An Optimization Case in Support of Next Generation NFV ... · SGSN Firewall CG-NAT ... An...

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An Optimization Case in Support of Next Generation NFV Deployment

Zahra Abbasi <[email protected]> Ming Xia M <[email protected]>, Meral Shirazipour <[email protected]>, Attila Takacs <[email protected]>

Ericsson Research Silicon Valley

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An Optimization Case in Support of Next Generation NFV Deployment | HotCloud15 | © Ericsson AB 2015 | 2015-07-06 |

A means to make the network functions more flexible and simple by minimizing dependence on HW constraints

v

§  Network Functions are SW-based over well-known HW § Multiple roles over same HW

ORCHESTRATION, AUTOMATION & REMOTE INSTALL

DPI BRAS

GGSN/ SGSN

Firewall

CG-NAT

PE Router

VIRTUAL APPLIANCES

STANDARD HIGH VOLUME SERVERS

Virtualised Network Model: VIRTUAL APPLIANCE APPROACH v

§  Network Functions are based on specific HW&SW § One physical node per role

DPI BRAS GGSN/SGSN

Session Border Controller Firewall CG-NAT

PE Router

Traditional Network Model: APPLIANCE APPROACH

Network Func-on Virtualiza-on (NFV) concept

Source: Adapted from D. Lopez Telefonica I+D, NFV

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Motivation: NF cloudification OPPORTUNITIES › All advantages of virtualization:

–  quick provisioning –  scalability –  mobility –  Reduced CapEx –  Reduced OpEx, …

CHALLENGES

› Too many decision parameters

– Number of VNF Instances – VNF placement – Service chain installation

› Hard optimization problem

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Motivation: optimized NFV deployment

VNF Design & Architecture

Optimized NFV deployment in the

cloud

› NFV is more than just softwarization of NFs E.g., Multiple instances per Virtualized Network Functions (VNF)

E.g., VNF disaggregation (several virtualized components per NF)

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State of the art solutions (cont.)

OpenNF, Stratus, ClickOS, Middlepipe etc.

NF cloudification solutions to ensure security, fault tolerance, performance and orchestration efficiency

Given virtualized Network Functions, how to deploy VNFs and implement service chains in the cloud in a cost efficient way?

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System Model DPI

BRAS GGSN/ SGSN

Firewall

CG-NAT

PE Router

VNFs (VIRTUAL APPLIANCES)

SERVICE CHAINS

›  VNF Resource Requirements – bandwidth/ – Computations (CPU, Memory) – Storage

›  Policy Requirements – Order – Affinity/anti-affinity – High availability, etc.

›  Business model requirements – Do tenants share a VNF instance or not

› Cloud infrastructure and server capacities

Objective: Place VNFs and implement VNF chains in the cloud to optimize The desired objectives by cloud operators and users

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NFV cost model components

Cloud operator Users Minimize resource consumption

Minimize OPEX (e.g., energy consumption)

Minimizing computation and networking overhead

Pay less

Minimize delay

Minimizing computation and networking overhead

Minimizing computation and networking latency

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Computation and networking aware NFV deployment

CPU utilization

Network cost efficient deployment

Computation cost efficient deployment

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Optimization case for NFV deployment

Cost types:

Computation

networking

Joint optimization of computation and networking cost

Cost contributors:

Number of VNF instances

Service chain assignment

Load distribution

Joint VNF placement and load distribution

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Optimization problem

VNF Footprints

Service chains and input traffic

VM capacity

NFV deployment manager

Optimal VNF placement

Optimal load distribution

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Joint optimization of computation and networking cost

CompCost i=CostPerInstancei NoInstancesi Each VNF i:

CommCost j=CostPerHop NoHopsj

Each VNF Chain j:

Optimization problem: ΣiCompCosti + ΣiCommCost j Minimize:

Subject to: Data Centers capacity,

input workload

VNF chains

server capacity

Mixed integer programming

(integer knobs to control VNF placement and load distribution): Theoretically hard to find the optimal solution

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Example solutions and results › Mixed integer programming › Greedy solution:

– solve the VNF deployment for every VNF chain one by one given the previous VNF placement decisions.

› Sample results – Test bed:

›  Platform: 1.8 GHz Intel core i5, and 8 GB memory ›  Solver: GLPK

– MILP runtime increasingly grows with increasing the size of problem in terms of number of servers, and number of VNF chain

– Greedy solution is fast and scalable which comes with increasing the communication cost

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Conclusions

› NFV paradigm: New needs and challenges for optimizing VNF placement in the cloud

› Call for efficient algorithms to solve large-scale and complicated NFV deployment problems

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Open questions for future work › Architectural challenges

– What are the various disaggregation architecture for various NFs ›  how does that impact the traffic model › How does that impact the NFV deployment model? › How does it impact the optimization modeling and the solution?

›  Implementation challenges – What are the profiling schemes (online versus offline)

› Algorithmic challenges – Online versus offline solutions

›  VNF migration overhead

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