CHUL LEE , CORE Lab. E.E. 1

Web Server QoS Management by Adaptive Content Delivery

September 26 2000September 26 2000

Chul LeeChul Lee

Tarek F. Abdelzaher and Nina Bhatti

Quality of Service, 1999. IWQoS '99. 1999 Seventh International Workshop on , 1999

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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

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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

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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 AdaptationLayer

Communicationsubsystem

Request

Request with Modified URL

Response

Load Monitor & Utilization Cotrol

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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

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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

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QoS Adaptation Architecture (4/4)

• The Utilization ControllerThe Utilization Controller– A good value : 85%

– Use well-known integral controller

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QoS Management (1/3)

• Performance IsolationPerformance Isolation• Service DifferentiationService Differentiation• Excess Capacity SharingExcess Capacity Sharing

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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

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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

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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

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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

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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

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Evaluation (4/5)

• Service DifferentiationService Differentiation– 2 classes

• B : basic class– increasing

• P : premium class– 100 req/s

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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

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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