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Dynamic Bandwidth Management in QoS aware IP Networks Committee: Dr. Paul Farrell Dr. Javed Khan Dr. Hassan Peyravi (Chair) Thesis Yasir Drabu Presentation Overview INTRODUCTION BACKGROUND WORK Related Work Proposed Model – Analysis and Algorithms SIMULATION Traffic Management - Active Queue Management (AQM), Scheduling Admission Control – Classification, Current Implementation and limitations. DYNAMIC BANDWIDTH MANAGEMENT (DBM) Application, Current Network Problems, QOS, Traffic Problem Definition and Research Goal. Setup - Topology, traffic models, parameters and scenarios. RESULTS CONCULSION and FUTURE WORK October 31, 2003 Dynamic Bandwidth Management 2 Applications – Changing Needs Conventional Apps Email, FTP, Telnet, etc Loss sensitive, High delay tolerance, jitter insensitive. The IP Network was designed for these. New Applications WWW started a new trend. Video, VoIP, Interactive/ Streaming Video, ecommerce, etc. Loss tolerant, delay sensitive, jitter sensitive to varying degrees. The IP Network was not designed for these. October 31, 2003 U T I L I T Y U T I L I T Y Bandwidth Traditional Applications Dynamic Bandwidth Management Bandwidth New Real-time Applications 3 Current Networks - Issues Inherent Problems Different traffic requirement, similar treatment. Signaling packets, Real-time packets, data packets, individual packets within a flow, all treated same. Ill behaved traffic hurts well behaved traffic. Unresponsive UDP flows dominate TCP flows. Congestion Control limited to end hosts. TCP is predominant means of congestion control. Changing/ Upgrading Difficult. Bottom line – Need Quality of service October 31, 2003 Dynamic Bandwidth Management 4 Quality of Service – QoS User QoS Application QoS Highly perceptional, hard to quantify. Applications change so do requirements. Network QoS Easy to quantify, well defined. All other QoS can be expressed in these terms. Metrics well defined. Availability Delay Delay Variation Throughput (Bandwidth) Packet Loss Rate. October 31, 2003 Fig: Core Network QoS metrics Dynamic Bandwidth Management 5 Network QoS – Approaches Best Effort Enhancements (RED, WRED, ECN) Pros: Implemented over existing infrastructure. Cons: No QoS guarantee Integrated Services Hybrid Best Effort Hard Qos Soft QoS Cost Differentiated Services Pros: High level of QoS Cons: Not Scalable, consistency issues, implementation a big problem. Complexity Pros: Scalable, incremental implementation Cons: QoS relative, unable to control flow misbehavior within aggregate. Constrain Based Routing Pros: Scalable, better network utilization Cons: Complexity (computational and space), state information coherence, routing stability. October 31, 2003 Courtesy Cisco Dynamic Bandwidth Management 6 Today’s IP QoS technology Technology Description Engineering Aspect RSVP DS Byte Out of Band Signaling In Band Signaling Signaling CAR (committed access rate) Classification and policing (application, protocol , DS Byte) Policing & Classification RED, WRED Weighted Random Early Detection Service class enforcement Congestion Avoidance WFQ, CBQ Weighted Fair Queuing Class-Based Queuing Queuing Policies Congestion Management and BW Allocation MPLS MPLS Diffserv IP+ATM QoS integration Leverage Layer2 October 31, 2003 Dynamic Bandwidth Management 7 Today’s Internet Traffic Conventional Traffic (Exponential, Smooth) Exponential like Voice Easier to analyze Concrete parameters arrival rate, queuing delay, etc. Can be simulated using Poisson's Distribution Actual Traffic (Self Similar, Bursty) Heterogeneous mix of data, voice and multimedia application Difficult to characterize. In some cases not possible to characterize. Effects of multiplexing Makes the aggregate more self similar Makes the traffic more exponential (contradicting) Is there a factor, that can be used to decide the actual effect? Can be simulated using Multiple Pareto distributions. October 31, 2003 Dynamic Bandwidth Management 8 QoS – Bandwidth Allocation Problem Fixed Bandwidth allocated for QoS guarantee Admission Control Requires a priori information about traffic characteristics. Traffic model does not accurately describe statistical behavior. User defined parameters may not accurately represent the actual traffic. LDR traffic