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2000. 11. 29 SSLAB, EE Dept, KAIST 박상호

기존의 resource management 와 다른 metric(revenue throughput) 을 제안하고 , 제안한 metric 을 극대화하기 위해 client 들의 session 특성을 분석하고 , 이를 resource Management 에 적용하였다.

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2000. 11. 29 SSLAB, EE Dept, KAIST 박상호

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  1. 기존의 resource management와 다른 metric(revenue throughput)을 제안하고, 제안한 metric을 극대화하기 위해 client들의 session 특성을 분석하고, 이를 resource Management에 적용하였다. Resource Management Policiesfor E-commerce ServersDaniel A. Menasce, Virgilio A. F. Almeida2nd Workshop on Internet Server Performance, 1999 2000. 11. 29 SSLAB, EE Dept, KAIST 박상호 System Software Research Lab.

  2. Content • Introduction • E-commerce workload • New Resource Management Policies • Framework • Performance • Conclusions & critiques System Software Research Lab.

  3. Introduction • Resource management • Resource : CPU, disk, network bandwidth • Traditional QoS metric • Client-side : response time • Server-side : throughput • Utmost important QoS metric for E-commerce servers • Revenue throughput : $/sec • Trade-off between throughput and response time • Poor response time • Customers leave the site • Reduction of revenue System Software Research Lab.

  4. E-commerce workloads(Cont’d) • Revenue throughput : $/sec • Potential lost revenue/sec : $/sec • Session : sequence of requests of different types • Browse, search, select, add to the shopping cart and pay • CBMG(Customer Behavior Model Graph) • Occasional buyer / heavy buyer • V = (Vb, Vs, Va, Vt, Vp) (average number of visits to the state) • V’ – Vhp = V x P (P : 5x5 matrix) • Vo=(6.76, 6.76, 0.14, 0.04, 2,73), Vh=(2.71, 2.71, 0.37, 0.11, 1.12) • Average session length = S = 1 + Vb + Vs + Va + Vt + Vp • So = 17.45, Sh = 8.03 • Buy to visit ratio = BV = Vp of occasional & heavy • BV = 0.9 x 0.04 + 0.1 x 0.11 = 0.047 • Upper bound on the revenue throughput System Software Research Lab.

  5. E-commerce workloads Occasional buyer System Software Research Lab.

  6. New Resource Management Policies(Cont’d) • Priority-based resource management : priorities change dynamically • State in which a customer is • Session length • Amount of money in his/her shopping cart * User profile h : Homepage b : Browse s : Search t : Select a : Add to cart p : Pay System Software Research Lab.

  7. New Resource Management Policies(Cont’d) • Managed resources • CPU • Processor sharing for classes Medium and Low • Ordered by $sc for the High • Disks • FIFO for classes Medium and Low • Ordered by $sc for the High System Software Research Lab.

  8. Framework(Cont’d) • Session generator • SURGE • Http requests • Current state, transition probability and think times • System response : poor response time -> customers leave the site • # of retries : (Occasional, Heavy) = (1,3) • Timeout = c2(state) + c1 * session_length c2(b,s,t,a,p) = (9,9,8,8,30), c1 = 0.1 System Software Research Lab.

  9. Framework(Cont’d) • Simulation model • Electric bookstore System Software Research Lab.

  10. Framework System Software Research Lab.

  11. Performance(Cont’d) System Software Research Lab.

  12. Performance System Software Research Lab.

  13. Conclusions & critiques • Conclusions • Novel metrics which combine the two views(client and server) • CBMG(Customer Transition Model Graph) to address the e-commerce workload characterization issue • Adaptive scheduling policy • Critiques • Real-environment • User profile : use cookie System Software Research Lab.

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