networked 3-d virtual collaboration in science and education ......networked 3-d virtual...
TRANSCRIPT
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Networked 3-D Virtual Collaboration in Science and Education: Towards ‘Web 3.0’
(A Modeling Perspective)
Michael Devetsikiotis Professor of Electrical & Computer Engineering
North Carolina State University [email protected]
http://www4.ncsu.edu/~mdevets
Collaborators: Mitzi Montoya, George Michailidis NC State Team: Michael Kallitsis, Vineet Kulkarni,
Ioannis Papapanagiotou, Nilesh Gavaskar, Yan Wang
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• 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
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• 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”
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• 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
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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
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• 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
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• 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
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• 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)
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• Robustness
– Admission Control – Load Scheduling
• Resource Allocation
– Concurrency Architectures
• Security
– Concurrency Architectures
• Performance Optimization
– Effectively leverage hardware co-processors
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– 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
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• 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
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• Income or Utility Component – Maximize utility charge
• Cost Component – Minimize delay-incurred cost
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• 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
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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 parallelization Student 1: MatLab; Student 2: OPNET and vice-versa
DE students will interact with their colleagues No commute needed for students working in industry
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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!
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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
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Allocating cloud resources
• Which virtual machines should be placed for execution?
• How do we optimally allocate cloud resources?
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Social-awareness
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Resource Allocation to Virtual Collaboration Environments
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Ultimate Goal: Socio-Technical Response Surface & Optimization
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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
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Optimal Resource Allocation • 3D/2D Knapsack placement problem:
CPU
Mem
ory
p1=10
p2=5
p3=1
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Optimal Resource Allocation (cont’d)
• Optimally allocate excess cloud resources:
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Measuring Virtual Presence
Analyze social networking patterns in relation to communication patterns Measure degree to which VW creates a sense of virtual
presence in the user Assess collaboration quality in VWs Evaluate network traffic in relation to social networking
and communication patterns (e.g., chat communication, voice, video)
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Trials in Class: Matlab Doubly Virtualized inside Wonderland
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Student Teams Collaborate inside Wonderland
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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
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Completed
Collaborative Virtual
Presence:
ELEMENTS
DATA COLLECTION
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Survey of Educational/Collaborative Experience
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Measuring Virtual Presence (Wonderland/Matlab) • CPU utilization analysis during collaboration:
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CPU, Memory and Network Monitored
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Measuring Virtual Presence (Opensim/Maze)
A negotiation and planning game in OpenSim LOST (in 3D maze) The game requires players to collaborate (lead/follow) in
order to meet a common objective under time constraints
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• 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)
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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)
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Bot Results (IM communication)
• Time completion vs. Concurrent in-world users
• Frames per second vs. Concurrent in-world users
• Time completion vs. Available bandwidth
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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
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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
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Next Steps & Future Plans - I Model 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 bots Simulation (demo) Measure maze completion time Measure Frames Per Second Change # of concurrent users Change CPU/memory/bandwidth
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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?
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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:
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• 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
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Wireless Positioning and Awareness
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• 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
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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.
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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.
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Examples of Social Distances
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Social Distance Aware Utility Function
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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) - β (χ - 1)
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Delay and Loss Matrices
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Resource Allocation Algorithm for WLAN
CHOOSE AIFS
Max Unew(AIFS, χ) – cost(AIFS, χ) Subject to
1<=AIFS<=7 1<= χ <= 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 computes loss and delay for the
current mix of calls after adding this new call
Loss (L) And
Delay (D) matrices
or
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Aware Allocation Pseudo Code
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Distance-Aware vs. Plain 802.11e
Call Capacity Total Utility
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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.
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• Steps of positioning system 1. 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
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The V911 Application – A Location Aware Application • Emergency response application, with the
locations being determined using WPS.
User Helper
WPS Server
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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.