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 planeis decoupled from data forwarding plane (data-plane)

and

can be directly programmable.

Software Defined Networking ( SDN )

Control planealg, protocols

Data plane:

hardware,

Packet forwarding

SDN decouples the control plane & data plane

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Data plane:

hardware,

Packet forwarding

Control planealg, 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

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OpenFlow Hardware

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.7

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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 Engineeringfor QoS Provisioning in SDN [1]

Cost Minimization for Rule Caching in Software Defined Networking [2]

Near-Optimal Routing Protection for In-Band Software-Defined Networks [3]

Threads of this dissertation

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Rule spaceis limited

Link bandwidthis limited

Min (rule-number)Opt (rule placement)

Min (rule caching cost)

Min (delay)Max (throughput)Link load-balance

Resilience guarantee

Cost Opt.

TrafficEngineering

Rule management

&Traffic

engineering

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Outline

Joint Optimization of Rule Placement and Traffic Engineering for QoS Provisioning in SDN [1]


Near-Optimal Routing Protection for Software-Defined Networks



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Joint Optimization of Rule Placement and Traffic Engineering for QoS Provisioning in Software Defined Network

(IEEE ToC2015)

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 2A 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-multiplexingnonRM: non rule-multiplexing

CP: candidate path providednonCP: no candidate path provided

RM-CP:

nonRM-CP:

Min (rule num)

Trivial RM-nonCP & nonRM-nonCPcases are ignored here.

NP-hardness Proof

• 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 varsto round them back into integer, and construct a solution.

20Conditionally 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

Joint Optimization of Rule Placement and Traffic Engineeringfor QoS Provisioning in SDN [1]


Near-Optimal Routing Protection for In-Band Software-Defined Networks [3]



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Topic 2: Rule Caching

Background:When traffic arrives at a switch,

packets need to be processed bylocal-switch orremote-proxy (e.g., a middleboxor even a controller).

Cost Minimization for Rule Caching in Software Defined Networking

(IEEE TPDS 2015)

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:

System model


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 oflocal-processing asandremote-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,

Offline Algorithm


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)

The 1st Online Algorithm


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Competitive ratio of this algorithm:

Online Alg 2: fixed length of Extra Caching Alg (ECA)


The 2nd Online Algorithm

Evaluation of 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



Near-Optimal Routing Protection for Software-Defined Networks [3]



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Background before topic-3When emergent events happen,

e.g., earthquake occurs,some critical network links might be disconnected.

Routing-protection is an important topic !

Because, in the perspective ofTraffic-engineering,

we need to guarantee theMin ( network recovery delay ).

Topic 3: routing protection

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Near-Optimal Routing Protection for In-Band SDNs(The extension of this topic has been published in IEEE JSAC, 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 WorkTopic 3: routing protection

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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


Basic idea: To eliminate the neighboring congestion,

refresh the entire configuration ,rather than the conventional local rerouting.

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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



Near-Optimal Routing Protection for Software-Defined 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 QoSProvisioning in Software Defined Network”, IEEE Transactions onComputers, 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 DefinedNetworking”, 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 andWeihua Zhuang, "Near-Optimal Routing Protection for In-Band Software-Defined Heterogeneous Networks", IEEE Journal on Selected Areas inCommunications (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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