compounds the issue. Some work in Call Admission Control – MBACs, little to no work in Aggregate Flow control. Weighted Scheduling – Weights associated based on admission rates. Bottom Line – Bandwidth Allocation is inefficient. October 31, 2003 Dynamic Bandwidth Management 9 Problem Definition and Goal “To design and develop a dynamic bandwidth management model for efficient utilization of a shared link in a QoS aware IP Network.” Design Guidelines: 1. Optimize bandwidth Utilization 2. Lower Loss rate 3. Non responsive UDP type flows can be controlled. Coexistence with Best effort 5. Minimizes bad drop/mark decisions. Fairness 4. Better Delay Vs Utilization trade off. Prevent starvation of BE traffic Scalable and Easily deployable October 31, 2003 The model has to be scalable on a huge network and be incrementally deployed. Dynamic Bandwidth Management 10 Background Work Three major components: Active Queue Management (AQM) Random Early Detect Exponential Weighted Measured Average (EWMA) Admission Control – Arrivals Scheduling – Service Allocation Longer Network Provisioning Days Hours Performance Management Minutes QoS Re-routing Bandwidth Reallocation Seconds Admission Control AQM Active Queue Management Milliseconds Scheduler Microseconds Admission Control October 31, 2003 Scheduling Dynamic Bandwidth Management Shorter Time Scale 11 Typical QoS Aware Interface Met erin g Classifier IN Admission Control S OUT Usually a QoS Enabled Edge Router has – Classifier, Admission controller, per class queue, scheduler. Optional Metering unit. October 31, 2003 Dynamic Bandwidth Management 12 Classification – Traffic Management Active Queue Management Non-Reservation Based Proactive FIFO PQ Reservation Based Router Centric FQ Policing Static WFQ WRR/CBQ DWRR Scheduling Back Pressure Traffic Management Adaptive Reactive AWRR/ACBQ ECN Host Centric October 31, 2003 Dynamic Bandwidth Management 13 Classification – Admission Control Extensive research on Call Admission Control. Direct Probing Host centric for more efficient bandwidth allocation. Packet Admission Control, limited work as compared to CAC. Early Rejection Packet Admission Control MBAC Slow Start AAC Router centric CAR Measurement Based Admission Control Call Admission Control Simple Sum Measured Sum Hoeffding Bounds Statistical Parameter Based Non-statistical Policy Based Admission Control October 31, 2003 Dynamic Bandwidth Management 14 Dynamic Bandwidth Management To over come the inefficient bandwidth allocation we use dynamic approach. Measure traffic conditions “online” and make admission and allocation decisions. Three approaches – closed loop, open loop and hybrid. Closed loop Queue Length Closed Loop Loss Dynamic Bandwidth Management Open Loop Delay State information used as feedback Prediction based on Past observations. October 31, 2003 Dynamic Bandwidth Management Rate Open Loop Hybrid 15 DBM – Proposed Architecture On QoS Enabled Router Interface: Introduce a controller Feedback from Token Bucket - Starvation Rate tells us rate of packet arrival Feedback from Queue – Average Queue Size tells us rate of packet departure. The Control has two components Adaptive Admission Control (AAC) Adaptive Class Based Queuing (ACBQ) Adjusted Token Rates r3 r2 r1 r0 B B B B 3 2 1 0 Controller w0 w1 w2 S w3 Adjusted Scheduler Weights Per Class Queue October 31, 2003 Dynamic Bandwidth Management 16 The Controller Components Dynamic Bandwidth Controller Adaptive Admission Control Adaptive Scheduler Main Function: Monitors Arrival Rate Decides Packet admission Monitor Queue State Decides BW allocation Design Parameters: Weight ( wi ) Queue Thresholds (Thi ) Decision Parameter: ( si ) Bucket October 31, 2003 Design Parameters: Service Bucket Size ( Bi ) Token Rate (ri ) Main Function: Decision Parameter: Average Queue Size (ˆ i ) Starvation Rate Dynamic Bandwidth Management 17 Adaptive Admission Control Analysis Bucket Starvation Rate This indicates how many tokens a flow needs. Fuller buckets mean lesser requirement and vice versa. : Link Capacity Startvation Rate : si (t ) 1 bi / Bi Normalize Token Distributi on : m pi (t ) si (t ) / s j (t ), 1 i m j 1 The Averaged Strarvation Rate : pˆ i (t ) (1 ) pˆ i (t 1) pi (t ) New Token Rate bi : Tokens in the Bucket of i thClass ri pˆ i (t ) m : Number