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Adaptive Resource Allocation: Self-Sizing for Next Generation Networks. Michael Devetsikiotis Electrical & Computer Engineering North Carolina State University Dr. Qi Hao, Nortel Networks, Ottawa Dr. Sandra Tartarelli, NEC Research, Germany Dr. Matthias Falkner, Cisco, Germany
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Adaptive Resource Allocation: Self-Sizing for Next Generation Networks Michael Devetsikiotis Electrical & Computer Engineering North Carolina State University Dr. Qi Hao, Nortel Networks, Ottawa Dr. Sandra Tartarelli, NEC Research, Germany Dr. Matthias Falkner, Cisco, Germany Dr. Jiangbin Yang, Lantern Communications Fatih Haciomeroglu (M. Sc., graduated) Srikant Nalatwad (Ph. D. candidate) Peng Xu (Ph. D. candidate) Robert Callaway (M. Sc. candidate)
Overview of Interests • Measurement-based, adaptive resource allocation • use traffic measurements to improve congestion control • include prices, QoS • use statistical methods to predict, model and simulate efficiently • Open Loop: • “Self-sizing” of ATM/MPLS via adaptation • Preemption and re-routing methods • Closed Loop: • Predictive Active Queue Management • Predictive methods for Explicit Congestion Notification • Games for loss networks (optical, wireless) with incomplete info
Self-Sizing Using Measurements • Bandwidth allocation: make adaptive rather than static • Use bandwidth more efficiently while satisfying QoS constraints. • Adaptive algorithms based on traffic measurements: large gains. • Plan: Separate into “data” and “control” parts • Research and compare effective bandwidth techniques. • Show efficiency gains, QoS delivery. • Investigate measurement time scales and parameter settings. • Research “globality” of information for larger networks (scaling). • Study of gain vs. adaptation time scale. • Try out in simulation, then emulation • C++, OPNET • RTFM in open source (IETF), Linux test-bed
C required bandwidth Traffic Descriptor: Effective Bandwidth • Theoretical Framework The EB is a measure of the amount of bandwidth a source requires to meet its QoS. From Large Deviation Theory: Under the hypothesis of large buffers: For each traffic class within each SD pair: • P(Q>b): required QoS • b: determined by max delay
S D Network NAP measurement point t Measurement window (n-1) n Measured Effective Bandwidths • For each traffic class within each SD pair the aggregated traffic is measured On-Line Estimation
Optimization Model Example • Optimize bandwidth partition among bands, given pricing, costs The optimal band partitioning problem (OBP) can be defined as finding and values that satisfy: • Decision Variables: : partitioned capacity for band b in link j : 1 if node pair p, traffic pair b, goes through route r , and 0 otherwise • Other Parameters: : effective bandwidth derived based on on-line measurement and QoS. Refer to the paper for other parameters.
Results: Data Part • Systematic method comparison (pros and cons, complexity) • Single node simulation: efficiency vs. QoS • SRD and LRD traffic (investigated several generators). • Selected algorithms (e.g., Gaussian, Norros, Courcoubetis, DRDMW). • Showed gains, detailed statistics, satisfied QoS while saving bandwidth. • Proposed novel dynamic time scale technique. • Established Linux QoS test-bed, with MPLS, LDP, etc. • Ported algorithms to IETF RTFM • Confirmed simulation results with realistic emulation
Emulation: RTFM EB estimation was also implemented in the meter side. This eliminated the need for large SNMP transfers and resulted in faster response.
