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A Distributed Bandwidth Broker based on Intelligent Agents.

This research outlines a Distributed Bandwidth Broker based on intelligent agents to manage network resources efficiently, re-route traffic, and allocate bandwidth effectively. It addresses the growing data traffic challenges in modern communication networks, proposing solutions for high-end service quality and optimal resource allocation. The study delves into MPLS and DiffServ scenarios, emphasizing the importance of implementing intelligent agents and available Internet tools for efficient network management. The work highlights the critical need for resource management due to the escalating demand for data services, surpassing the current network capacities. It discusses various strategies such as re-routing, bandwidth re-allocation, and network provisioning to enhance end-to-end Quality of Service. The proposed system aims to optimize network performance, ensure traffic isolation, and improve overall user experience in diverse network environments, including core networks and access networks. 8

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A Distributed Bandwidth Broker based on Intelligent Agents.

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  1. A Distributed Bandwidth Broker based on Intelligent Agents. Jose L Marzo <marzo@eia.udg.es>http://eia.udg.es/~marzo Universitat de Girona, Spain

  2. Outline • Introduction: towards a QoS Internet • Proposal: resource management • Why: traffic > network capacity • Where: core network • What: re-routing and bandwidth re-allocation • How: intelligent agents (+ available Internet tools) • MPLS & DiffServ scenario • Conclusions and future work

  3. Outline • Introduction: towards a QoS Internet • Proposal: resource management • Why: traffic > network capacity • Where: core network • What: re-routing and bandwidth re-allocation • How: intelligent agents (+ available Internet tools) • MPLS & DiffServ scenario • Conclusions and future work

  4. QoS Internet Faster/CheaperTechnologies End to end level QoS Architectures QoS“Internet” Current InternetHigh rate of growing Node to node level Multimedia Demand Lack of Qualityof Service

  5. End to end level QoS Architectures QoS“Internet” Current InternetHigh rate of growing Node to node level Heidelberg QoS model TINA Heterogeneous environments (Services, protocols, Technologies,…) Small set of discrete QoS & Traffic classes. Lack of Qualityof Service OSI QoS Framework XRMExtended Integrated Model MASI Path reservation &resource reservation Desirable:high resourceallocation OMEGA TENET Traffic isolation viascheduling and policing Int-Serv Architecture End SystemQoS framework Admission control QoS Architecture Vendor Solutions(CISCO, JUNIPER, FORE,…) “All structural levels have to be taken into account” QoS architectures

  6. End to end level QoS Architectures QoS“Internet” Current InternetHigh rate of growing Node to node level Lack of Qualityof Service End to end level • Application level • Bandwidth reduction (compression, codification,..) • Shaping filters (at sender edge) • Timing filters (at receiver edge) • Variable bit rate encoders • Multimedia standards (H-323,…) • Real Time Stream Protocol (RTSP) • Real Time Protocol (RTP) • TCP improvements (ECN)

  7. End to end level QoS Architectures QoS“Internet” Current InternetHigh rate of growing Node to node level Lack of Qualityof Service Node to node level

  8. Outline • Introduction: towards a QoS Internet • Proposal: resource management • Why: traffic > network capacity • Where: core network • What: re-routing and bandwidth re-allocation • How: intelligent agents (+ available Internet tools) • MPLS & DiffServ scenario • Conclusions and future work

  9. Resource management, why?(some key points) • In 1998, data traffic outmatched voice traffic • The capacity of the computation systems (switching) doubles every 18 months (Moore’s law) • The transmission capacity doubles each 6-9 months • Wavelength fiber technologies will reach 1024 wavelengths, 40 Gbit/s capacity each = 40,000 Gbit/s in just one fiber!) • Data traffic doubles every 3-4 months!!! The growing of traffic is greater than the capacity of the network, therefore, resource management is required.