of Classes Bi : Bucket Size of i thClass October 31, 2003 Dynamic Bandwidth Management 18 AAC: Algorithm October 31, 2003 Dynamic Bandwidth Management 19 Adaptive Scheduler (ACBQ) Analysis Each Queue has TH – Upper Threshold Average Queue Size ̂ i For Lesser Delay lower TH . Average Queue Size indicates w.r.t to TH tells us how much service the queue class requires. October 31, 2003 Absolute Service Starvation Rate : q (t ) ˆ (t ) / T i i hi Normalized Service Stravation Rate : n wi (t ) qi (t ) / q j (t ) j 1 Average Service Rate : i (t ) wi (t ) Dynamic Bandwidth Management 20 ACBQ – Algorithm The parameters: Wq typically set to 0.002 typical _ transmissi on _ time 0.001 October 31, 2003 Dynamic Bandwidth Management 21 Simulation Setup October 31, 2003 Dynamic Bandwidth Management 22 Simulation Scenarios No. Scenario Name Traffic Load 1 Base Line Exp 0.3-0.95 None Static CBQ 2 Static Allocation Exp 0.3-0.95 CAR Static CBQ 3 Partial Adaptive Exp 0.3-0.95 AAC Static CBQ 4 Fully Adaptive Exp 0.3-0.95 AAC Adaptive CBQ 5 Base Line Pareto 0.3-0.95 None Static CBQ 6 Static Allocation Pareto 0.3-0.95 Car Static CBQ 7 Partial Adaptive Pareto 0.3-0.95 AAC Static CBQ 8 Fully Adaptive Pareto 0.3-0.95 AAC Adaptive CBQ October 31, 2003 Dynamic Bandwidth Management AC Scheduling 23 Data Collections Each simulation scenario was run 3 times with different seeds For a duration of 70 seconds Data was collected between 10-70 seconds, assuming the simulator too 10 seconds to reach steady state. The instantaneous values per simulation scenario were averaged over the duration of the simulation. Then again the averages were averaged for the three runs. October 31, 2003 Dynamic Bandwidth Management 24 Delay Performance – Exponential October 31, 2003 Dynamic Bandwidth Management 25 Delay Performance – Pareto (LRD) October 31, 2003 Dynamic Bandwidth Management 26 Jitter Performance - Exponential October 31, 2003 Dynamic Bandwidth Management 27 Jitter Performance - Pareto October 31, 2003 Dynamic Bandwidth Management 28 Loss Rate – Exp and LRD October 31, 2003 Dynamic Bandwidth Management 29 Offered Load Vs Throughput Average: 5% BW saving At 80% Load – 14.5% BW saving October 31, 2003 Average: 11% BW saving At 80% Load – 30% BW saving Dynamic Bandwidth Management 30 Simulation Results Summary Higher Efficiency Lower Packet Drop - Prevented bad admission decisions. Increased Throughput Bursty traffic showed better gain. Tradeoffs for Higher Throughput Increased Delay Increased Computation to O(N), Where N is the number of QoS classes. N is always small. October 31, 2003 Dynamic Bandwidth Management 31 Conclusion CONTRIBUTION New approach to Bandwidth Management Our approach performs better than commercial implementing in terms of bandwidth utilization. FUTURE WORK Define an accurate relation between the QOS metrics and Control Parameter. What is the best time scale of operation? How does it behave with RED and its Variants? End to End QOS is still not addressed. October 31, 2003 Dynamic Bandwidth Management 32 Thank you. October 31, 2003 Dynamic Bandwidth Management 33 View On Fundamental Limitations Application Utility as a function of Network Performance This is undefined, and needs to be well defined. QOS tries to provide better than BE How about worse than BE, Scavenger service, “nice” in UNIX process resource sharing. Elevated services, non-elevated services (Internet2). Deployment is a bigger challenge that most people think. October 31, 2003 Dynamic Bandwidth Management 34 2 Ethernet and 8 Slip Interface Node Model Opnet R ou ter N od e M od el IP Child Process Model: The child process model, ip_output_iface implements all the Scheduling and AQM algorithms like Class Based Queuing, RED, WRED, etc. IP Pro ces s Mo de ip_dispatch Process Model Child Process October 31, 2003 Dynamic Bandwidth Management IP Node Model: The router implements the complete networking stack. We are interested in the IP node. We modify this node to allow us to probe some of the statistics that we collect in relation to our proposed model. We also modify the underlying process models by implementing our adaptive algorithms. IP Process Model: The IP process model has several child process models like ip_icmp, ip_vpn etc. Each child process model implements a specific feature of IP. 35