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Comparative Analysis of Best-Effort vs. Reservation-Based Network Services

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This paper delves into the ongoing debate about the viability of best-effort (BE) service versus reservation-based systems in networking. It discusses the current limitations of best-effort service lacking guarantees for real-time applications like audio and video. Through a thorough analysis, it posits scenarios where reservations may improve performance but also examines the implications of over-provisioned networks. The study aims to determine optimal network design principles that maximize user utility and satisfaction while addressing the complexities involved in reservations versus adaptive applications.

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Comparative Analysis of Best-Effort vs. Reservation-Based Network Services

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  1. Current Research Topics -Sigcomm Sessions -QoS -Network analysis & security -Multicast -giga/tera bit routers /fast classification -web performance -TCP -Diff Serv. -Routing -Network Topology

  2. BEST-EFFORT VERSUS RESERVATIONS A simple comparative Analysis Lee Breslau & Scott Shenker PROCEEDINGS OF ACM SIGCOMM’ 98

  3. MOTIVATION -Current Internet -single class of best-effort service -no guarantees about if/when packets are delivered -Best-effort service not suitable for real-time applications -audio, video, soft real-time … -Solution: Integrated Services -Applications reserve bandwidth -Network admits flows, enforces reservations...

  4. KEY QUESTIONS -Are reservations really necessary? -Are we better off with an over-provisioned BE network? *Arguments for reservations -Applications needing higher quality benefit *Arguments against reservations -Network must operate at low utilization levels for low blocking probability -Low util =>over-provisioned BE network is ok -Soft real-time applications are adaptive

  5. u video u u elastic ftp 1 0 delay delay delay GOAL OF NETWORK DESIGN • A network should be designed to meet user needs • Each user gets a utility u based on network bandwidth, delay, jitter • Design the network to maximize total utility (ui) =>provide maximum satisfaction to users

  6. elastic  b/w Rigid  b/w FIXED LOAD MODEL Assume -Single link of capacity C -K identical flows, b/w b=C/k -Utility fn (0)=0, ()=1 -Maximize v(k)= •Elastic applications -v(k) increases as k->  =>admit  flows to maximize v =>use B/E network •Rigid applications -v(k) drops to 0 beyond a certain k -admit no more than kmax flows =>use reservation

  7. VARIABLE LOAD MODEL -Number of flows is taken from a probability distribution -V= -Reservations=> v= -Question: How much extra b/w do you need for BE network to match the performance of resv? -trade b/w for complexity -P(k): -exponential -Poisson -Algebraic (heavy tailed)

  8. 1 Results -BE v/s rigid v/s adaptive application -Fixed model : substantial difference between BE & resv-capable network -adaptive applications change the picture -less compelling case for reservations -Poisson: equal costs except at highest prices -Exponential: small difference -Algebraic: Resv is better IF complexity is small -B/W is expensive -? -B/W is cheap -?

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