A SLA Framework for QoS Provisioning and Dynamic Capacity Allocation Rahul Garg (IBM India Research Lab), R. S. Randhawa (Stanford University), Huzur Saran (IIT Delhi) and Manpreet Singh (Cornell University)

Roadmap Current SLAs and Related Work Drawbacks Proposed SLA Merits Applications Simulation results Conclusion and Future work

What is SLA? Service level agreement between customer and service provider. QoS parameters Committed info rate Transit delay Packet loss rate Pricing information

Current SLAs Static (peak) Provisioning Flat rate pricing 95-5 model Drawbacks: Need for bandwidth is time-varying Hard to predict Charges based on peak consumption Actual usage is typically much lower Under-utilization of resources Lower revenue for service provider

Advanced Schemes in Research Literature Continuous bandwidth auction (Semret et.al. 1999) Usage based pricing (Anantharam et.al. 2000) Drawbacks: Difficult to implement Significant departure from traditional pricing Hard for bandwidth providers to plan Users may not get bandwidth in case of overload

Proposed SLA: TTPP Three Tier Pricing with Penalties Revenue Discount Premium

Proposed SLA: TTPP Static QoS parameters Reliability Availability Mean Time to Failure Grade of Service Committed info rate Long-term expected capacity Pricing Information: Charging rate r Discount rate d Premium rate p Penalty rate q

Service Provider Customer 1 Customer 2 TTPP: An Illustration TTPP SLA

Revenue TTPP: An Illustration (Contd.)

Discount TTPP: An Illustration (Contd.)

Premium Discount TTPP: An Illustration (Contd.)

Premium Give me back!!! TTPP: An Illustration (Contd.)

Premium Penalty TTPP: An Illustration (Contd.)

Choices for Penalty Fixed Penalty Delay Dependent Penalty Proportional Penalty

Need for Admission Control

Request at Low Premium Admission Control: Illustration

Paying low premium Admission Control: Illustration

Paying low premium Admission Control: Illustration Request at high premium

Paying low premium Admission Control: Illustration Sorry, no resource

Paying low premium Admission Control: Illustration I should not have given resource to customer II

Admission Control Resize request to increase usage Non-preemptive May lose higher premium in future May have to pay penalties in future Accept or not??? Objective: Maximize the total earnings

Trunk Reservation based Scheme What is Trunk Reservation? No, Not This!!!

What is Trunk Reservation? Consider Capacity of C units shared by two customers Customer I has higher priority than customer II Trunk reservation of t units against customer II The scheme: Whenever possible, accept requests of type I Accept requests of type II only if more than t units are available.

Calculation of Trunk Reservation Used in Telecommunications Even small trunk reservation parameter is effective (Reiman, 1991) Assigns absolute priority to one class over the other Markov Decision Theory can be used Solution space increases Difficult for online implementation

Calculating trunk reservation Theoretical analysis to find optimal trunk reservation for the case of revenues only. tr = k log(r 1 /r 2 ) / log(C 1 /C 2 ) Get trunk reservation parameter based on the SLA Heuristic used for the general case Replace r i by priority of the customer Priority(i ) = d i + q i i if usage < prov. Capacity = p i otherwise Constraint: no trunk reserved for a customer with usage above the provisioned capacity

Applications of TTPP Application Service Provider (ASP) Dynamic resource(bandwidth/server) allocation Pricing the web hosting service Customer books committed resource in advance, at a negotiated price Sends resize request Perceived need for bandwidth QoS and traffic requirements

Applications of TTPP (Contd.) Pay revenue for his booked resources Earn discount for releasing unused resources Pays premium for usage beyond the booked resources Get penalty if provider does not return back your released resources

Applications of TTPP (Contd. ) Virtual Private Networks (VPN) Mechanism for traffic engineering for VPN’s Set up MPLS LSP’s with provisioned bandwidth (e.g. using CR-LDP)

Simulation Model Discrete Event Simulator Realistic traffic model Actual web traces (Internet Traffic Archive) Fixed data transfer rate of 20Kbps Comparison with Peak Provisioning model Fixed r (= 1), d (= 0.5), p (= 2) Fixed penalty q (= 5)

Performance Metric Capacity allocated to customers under TTPP adjusted to give same blocking probability as in peak provisioning Customer payoff Total payment made in Peak Provisioning Total payment made in TTPP SLA Payoff for service provider Total earnings in TTPP SLA per unit installed capacity Total earnings in Peak Provisioning per unit installed capacity

Hits per second vs Time

Instantaneous Capacity vs Time

Simulation Results ASP: Customer payoffs: 1.3 to 5.02 ASP payoff: 1.06 VPN: Customer payoff: 1.07 to 1.88 Network payoff: 1.13

FCFS vs Trunk-based scheme User No.Blocking Prob (FCFS) Blocking Prob (Trunk)

Merits of TTPP Significant statistical multiplexing gains Committed information rate helps the service provider in planning resource allocation Evolutionary in nature Coexist with the current fixed capacity SLA Frequency of resize requests Tradeoff between complexity and degree of dynamic pricing Low overheads of the scheme Idea generalizable to any resource sharing

Future Work How to select the parameters of the proposed SLA long-term committed bandwidth Resize frequency Pricing parameters Different forms of penalty Other admission control algorithms

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References Aurel Lazar and Nemo Semret. Design and analysis of the progressive second price auction for network bandwidth sharing. Telecommunication Systems: Special issue on Network Economics, Internet Traffic Archive, Richard La and Venkat Anantharam. Charge sensitive TCP and rate control in the internet. In Proceedings of INFOCOM Martin I. Reiman. Optimal trunk reservation for a critically loaded link, ITC 1991.