Networked 3-D Virtual Collaboration in Scienceand Education: Towards ‘Web 3.0’

(A Modeling Perspective)

Michael DevetsikiotisProfessor of Electrical & Computer Engineering

North Carolina State Universitymdevets@ncsu.edu

http://www4.ncsu.edu/~mdevets

Collaborators: Mitzi Montoya, George MichailidisNC State Team: Michael Kallitsis, Vineet Kulkarni,

Ioannis Papapanagiotou, Nilesh Gavaskar, Yan Wang

• Main themes:– Services dominate (“service oriented networks”?)– Ubiquitous social nets and virtual collaboration – Distributed and virtualized delivery (the “cloud”)– Convergence: telecom - services - infrastructure

• Horizontal integration and fixed-to-wireless convergence• NGN, IMS/SIP, web services: middleware meets the telcos

• New apps: p2p, virtual worlds, social nets, games, virtual collaboration, tele-presence, Web 2.0,…

• Emerging “application content infrastructure”• All via next generation communication networks

Strategic Trends and Overview

• Our larger goals:– Capture “presence” and location awareness– Quantify socio-technical interactions– Characterize workload spatially and temporally– Optimize over multiple resources, across layers

Overview: Service Oriented Networks

SON and Convergent Nets

Workload Aggregation and Distributed

Delivery

Social Apps and Virtual

Collaboration

“Cloud”

• Service Oriented Networking

• Resource optimization in net appliances

• Virtual collaboration environments and socio-technical modeling

• Resource optimization in clouds and wireless

• Aggregation architectures and traffic models

Research Topics

Services in Networks and Economy• Over 70% of advanced economies today in services• Components becoming “commodities”• Applies to telecom and IT sectors too• Services are about “co-production” and “innovation”• A new “Service Sciences” discipline is emerging• Both human level and software/middleware

Business/Economics

Competition

Technology/Resources

Congestion, QoS

Services/Innovation

Flexibility

Overview of ServiceOverview of Service--Oriented NetworkingOriented Networking• Definition

– Service-Oriented Networking (SON): emerging network architecture gaining IT efficiency by providing intelligent functionality in the network fabric, previously unavailable or impractical to implement.

• Details

– Application awareness in the network fabric is key– Challenges end-to-end principle of networks (“don’t

touch the payload”)– Assumes that the network can make intelligent

decisions based on application data– Revisits earlier research in application-aware networks– NGN standards make architecture more flexible

ServiceService--Oriented Networking: Oriented Networking: ““AwarenessAwareness””

Delivery: SON AppliancesDelivery: SON Appliances

ServiceService--Oriented Networking Functions: Oriented Networking Functions: Functional OffloadingFunctional Offloading

• Offload services into the network fabric that can leverage specialized hardware (cryptographic or XML processing ASIC/FPGA)

• In this example, the network offers a value added service of securing SOAP/XML requests and responses inline

• In certain situations, the network could provide a full offload of endpoint services (e.g., caching stock prices), and would be managed by a caching policy

ServiceService--Oriented Networking Functions: Oriented Networking Functions: ContentContent--Based RoutingBased Routing

• Content-based routing typically involves applying a rule against some part of a service request (header or content) to derive a token as a result.

• This token is then used to make a routing decision

• In this example, where requests are XML messages, we utilize XPath to extract the appropriate routing token

• This value-added service can be used to enable service partitioning (higher efficiency)

Challenges in SON DevicesChallenges in SON Devices

• Robustness

– Admission Control– Load Scheduling

• Resource Allocation

– Concurrency Architectures

• Security

– Concurrency Architectures

• Performance Optimization

– Effectively leverage hardware co-processors

Challenges in building Service Oriented Challenges in building Service Oriented NetworksNetworks

– Scalability of the network with network entities

– Adaptation of network to changes in state

– Distributed policy-driven dissemination of network management data between nodes

– Distributed control of the network to connect consumers and providers while enforcing appropriate policies

Our Research in Control of Service Oriented Our Research in Control of Service Oriented NetworksNetworks

• Service networking and optimization– Service delivery pricing and optimization– Service-oriented networking– Architecture for service brokering and delivery– Measurement-based control of service centers or

“appliances”– Virtualized server characterization and control

• Networked virtual collaboration• Cross layer and wireless design

– WiFi and WiMax QoS modeling– Cross layer modeling, simulation, optimization– Mesh and multihop systems (WiFi, WiMax,

hybrid)

