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Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)
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Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective)

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Professor Michael Devetsikiotis gave a lecture on "Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective) " in the Distinguished Lecturer Series - …

Professor Michael Devetsikiotis gave a lecture on "Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective) " in the Distinguished Lecturer Series - Leon The Mathematician.

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  • 1. 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 mdevets@ncsu.edu http://www4.ncsu.edu/~mdevets Collaborators: Mitzi Montoya, George Michailidis NC State Team: Michael Kallitsis, Vineet Kulkarni, Ioannis Papapanagiotou, Nilesh Gavaskar, Yan Wang
  • 2. Strategic Trends and Overview• 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
  • 3. Overview: Service Oriented Networks• Our larger goals: – Capture “presence” and location awareness – Quantify socio-technical interactions – Characterize workload spatially and temporally – Optimize over multiple resources, across layers SON and Convergent Nets “Cloud” Social Apps and Workload Aggregation Virtual and Distributed Collaboration Delivery
  • 4. Research Topics• 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
  • 5. 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 Services/Innovation Technology/Resources Flexibility Congestion, QoS
  • 6. Overview of Service-Oriented 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
  • 7. Service-Oriented Networking: “Awareness”
  • 8. Delivery: SON Appliances
  • 9. Service-Oriented Networking Functions: Functional 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
  • 10. Service-Oriented Networking Functions: Content-Based 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)
  • 11. Challenges in SON Devices• Robustness – Admission Control – Load Scheduling• Resource Allocation – Concurrency Architectures• Security – Concurrency Architectures• Performance Optimization – Effectively leverage hardware co-processors
  • 12. Challenges in building Service OrientedNetworks – 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
  • 13. Our Research in Control of Service Oriented Networks
  • 14. Our Research in Network Services• 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)
  • 15. “Appliance” Scheduling
  • 16. Optimal Resource Allocation
  • 17. General Formulation – I • Income or Utility Component – Maximize utility charge • Cost Component – Minimize delay-incurred cost
  • 18. Networks of SON Appliances
  • 19. MBORA Distributed Version
  • 20. Distributed MBORA: Supplier’s Utility
  • 21. Approach Using Dual Decomposition
  • 22. Algorithm Based on Decomposition
  • 23. Alternative: Gauss-Seidel Iterations
  • 24. Algorithm based on Gauss-Seidel
  • 25. Two-Node Example of Gauss-Seidel
  • 26. Distributed 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
  • 27. Networked Virtual Collaboration
  • 28. Cloud computing & Virtual Collaboration Enterprises are moving towards the application ofVirtual Worlds for internal deployment Virtual Worlds enable ubiquitous presence andvirtual 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
  • 29. VIRTUAL COMPUTING LAB (VCL)“The Virtual Computing Lab (VCL) is a remote access servicethat allows to reserve a computer with a desired set of applicationsfor 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!
  • 30. 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
  • 31. Allocating cloud resources • Which virtual machines should be placed for execution? • How do we optimally allocate cloud resources?
  • 32. Social-awareness
  • 33. Resource Allocation to Virtual CollaborationEnvironments
  • 34. Ultimate Goal: Socio-Technical Response Surface & Optimization
  • 35. 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
  • 36. Optimal Resource Allocation• 3D/2D Knapsack placement problem: p3=1 Memory p2=5 p1=10 CPU
  • 37. Optimal Resource Allocation (cont’d)• Optimally allocate excess cloud resources:
  • 38. Measuring Virtual Presence Analyze social networking patterns in relation tocommunication 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)
  • 39. Trials in Class:Matlab Doubly Virtualized inside Wonderland
  • 40. Student Teams Collaborate inside Wonderland
  • 41. 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
  • 42. Collaborative Virtual Presence:ELEMENTS DATACOLLECTION ted Co mple
  • 43. Survey of Educational/Collaborative Experience
  • 44. Measuring Virtual Presence(Wonderland/Matlab)• CPU utilization analysis during collaboration:
  • 45. CPU, Memory and Network Monitored
  • 46. 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
  • 47. Measuring Virtual Presence (cont’d)• 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
  • 48. 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 servers 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)
  • 49. Bot Results (IM communication) 250 50 45 Frames Per Second (FPS)• Time completion vs. 200 40 Traversal Time (secs) 35 Concurrent in-world 150 30 25 users 100 20 15 50 Time (secs) 10• Frames per second vs. 0 FPS 5 0 Concurrent in-world 25 37 50 60 Concurrent Users 75 82 90 users Trickle bandw idth 180 160 140 120 Traversal Time 100• Time completion vs. 80 60 Trickle bandw idth Available bandwidth 40 20 0 50 100 150 200 Available Bandw idth
