
Explanation (cont.)

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Rules paly various functionalities.
Rules have to be installed in TCAMs of switch.
Explanation (cont.)

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Outline
Joint Optimization of Rule Placement and Trac Engineering
Networking [2]
Near-Optimal Routing Protection for In-Band Software-
Defined Networks [3]

Threads of this dissertation
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Rule space
is limited
Link
bandwidth
is limited
Min (rule-number)
Opt (rule placement)
Min (rule caching cost)
Min (delay)
Max (throughput)
Link load-balance
Resilience guarantee
Cost Opt.
Traffic
Engineering
Rule
management
&
Traffic
engineering

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Outline
Networking [2]
Networks

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JointOptimizationofRulePlacementandTrafficEngineering
forQoSProvisioninginSoftwareDefinedNetwork
(IEEEToC2015)
Topic 1:
• Conventionally, duplicated rule-installation
• For each traffic flow, original SDN-protocol installs forwarding
rules on its traversing path
Installs 2 rules for the 2 flows.
If DstIP=0.0.0.3,
then, do Action 1
IP=0.0.0.1
IP=0.0.0.2
If DstIP=0.0.0.3,
then, do Action 1
IP=0.0.0.3
ControllerRule 1
Rule 2
A motivation case.
Server
Clients

• Turning duplicated rule-installation -> multiplexing rule-installation,
when we conduct the TE:
• Only install one common rule that works for multiple flows.
• Total rule-space can be reduced.
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So, we study a problem of
rule-placement:
Min (total rule No.)
subject to:
limited rule space;
link capacity.
Idea
Topic 1: Joint Optimization of Rule Placement and Traffic Scheduling

• 4 cases of formulations :
• MIP: mixed integer programming
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RM: rule-multiplexing
nonRM: non rule-multiplexing
CP: candidate path provided
nonCP: no candidate path provided
RM-CP:
nonRM-CP:
Min (rule num)
Trivial RM-nonCP & nonRM-nonCP
cases are ignored here.

NP-hardnessProof
• Theorem 1. Given a set of candidate paths, the rule placement
problem (RP) mentioned above is NP-hard.
• The proof is done by reducing the well-known 2-partition
problem to the RP problem.
• i.e., we construct a special case of RP problem into the 2-partition
problem.
• 2-partition problem is NP-hard -> rule-place. Problem is NP-
hard.
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Algorithms design
• Fast heuristics based on Relaxing-and-Rounding
• 1st step: Relax the Integer-variables -> Continuous ones
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[0, 1]
Conditionally round.

Algorithms design(Cont.)
• Fast heuristics based on Relaxing-and-Rounding
• Critical idea of 2nd step: conditionally select a part of relaxed vars
to round them back into integer, and construct a solution.
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Conditionally round some
back into integer.

Case study under CP
• With candidate paths provided.
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Cost: 40 rules. Cost: 20 rules.>

Case study under nonCP
• Without candidate paths
22Cost: 40 rules. Cost: 20 rules.
>
Efficiency of RM is proved.

More simulation results
• Show that Rule-Multiplexing (RM) mechanism outperforms than
nonRM.
• Particularly, RM-nonCP has the best performance.
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Outline
Networking [2]
Near-Optimal Routing Protection for In-Band Software-
Defined Networks [3]

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Topic 2: Rule Caching
Background:
Whentrafficarrivesataswitch,
packetsneedtobeprocessedby
local-switch or
remote-proxy(e.g.,amiddleboxorevenacontroller).
CostMinimizationforRuleCachinginSoftware
DefinedNetworking
(IEEETPDS2015)
Virus
Controller
controls all switches
Arriving flow
Server
Firewall
proxies
Client
… Allowed flow
Ingress
switch
Malware
DoS
redirect
Redirected flow

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Decisions for each traffic-flow at each time-slot:
Systemmodel
remote-processinglocal processing
When to install rule?
How long to cache the rule?
Which way to process packets?
0-1 decisionyt = 0 yt = 1
At time-slot t : Remote cost:
expense at the
remote proxy.
Local cost:
expense at the
switch.
xt = 0 or 1?

local-processing cost remote-processing cost
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Total Cost = +
Problem: How to Minimize a joint cost ?
Given a set of flows and required rules,
We normalize the unit cost of
local-processing as
and
remote-processing as
Formulation
Trigger of remote
processing
Fetch at least one time
before caching
Packets in each Time-
slot need to be processed

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Basic analysis:
Typical patterns in an optimal solution:
Three elements of optimal solution: Only remote processing
Only local processing
Hybrid
Idea: achieve the goal by deciding: for a flow,
whether and when to install rules in a switch,
& how long to cache the rules if install them.
Algorithm Design
valid

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How good of this algorithm?
If the trace of a flow is given,
OfflineAlgorithm

Evaluation of offline-algorithm
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Proactive algorithm : rules are only fetched in the first time slot and cached all the remaining duration.
Reactive algorithm triggers remote process at each time slot.