Our Research in Network Services

““ApplianceAppliance”” SchedulingScheduling

Optimal Resource AllocationOptimal Resource Allocation

General Formulation General Formulation –– II• Income or Utility Component

– Maximize utility charge• Cost Component

– Minimize delay-incurred cost

Networks of SON AppliancesNetworks of SON Appliances

MBORA Distributed VersionMBORA Distributed Version

Distributed MBORA: SupplierDistributed MBORA: Supplier’’s Utilitys Utility

Approach Using Dual DecompositionApproach Using Dual Decomposition

Algorithm Based on DecompositionAlgorithm Based on Decomposition

Alternative: GaussAlternative: Gauss--Seidel IterationsSeidel Iterations

Algorithm based on GaussAlgorithm based on Gauss--SeidelSeidel

TwoTwo--Node Example of GaussNode Example of Gauss--SeidelSeidel

Distributed MBORA: EvaluationDistributed MBORA: Evaluation

• Used deterministic network calculus for end-to-end delay

• Recently used stochastic calculus providing tighter bound

• Approximate -- need better formula to include processing delays

• Gauss-Seidel versus Dual Decomposition

• Working on better understanding and more alternatives

Networked Virtual CollaborationNetworked Virtual Collaboration

Cloud computing & Virtual Collaboration

Enterprises are moving towards the application of Virtual Worlds for internal deployment Virtual Worlds enable ubiquitous presence and virtual collaboration Apply same paradigm in education:Access applications via a virtual world Synergistic work

and parallelizationStudent 1: MatLab; Student 2: OPNET and vice-versa

DE students will interact with their colleaguesNo commute needed for students working in industry

VIRTUAL COMPUTING LAB (VCL)

“The Virtual Computing Lab (VCL) is a remote access service that allows to reserve a computer with a desired set of applications for yourself, and remotely access it over the Internet”

• Users have remote desktop access to machines loaded, on demand, with the desired software.• Anytime-anywhere access to applications, transparent to users.• Ease of system configuration and management, and scalability.

Does not support collaboration among users, yet!

VCL 3.0: a motivating example

• Users request their applications from VCL• An image of a virtual world with those apps is created• Remote connections are created to those apps from inside the world• Resources to virtual machine are given according to socio-technical

characteristics of the group members

Allocating cloud resources

• Which virtual machines should be placed for execution?

• How do we optimally allocate cloud resources?

Social-awareness

Resource Allocation to Virtual Collaboration Environments

Ultimate Goal: Socio-Technical Response Surface & Optimization

Social Distance – Connectivity Graph

• Construction of connectivity graph– Bandwidth availability of each user– Physical distance (implies communication delay)– Business distance– Group size, level of trust between collaborators, etc could also be used

• Use of graph’s diameter to differentiate between different connectivity graphs• Main idea: assign more resources to group with smaller social-distance• Larger social-distance: conditions not favoring high collaboration quality

Optimal Resource Allocation• 3D/2D Knapsack placement problem:

CPU

Mem

ory

p1=10

p2=5

p3=1

Optimal Resource Allocation (cont’d)

• Optimally allocate excess cloud resources:

Measuring Virtual Presence

Analyze social networking patterns in relation to communication patternsMeasure degree to which VW creates a sense of virtual

presence in the userAssess collaboration quality in VWs Evaluate network traffic in relation to social networking

and communication patterns (e.g., chat communication, voice, video)

Trials in Class:Matlab Doubly Virtualized inside Wonderland

Student Teams Collaborate inside Wonderland

Measuring Virtual Presence (Wonderland/Matlab) A group-based collaboration exercise in Wonderland

Matlab exercise

Data collected from 120 students (Spring 2010, ECE 220) Measures include:

Average time per solution Individual contribution to team performance Perception of virtual presence, communication mode (voice/chat),

collaboration quality

Completed

Collaborative Virtual

Presence:

ELEMENTS

DATA COLLECTION

Survey of Educational/Collaborative Experience

Measuring Virtual Presence (Wonderland/Matlab)• CPU utilization analysis during collaboration:

CPU, Memory and Network Monitored

Measuring Virtual Presence (Opensim/Maze) A negotiation and planning game in OpenSimLOST (in 3D maze)The game requires players to collaborate (lead/follow) in

order to meet a common objective under time constraints

• Repeat experiment with different levels of:– CPU (using cpulimit command)– Memory (using ulimit command) – Bandwidth shaping (using the trickle tool)

• Obtain objective functions for our optimization problem• Define the weights for the edges of our connectivity graph

Measuring Virtual Presence (cont’d)

