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CHUL LEE, CORE Lab. E.E. 1 Web Server QoS Management by Adaptive Content Delivery September 26 2000 Chul Lee Tarek F. Abdelzaher and Nina Bhatti Quality.

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Presentation on theme: "CHUL LEE, CORE Lab. E.E. 1 Web Server QoS Management by Adaptive Content Delivery September 26 2000 Chul Lee Tarek F. Abdelzaher and Nina Bhatti Quality."— Presentation transcript:

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2 CHUL LEE, CORE Lab. E.E. 1 Web Server QoS Management by Adaptive Content Delivery September 26 2000 Chul Lee Tarek F. Abdelzaher and Nina Bhatti Quality of Service, 1999. IWQoS '99. 1999 Seventh International Workshop on, 1999

3 CHUL LEE, CORE Lab. E.E. 2 Introduction (1/2) Today’s web serversToday’s web servers –Offer poor performance under overload –Have no means for prioritizing requests –Have no mechanism for pre-allocating end-system capacity to a particular site or hosted service

4 CHUL LEE, CORE Lab. E.E. 3 Introduction (2/2) Overload protectionOverload protection –Load balancing and admission control –Multicast –Content adaptation The compression of Images(65% of the total bytes, e-commerce site) Reducing # of embedded objects per page Reducing local links Multiple content trees –/full_content and /degraded_content –Static and dynamic

5 CHUL LEE, CORE Lab. E.E. 4 QoS Adaptation Architecture (1/4) Content adaptation layerContent adaptation layer –Decides on “right” content tree –Prevent underutilization or overload –Server load monitoring & server utilization control Web Server Process Content Adaptation Layer Communication subsystem Request Request with Modified URL Response Load Monitor & Utilization Cotrol

6 CHUL LEE, CORE Lab. E.E. 5 QoS Adaptation Architecture (2/4) Load MonitoringLoad Monitoring –To quantify server utilization The request service time (a URL of size x) –T(x) = a + bx (a : fixed overhead comp. b : data-size dep. comp.) System utilization –U = aR + bW (R : request rate, W : delivered BW) Determine a and b off-line

7 CHUL LEE, CORE Lab. E.E. 6 QoS Adaptation Architecture (3/4) Utilization ControlUtilization Control –The Content Adaptor M content trees G : the severity of the adaptation action required from the adaptor –G = M : all requests served the highest quality content –G = 0 : all requests must be rejected –H() : hashing function, maps a given client id to the same number every time

8 CHUL LEE, CORE Lab. E.E. 7 QoS Adaptation Architecture (4/4) The Utilization ControllerThe Utilization Controller –A good value : 85% –Use well-known integral controller

9 CHUL LEE, CORE Lab. E.E. 8 QoS Management (1/3) Performance IsolationPerformance Isolation Service DifferentiationService Differentiation Excess Capacity SharingExcess Capacity Sharing

10 CHUL LEE, CORE Lab. E.E. 9 QoS Management (2/3) Performance IsolationPerformance Isolation –A virtual server A web server can host multiple independent sites Associate a virtual server with each hosted site Capacity planning Load Classification Utilization control

11 CHUL LEE, CORE Lab. E.E. 10 QoS Management (3/3) Service DifferentiationService Differentiation –Support client prioritization  lower priority clients are degraded first –The capacity should be made available to clients in priority order Sharing Excess CapacitySharing Excess Capacity –The excess capacity is made available to other virtual servers

12 CHUL LEE, CORE Lab. E.E. 11 Evaluation (1/5) EnvironmentsEnvironments –Testing tool : httperf –Clients : 4 WSs, connected to 100M switched ethernet Estimating Service TimeEstimating Service Time –T(x) = a + bx, a = 1.604, b= 0.063

13 CHUL LEE, CORE Lab. E.E. 12 Evaluation (2/5) Request Rejection OverheadRequest Rejection Overhead –To quantify the rejection overhead The server rejects all requests by closing the connection as soon as the request is read off the server socket The maximum rate was found : 900 reqs/s  1.1ms/req (cf. a = 1.604) Rejecting a set of requests will consume almost 70% of the resources it would take to serve them a short URL

14 CHUL LEE, CORE Lab. E.E. 13 Evaluation (3/5) Performance IsolationPerformance Isolation –Non-guaranteed background best-effort traffic To overload the machine 300req/s(for 32KB URLs) –Server V1 Guaranteed BW : 13Mb/s Maximum guaranteed rate : 50req/s –Server V2 Guaranteed BW : 27Mb/s Maximum guaranteed rate : 100req/s

15 CHUL LEE, CORE Lab. E.E. 14 Evaluation (4/5) Service DifferentiationService Differentiation –2 classes B : basic class –increasing P : premium class –100 req/s

16 CHUL LEE, CORE Lab. E.E. 15 Evaluation (5/5) Excess Capacity SharingExcess Capacity Sharing –V1 13 Mb/s, 100req/s allowed to overrun its capacity Increased gradually 0-250req/s –V2 27 Mb/s, 100req/s Held constant 100req/s

17 CHUL LEE, CORE Lab. E.E. 16 Conclusion ConclusionConclusion –Content adaptation enables a server to provide a smooth range of client degradation Performance isolation Service differentiation Sharing excess capacity Future WorkFuture Work –Handling and adapting dynamic content : unpredictability of CGI –HTTP 1.1 : persistent connection –VOD server Scalable video encoding schemes  to avoid multiple copies –Appropriate content authoring and management tools to preprocess web contents


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