9/8/2014 1 A Practical Traffic Management for - PowerPoint Presentation

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9/8/2014

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A Practical Traffic Management for Integrated LTE-WiFi Networks

Speaker

: Rajesh Mahindra

NEC Labs America

Hari

Viswanathan

, Karthik Sundaresan, and Mustafa Arslan

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Key TrendsData traffic exploding on cellular networksRise in video streaming, social networking

Revenue per byte is decreasing

Mobile operators embracing

WiFi

as a key technology to enhance LTE experience

Cheap to deploy – unlicensedEasy (fast) to deploy – unplanned

Critical to manage flows across

APs-Basestations to

maximize

QoE and resource utilization

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Operator-based WiFi deploymentsAbsence of network-wide traffic managementDevices always connect to WiFi when available (static policy)Past focus has been authentication methods over WiFi

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Today: Devices always connect to WiFi

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Operator-based WiFi deploymentsAbsence of network-wide traffic managementDevices always connect to WiFi when available (static policy)Past focus has been authentication methods over WiFi

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Absence of tight data-plane integration

3GPP based deployments have high

CAPEX

Requires backhauling

WiFi

traffic through mobile core

Increased investment in infrastructure

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Today: Resistance to Tight Integration of LTE and WiFi

LTE Core-Network

ePDG

3GPP standard

WiFi

Gateway

PDN-gateway

MME

Serving-gateway

INTERNET

Increased backhaul

costs

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Operator-based WiFi deploymentsAbsence of network-wide traffic managementDevices always connect to WiFi when available (static policy)Past focus has been authentication methods over WiFi

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Absence of tight data-plane integration

3GPP based deployments have

high

CAPEX

Requires backhauling

WiFi

traffic through mobile core

Increased investment in infrastructure

Inability to perform dynamic

network selection

Result

Diminishes the potential effectiveness of

WiFi

Degrades the user Quality of Experience (QoE)

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OpportunityState of the Art: Client-side solutions Qualcomm’s CnE, Interdigital SAMStatic policies (application level) enforced locally on each clientQoE requirements provided by the application on the clientClient-side decision making -> inefficient use of network resourcesOperator agnostic mobile service (MOTA), in Mobicom

2011Requires frequent network state information from each base stationIncompatible with standards -> difficult to deployIndividual decisions by client -> sub-optimal

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Inability for Mobile Operators to perform effective

network-wide traffic management!

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Our Idea: A Traffic Management Solution

LTE Core-Network

WiFi

Gateway

Traffic Manager

Maps user flows to appropriate network(LTE/

WiFi

)

Centralized management ->

E

fficient

use of network resources

Reduces backhaul costs -> Facilitates

dynamic

traffic

mgmt

Operates for each LTE cell ->

Scalable

Standards agnostic ->

Easily

Deployable

PDN-gateway

MME

Serving-gateway

Network Interface Assignment

Switching Service

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ComponentsNetwork Interface Assignment Algorithm (NIA)Goal: Dynamically maps user traffic flows to appropriate LTE basestation or

WiFi

AP

Interface switching service (ISS)

Goal: Switch current user flows from WiFi AP to LTE or vice versa based on decisions from NIA

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Component 1: NIA9/8/201411

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Consider an LTE cell and multiple

WiFi APs in its coverage area Assign basestation/ AP to each flowMaximize sum of users flows’ QoEQoE captured using “utility”Weighted PF provides differential QoEPricing function supports 2 modelsBased on data usage

Based on price/byteProblem Formulation

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Weight

Throughput

Network Pricing

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Throughput ModelsLTE basestation performs weighted PFWiFi AP performs throughput based fairnessAlgorithm does not depend on specific schedulerWiFi APs may perform weighted PF9/9/201413

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Problem depiction9/9/201414

3Mbps

5

Mbps

1Mbps

2Mbps

4Mbps

8Mbps

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Problem depiction9/9/201415

4Mbps

6Mbps

2Mbps

3Mbps

2Mbps

5Mbps

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Problem depiction9/9/201416

5Mbps

7Mbps

3Mbps

3Mbps

3Mbps

7Mbps

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Network Interface Assignment (NIA)Problem is NP-HardIncluding the simplest topology of an LTE cell and a WiFi APNIA is a two-step greedy heuristicConsiders each AP-basestation in isolationFixes assignment for AP that maximized incremental utilityIterate till all hotspots are coveredComplexity is O(K2S2), where K = # clients, S = # APs

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NIA ExampleTrigger - arrival/departure of clients or periodicStep 1: In each WiFi hotspot, partition clients into two sets, LTE and WiFi, so that sum of utilities is maximized 9/8/201418

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9/8/201419NIA ExampleStep 2: Finalize interface assignment for clients in the WiFi hotspot with the highest incremental utility

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9/8/201420NIA Example – IterateRepeat 1&2 with the new initial condition until all hotspots are covered

Done!

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9/8/201421Component 2: Interface Switching Service

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Mid-session network switching capability

facilitates dynamic traffic

mgmt

Leverage HTTP characteristics

HTTP traffic (

esp video and browsing) dominates (>90% of internet)

Session content(s) are downloaded using multiple HTTP requestsVideo streaming use HTTP-PD (Progressive Download) or DASH (Dynamic Adaptive Streaming over HTTP): A HTTP-GET request/chunkBrowsing

: A HTTP-GET request/object

DASH Server

HTTP

TCP

Multi-resolution

video

VIDEO

VIDEO

VIDEO

Clients

Design Considerations

HTTP GET

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Interface Switching Service (ISS)9/9/201423

SwitchInterfaceLTEInterface to NIAHTTP based Video streaming/Browsing

Control TrafficWiFi

LTE

HTTP-GET

ISS Controller

Application /

Browser

HTTP Proxy

Control Logic

Mobile Device

Internet

Other types of traffic can leverage existing 3GPP standards for seamless interface switching

Switch to

WiFi

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Prototype9/9/201424Linux Laptop(Client)NEC LTE BasestationWiFi Gateway

DlinkWiFi APOpenEPCLTE Core

HTTP

requests

Chrome

Browser

Shrpx

HTTP Proxy

ISS Control

ATOM

NIA Algorithm

Squid HTTP Proxy

Squid HTTP Proxy

ISS Control

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9/8/201425Experiment 1: Large-scale evaluation

Topology: 1 LTE basestation and 3 WiFi APsResult: ATOM performs better than client-side solutions

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9/8/201426Experiment 2: Benchmarking the ISS

Measured the time taken for flows to switch using ISS:HTTP based video streaming flowsHulu (uses HTTP-DASH) v/s Youtube(uses HTTP-PD)Insight: Switching time improves with DASH streamingDASH flows use smaller chunk sizes to

ensure adaptive-ness to changing network conditions

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Operators have to look towards exploiting multiple access technologies to increase

capacity

WiFi

offers the cheapest alternate to cellular

Our Contributions

:

a traffic management

solution that assigns user flows to LTE basestation/

WiFi

APs

Low complexity, scalable algorithm for flow assignment

Network-based solution more effective

than client-side

solutions

HTTP based switching provides dynamic flow assignment at lower costs

Summary

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