Trials with Programmable Bots

• Use programmable bots as subjects for maze traversal– A guide bot instructs the lost bot how to “escape” the maze

• Vary the amount of server's communication bandwidth– Introduce dummy bots who chat with each other– Limit allocated bandwidth using Linux tools

• Vary the concurrent in-world participants• The metrics which we can capture:

– Frames per Second reported by server– Maze completion time

• Bots communicate using IM chatting• Future extension: emulate voice communication• (Server specifications: 2.8Ghz, 16 core, 64 bit machine, 16 GB

RAM)

Bot Results (IM communication)

• Time completion vs. Concurrent in-world users

• Frames per second vs. Concurrent in-world users

0

50

100

150

200

250

25 37 50 60 75 82 90

Concurrent Users

Trav

ersa

l Tim

e (s

ecs)

0

510

1520

25

3035

4045

50

Fram

es P

er S

econ

d (F

PS)

Time (secs)FPS

• Time completion vs. Available bandwidth

Trickle bandw idth

0

20

40

60

80

100

120

140

160

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50 100 150 200

Available Bandwidth

Trav

ersa

l Tim

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Trickle bandw idth

Contributions so far

• Optimal and dynamic allocation of cloud resources (e.g., CPU, memory, network bandwidth)

• Why consider presence status of users– Going towards social awareness– Introducing social distance and connectivity graphs

• Applications: cloud computing & virtual worlds– NCSU’s VCL– Amazon EC2

Summary

Social-aware optimization framework Motivation: Resource allocation of cloud resources to virtual machines that host virtual collaboration environments User's presence perception needs to be correlated with tangible resources (CPU, memory, bandwidth) Future work: Continue trials and experiments to: Find suitable utility functions per resource Investigate other important parameters to be used in the

graph weight function

Next Steps & Future Plans - IModel patterns/bundles as service-oriented network for deployment in CloudBurst (IBM DataPower appliance)

•Analyze network traffic, CPU patterns (also, power consumption?)

•Obtain the resource requirements of virtual images according to type of application used and participant social/business type•Use above information in the virtual machines placement problem

Continue to collect scaling data from botsSimulation (demo)Measure maze completion timeMeasure Frames Per SecondChange # of concurrent usersChange CPU/memory/bandwidth

Next Steps & Future Plans - II

• Placement problem–Add the green dimension: place virtual machines to also

account for their power consumption–Use physical space, cooling and power constraints

• Smart Grid extension? Energy appliances?

Network-Enabled Collaboration for Innovation

Virt

ual P

ublic

Sch

ools

(K-1

2)

Art

City

SSM

E C

omm

unity

Serio

us G

amin

g

Med

ical

Tec

hnol

ogy

Social Innovation

VIRTUAL ORGANIZATIONS

Industry/University Commercial Innovation

Centennial Living Labs

Virtual RTP

Virtual Proximity: Testing & Implementation

VIRTUAL WORLDS AND 3Di

COLLABORATION PROTOCOLS

Enabling Mechanisms

NETWORK & MIDDLEWARE

Ope

n So

urce

S/W

(Jaz

z, V

CL)

Partners:

• Autonomic service delivery platform for the Arts• Enabling artistic virtual organizations and remote interactions by use

of high speed networking and on-demand service delivery.• Combine network services with virtual collaboration research, and

with hands-on, “living lab” setting on campus (immersive Art Village in dorm, Centennial trials and pilot event in EBII).

• Use Centaur lab as hub for connectivity.• RENCI and other telepresence and mixed reality facilities (e.g.,

Cisco)• Use-cases: wireless-based mobile gaming and virtualized dance

activities: also serve as sources of system performance and workload measurements and analysis.

• Measurement phase followed by a design phase, where the algorithms and protocols in Nortel-sponsored wireless mesh trial can be adapted for optimized performance in real-life setting.

• Our work on service-delivery platforms and resource allocation will be tested and tried in this environment and its performance will be tuned accordingly.

ArtCity: Network-Enabled Art

Wireless Positioning and Awareness

• Partnering with Nortel, Carleton University in Ottawa, Canada, and Cisco

• Analysis: Cross-layer modeling of performance• Trial: Wireless mesh testbed in EB-II• Benefits from Centennial campus wireless network

• Emphasize location, distances and “aware”network

• Building Wi-Fi positioning system in EB-II• Stage serious gaming trials

Nortel SIP and Next Gen Services over Mesh Wireless

Social Distance Aware Utility Functions• Motivation

– Utility Functions defined almost always at the transport layer

– Social distance of a user to her peers affects desired utility

• Approach– Formalize the type of distances (social, effective)

between related entities in a social graph– Define and solve the Social Distance Aware Resource

Allocation Problem.