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How good of this algorithm?
Online Alg 1: Exactly Match the Flow(EMF)
The1st OnlineAlgorithm

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Competitive ratio of this algorithm:
Online Alg 2: fixed length of Extra Caching Alg (ECA)
The2nd OnlineAlgorithm

Evaluationof online algorithms
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Performance of Online
algs is within
theoretical bound
Online algs
perform better
than the original
SDN protocol.
More experiments to
prove the correctness
of theoretical bounds
for the online algs.

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Outline
Networking [2]
Networks [3]

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Background before topic-3
When emergent events happen,
e.g., earthquake occurs,
some critical network links might be disconnected.
Routing-protection is an important topic !
Because, in the perspective of
Traffic-engineering,
we need to guarantee the
Min ( network recovery delay ).
Topic 3: routing protection

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Near-OptimalRoutingProtectionforIn-BandSDNs
(TheextensionofthistopichasbeenpublishedinIEEEJSAC,2016.)
https://www.researchgate.net/publication/301842070_Near-Optimal_Routing_Protection_for_In-Band_Software-
Defined_Heterogeneous_Networks
• Motivation:
• The controller<->switch connections are critical ( higher priority than
the data-plane routing paths ),
• disconnection brings very serious damages.
• When link failure occurs, the fast recovery is needed.

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• Question: How to protect the controlling channels?
• with a low recovery delay,
• with a reasonable cost of switch node-configuration.

• Traditional routing protection
• Local routing via Backup paths
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Related Work

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• Dedicated-backup, e.g., 1+1 (1+N) protection
• With no recovery delay at all !!
• But with high cost on both terms:
• Link ( high-bandwidth consumption )
• Node ( switch-configuration cost )
• Trade-off has to be considered:
• If adopt dedicated-backup,
• Reduce the ( cost ) !
Optional Approach :
Double backup paths,
High cost: double Traffic
rate !!

Formulation
• System model
• As shown in Figure 2.
• Formulation with Obj:
• Min ( link-bandwidth cost + connection-setup cost )
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Exact |Ds| number of in-use
paths must be selected.
Capacity constraints on link.
Capacity constraints on node.

Algorithm
• Markov-Approximate based Algorithm
• Obj: load-balancing + connection-setup cost
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Define MC
Transit between different
states
Re-Compute transition
rate of different states

Basicidea:
Toeliminatetheneighboringcongestion,
refreshtheentireconfiguration ,
ratherthantheconventional localrerouting.
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Online handlingTheory
Online handling in case of link-failure

• Simulation
• Fat-tree Datacenter network
• Representative running case
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• Comparison with conventional Local routing
• on the link-bandwidth consumption
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reroute via link (0,4).

• Convergence property of the proposed algorithm
• Comparing with other benchmark algs.
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Outline
Networking [2]
Networks [3]
Conclusion and Future Work

Conclusion and Future Work
• Conclusion
• 3 topics related to Cost-optimization problems over Traffic-
Engineering & Resource-utilization.
• Future work
• I am going to focus on the business logics under SDNs:
• Network Function Virtualization (NFV)
• Resilience and Security enhancement for SDNs
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Major references in slides:
[1] Huawei Huang, Song Guo, Peng Li, Baoliu Ye and Ivan Stojmenovic,
“Joint Optimization of Rule Placement and Traffic Engineering for QoS
Provisioning in Software Defined Network”, IEEE Transactions on
Computers, vol. 64, no. 12, pp. 3488-3499, December 2015.
[2] Huawei Huang, Song Guo, Peng Li, Weifa Liang and Albert Y.
Zomaya, “Cost Minimization for Rule Caching in Software Defined
Networking”, IEEE Transactions on Parallel and Distributed Systems (TPDS),
vol. 27, no. 4, pp. 1007-1016, April 2016.
[3] Huawei Huang, Song Guo, Weifa Liang, Keqiu Li, Baoliu Ye and
Weihua Zhuang, "Near-Optimal Routing Protection for In-Band Software-
Defined Heterogeneous Networks", IEEE Journal on Selected Areas in
Communications (JSAC), vol. 34, no. 11, pp. 2918-2934, October, 2016.

Cost-Efficient Rule Management and Traffic Engineering for Software Defined Networks

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