  • 50. 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
  • 51. Summary Social-aware optimization framework Motivation: Resource allocation of cloud resources tovirtual machines that host virtual collaboration environments Users presence perception needs to be correlated withtangible 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
  • 52. Next Steps & Future Plans - IModel patterns/bundles as service-oriented network fordeployment in CloudBurst (IBM DataPower appliance)•Analyze network traffic, CPU patterns (also, powerconsumption?) •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
  • 53. 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?
  • 54. Network-Enabled Collaboration for InnovationEnabling VIRTUAL ORGANIZATIONSMechanisms Partners: Open Source S/W (Jazz, VCL) Virtual Public Schools (K-12)COLLABORATION Medical Technology SSME Community Serious GamingPROTOCOLS Art CityVIRTUAL WORLDSAND 3DiNETWORK &MIDDLEWARE Social Innovation Industry/University Commercial InnovationVirtualProximity: Centennial Living LabsTesting &Implementation Virtual RTP
  • 55. ArtCity: Network-Enabled Art• 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.
  • 56. Wireless Positioning and Awareness
  • 57. Nortel SIP and Next Gen Services over Mesh Wireless• 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
  • 58. 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.
  • 59. 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.
  • 60. Examples of Social Distances
  • 61. Social Distance Aware Utility Function
  • 62. 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)
  • 63. Delay and Loss Matrices
  • 64. Resource Allocation Algorithm for WLAN • 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 CHOOSE AIFS Max Unew(AIFS, ) – cost(AIFS, ) Subject to 1<=AIFS<=7 1<=  <= 3 Algorithm computes Loss (L) loss and delay for the And current mix of calls after Delay (D) matrices adding this new call or
  • 65. Aware Allocation Pseudo Code
  • 66. Distance-Aware vs. Plain 802.11e Call Capacity Total Utility
  • 67. 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.
  • 68. Wi-Fi Positioning System • 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
  • 69. The V911 Application – A LocationAware Application• Emergency response application, with the locations being determined using WPS. WPS Server User Helper
  • 70. 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.
  • 71. 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
  • 72. 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.
  • 73. Design of the Broadband NGN Architecture Quadruple Play Service Delivery Architecture = ? Triple Play Services (Voice, Video, Internet) + Broadband Wireless Access Services Single-Edge Multi-Edge Geographic Separate devices for various All services flow through single device, services. Could be service specific Centralized located in a centralized PoP edge, or common per-subscriber PEP but on multiple systems All services flow through a single device, Some services are produced on Distributed distributed in the architecture close to the distributed devices, whereas other subscriber services are produced centrally Clustered: Multiple Edge routers will support one service Un-clustered: One Edge router for one service
  • 74. 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)
  • 75. Centralized Single Edge vs Multi-EdgeArchitecture • 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)
  • 76. Distributed Single vs Multi Edge Architecture
  • 77. 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:
  • 78. Network monitoring through Netflowdata• Proof of concept for netflow data: Classification Apps are on average ~60%• http://benediction.ece.ncsu.edu/ece480/
  • 79. 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 applications It 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
  • 80. Comparison of aggregate traffic andsignature based classification
  • 81. 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/
  • 82. 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.
  • 83. 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 todays 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.
  • 84. 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 BadGenerally is based on the robustness and type of the game (there are games that accommodate lag of 200ms)
  • 85. 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”?
  • 86. Optimization ModelThe following linear integer programming problem needs to be solved:   min   n{i , j , k }  c x co x ,cYn x ,c   co L 3Y L 3n ,t  co L 2Yn L 2  t  c  [1G,10G ], x  [ L 2, L3]• Yn x ,c Number of interfaces with capacity c at location n• Yn x 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)
  • 87. 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
  • 88. Summary: Service Oriented Networks• Our work focuses on: – Capturing “presence” and location awareness – Quantifying socio-technical interactions – Characterizing workload spatially and temporally – Optimizing over multiple resources, across layers SON and Convergent Nets “Cloud” Social Apps and Workload Aggregation Virtual and Distributed Collaboration Delivery

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