Social Distance Aware Resource Allocation

• Network is explicitly made aware of the resource requirements.• Resource allocation decisions happen in terms of parameter

tuning at corresponding protocol layers.• Better resource allocation decisions possible due to social

context awareness.

Examples of Social Distances

Social Distance Aware Utility Function

Resource Allocation in a WLAN• Resource Allocation

– Access Points are aware of the traffic demand.– 802.11e compliant AP’s and nodes are necessary for

QoS differentation.– AIFS, CWMin are among the parameters that can be

controlled.– We use AIFS as the control parameter for our

simulations in ns-2.– The end user application is VoIP.

• Modified VoIP Utility Function– MOS*(R) = MOS(R) - ( - 1)

Delay and Loss Matrices

Resource Allocation Algorithm for WLAN

CHOOSE AIFS

Max Unew(AIFS, ) – cost(AIFS, )Subject to

1<=AIFS<=71<= <= 3

• For our example, we consider social distances to be chosen from the set {1,2,3} with 1 signifying the highest priority.

• Control parameter = AIFS

Algorithm computesloss and delay for the

current mix of calls afteradding this new call

Loss (L)And

Delay (D)matrices

or

Aware Allocation Pseudo Code

Distance-Aware vs. Plain 802.11e

Call Capacity Total Utility

Real World Implementation• Effective Distance

– To measure this quantity, applications need to become location-aware.

– Social distance awareness is also necessary. But this is usually easier, since it is determined by the user herself.

• Our Solution– Implement a Wi-Fi Positioning System for locating

devices when inside buildings (EB-II).– Devices are GPS-enabled (iPhones/Android devices)

to facilitate positioning when outside.

• Steps of positioning system1. Client retrieves data

(Visible Access Points and their RSSI)

• 2.Client sends data to server

• 3-6.Server enters data into database, uses algorithm to calculate position

• 7. Other Clients open map using browser and get the location information from the server

Wi-Fi Positioning System

The V911 Application – A Location Aware Application• Emergency response application, with the

locations being determined using WPS.

User Helper

WPS Server

Ongoing Work• Implement applications which have a social

context in addition to being location-aware.– A game with 4 teams competing against each other.

• Perform trials with devices spread out both indoors and outdoors.

• Measure network performance metrics (delay, loss, jitter etc).

• Relate the user’s experience to the metrics –culminating in the definition of the social distance aware utility function for this application.

Traffic Modeling

Motivation:Better models required for performance studies in:

QoS, admission control, testing

Goals:Accurate models of statistical behavior of traffic

Computationally efficient models of traffic

Purpose of Traffic Measurements

Capture traffic from different places across research and education campuses.

Identify emerging traffic patterns. Deconstruct and analyze the intersecting

networking and social distances (K-20). Calculate end-to-end delay and packet loss

rate based on traffic measurements.

Design of the Broadband NGN Architecture

Services

Geographic Single-Edge Multi-Edge

Centralized All services flow through single device, located in a centralized PoP

Separate devices for various services. Could be service specific edge, or common per-subscriber

PEP but on multiple systems

DistributedAll services flow through a single device, distributed in the architecture close to the

subscriber

Some services are produced on distributed devices, whereas other

services are produced centrally

Clustered: Multiple Edge routers will support one serviceUn-clustered: One Edge router for one service

Quadruple Play Service Delivery Architecture

= ?Triple Play Services (Voice, Video, Internet) + Broadband Wireless Access

Network Design Problems• Horizontal Design

– Location Problems: “Pure” topological design problems where demands are not taken into account. More of where to place the devices

– Dimensioning Problems: Network Design Problems that take into account the demand volumes. More on dimensioning the locations and interconnections

• (Proposed) Vertical Design– Optimally Process next generation services/flows– The network devices may support multiple layers (IP

routing, SON)

Centralized Single Edge vs Multi-Edge Architecture

• Single Edge– All types of traffic are

backhauld to the BNGs located at a single PoP

– Subscriber termination functionality, and IP Qos policies are executed in the BNG

– Multicast Traffic for video is transmitted from the edge router (L2 multicast VLAN)

• Multi Edge– Different types of edge

routers (BNG/Video BNG and business at MSE)

– Clustered: Multiple number of BNGs and MSEs at the same PoP

– Benefits from incumbency (easier to evolve)