  10. QoS InternetLogical stack Framework Application-Service Application-Service OS + QoSRTSP, RTP,.. OS + QoSRTSP, RTP,.. RSVP RSVP (Tunneled) TCP-UDP IPIntServ TCP-UDP IPIntServ IP IntServ DiffServ + MPLS (IP) DiffServ + MPLS IP IntServ DiffServ + MPLS Core Network Access Network (LAN, CableModem, ISDN-XDSL, ATM, Wireless) Access Network (LAN, CableModem, ISDN-XDSL, ATM, Wireless) (D)WDM SONET ATM Heterogeneous &Flow performance oriented “Homogeneous” &network performance oriented Heterogeneous &Flow performance oriented

  11. 3) change physical configuration physical configuration 4) Spare capacity management Fault Management Path Restoration new allocation 5) 2) resource alloc. Restoration Fault Accept/reject connection 6) 1) connect request Network Proposal: resource management components&time scales Network Provisioning Large Routing day hour Performance Management Bandwidth Re-allocationRe-routing min sec Flow Admission Control ms Short User Pool Time scales

  12. End-to-endLevel End -to-end Level Terminal Terminal Server Terminal Distributed Intelligent Agents Where, what & how to do resource management? Network level Routing Strategies AccessNetwork AccessNetwork CoreNetwork A CoreNetwork B Shaping & Policing Admission Control MPLS Fast Label Switching / [ATMVirtual Path Network] DiffServ Spare capacity management & path restoration Bandwidth re-allocation & re-routing

  13. Virtual Networks

  14. MPLS Label Switching Paths (LSPs) DiffServ class (EF) DiffServ class (AE) DiffServ class (BE) Virtual Network example:DiffServ & MPLS case Physical Network

  15. 2 path 1 - congested 1 path 2 - under utilized 1 2 2 1 2 1 X Bandwidth re-allocation 3 4 1 1 2 3 4 2 Re-routing 3 4 path 1 - congested path 2 - not enough capacity to expand path 1 Performance managementTraffic Engineering

  16. Pre-planned restoration B B B Active path Fault Fault A C A C A C Bandwidth capture message E D E D E D Backup Paths Fault management Dynamic restoration Possible Alternatives B Active path B B Broadcast Messages C A A C A C Fault Fault E D E D E D Data Flow

  17. Intelligent Agents Systems • Main properties • Autonomy: agents operate without the direct intervention of humans or others • Social ability: agents interact or co-operate with other agents (and possibly humans) • Reactivity: agents perceive their environment and respond in a timely fashion to changes • Pro-activeness: agents are able to exhibit goal-directed behaviour by taking the initiative. • Other possible attributes/assumptions • Mobility, the ability of an agent to move around a network • Veracity, they do not knowingly communicate false information • Benevolence, they do not have conflicting goals • Rationality, an agent will act in order to achieve its goal

  18. Node 2 P2 M2.1 M2.3 pl2 M2.l2 M2.l3 1 Node 3 3 2 pl3 M1.l2 M3.l3 M1.1 M3.3 M1.2 P1 P3 M3.2 M1.5 M3.1 pl4 M1.l1 M3.l4 pl1 1 5 Node 1 M4.l1 M4.l4 M4.5 M4.1 P4 Node 4 Pn Network Planning agent at the node n Network Monitoring agent x at the node n Mn .x n Path n (local label) ln Physical Link pln (global label) Monitoring and Management action Multi-Agent System Architecture (II)

  19. Outline • Introduction: towards a QoS Internet • Proposal: resource management • Why: traffic > network capacity • Where: core network • What: re-routing and bandwidth re-allocation • How: intelligent agents (+ available Internet tools) • MPLS & DiffServ scenario • Conclusions and future work

  20. MPLS & DiffServ & ATM scenario • Objective • Resource management • Avoid functional overlapping • Role distribution • Routing/forwarding: MPLS • QoS: DiffServ • Bandwidth Re-allocation • L3(2.5) level MPLS resource allocation • ATM L2, Virtual Path BW re-allocation

  21. LSRb I H G F A LSRx E B D K C J LSRa load prediction time BW re-allocation example: MPLS level 2.5 BW re-allocation E P ME-aknowledgedatabase ME-b ME-a

  22. Re-routing example: ATM level 2 LSRb I H F D G A LSRx B K E C J Re-routing LSRa E P MPLS Layer ME-aknowledgedatabase LSPE VPG+F ATM layer ME-b ME-a VPE

  23. Conclusions and Future Work • Network performance and QoS problems, at the new IP based networks, has been introduced and its need justified. • To carry out some performance and fault management features a decentralised multi-agent (MA) system is proposed. • This MA-system can use existing, or future, “tools” to performance its features (i.e. re-routing and bandwidth re-allocation) • The scalability of the system has been analysed and it is quite acceptable. • This is a work in progress, more work to define specific approach functionality and on the simulation system has to be done.

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