Distributed Single vs Multi Edge Architecture

How to measure traffic?Collection of traffic statistics is currently performed• Flow monitor, e.g., Cisco NetFlow• Sophisticated Network Monitoring EquipmentThe port based measuring system (identify applications

based on ports) captures only 60% of trafficOur implementation for netflow data:

Network monitoring through Netflow data

• Proof of concept for netflow data: Classification Apps are on average ~60%• http://benediction.ece.ncsu.edu/ece480/

Traffic hiding into similar ports• Applications do not belong to static ports• Maybe they belong to P2P applications that use dynamic

ports (even FTP does initiation at 21 and jumps to 20; Skype also uses 80)

• Maybe they belong to media applicationsIt is hard to find out who generated what unless expensive stateful (resource

draining) DPI monitors are used

• The proposed implementation• Level 1: Signature checking in Packet files (processor intensive)

and store in hash table• Level 2: Check every flow in the hash table• Worst case: Check for Port and ToS on the header

Comparison of aggregate traffic and signature based classification

Our Vision for “Weather Maps”• Share statistics among

NCREN research community• Anonymize data• Multiple measuring points• Per link traffic/Per application

(taking a step forward from regular MRTG/CACTI as shown in the figure)

• http://benediction.ece.ncsu.edu/

Methodology for NCREN Related Campuses

• Detailed per-application capture in various locations of NCSU campus.

• Analysis of the intersecting social, networking and geographical distances of K-20 users.

• Identify architectural modifications to improve the overall performance of the network.

• Propose how new wireless access technologies and mobility patterns affect the traffic.

• Analyze flow of IP packets through an aggregation network to calculate probability of end-to-end delay and packet loss.

Peer2peer Networks (e.g., eDonkey, emule, Gnutella, Kazaa)

• Peer-to-peer file sharing applications have evolved to one of the major traffic sources in the Internet.

• In particular, the eDonkey file sharing system and its derivatives are causing high amounts of traffic volume in today's networks.

• The eDonkey system is typically used for exchanging very large files like audio/video CDs or even DVD images.

• Peer2Peer Network based on “torrents”: function different than other P2P programs. A large file is divided into chunks. Peers interested in the same file self-organize into a torrent and peers exchange file chunks with each other.

Modeling Game Traffic• In order for a game with client-server topology to be effective it must

accommodate huge lag (ping, roundtrip delay) and loss• Methods to solve the lag in games:1. Client-side prediction of the game state i.e. movements of objects

and other players2. Combine movement with inertia or reducing maximum velocity of

objects prediction is even more effectiveThe most robust games tolerate lag up to 1 sec (mean value is usually

around 200ms) and loss up to 40%

Quality based on Ping times<50ms Excellent<100ms Good>150ms Bad

Generally is based on the robustness and type of the game (there are games that accommodate lag of 200ms)

Modeling Games an Open Issue• Games have different requirements (a 3D first

shooter game requires very low, but 2D strategy games can accommodate higher lag)

• Clients do not act independently (social networking aspects)

• Server traffic per client is dependent on all clients (many models assume independency)

• Each game sends different messages and there is not currently a general social formalization.

• Will 3D Virtual Worlds become the “Web 3.0”?

Optimization ModelThe following linear integer programming problem needs to be solved:

, , 3 3 2 2,

{ , , }min

[1 ,10 ], [ 2, 3]

x c x c L L L Ln n t n

n i j k c x tco Y co Y co Y

c G G x L L

• Number of interfaces with capacity c at location n• Number of elements (x=L2 switch, x=L3 router)

Constraints• Quantitave (Elements/Interfaces)• Qualitative (placement of functionalities)• Number of Ports/Interfaces• Line Card Capacity per Network Element• Interface Capacity (1Gbps/10Gbps)• Traffic Flow constraints (local or non-local)

,x cnY

xnY

Capital Expenditures (CapEx)

• Argument around number and cost of devices• Pure L2 aggregation networks combined with

centralized edge architectures seem to have cost advantages– BUT: how to handle multicast traffic in a pure L2 aggregation

network?– Motivates IP-enabled aggregation networks

• Number of IP enabled devices in centralized and distributed architectures of same order of magnitude– Thus Capex in a similar range– CapEx of distributed architecture comparable to centralized

architecture with IP-enabled aggregation infrastructure

• Our work focuses on:– Capturing “presence” and location awareness– Quantifying socio-technical interactions– Characterizing workload spatially and temporally– Optimizing over multiple resources, across

layers

Summary: Service Oriented Networks

SON and Convergent Nets

Workload Aggregation and Distributed

Delivery

Social Apps and Virtual

Collaboration

“Cloud”