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Web Server QoS Management by Adaptive Content Delivery

Web Server QoS Management by Adaptive Content Delivery. Tarek F. Abdelzaher and Nina Bhatti Quality of Service, 1999. IWQoS '99. 1999 Seventh International Workshop on , 1999. September 26 2000 Chul Lee. Introduction (1/2). Today’s web servers Offer poor performance under overload

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Web Server QoS Management by Adaptive Content Delivery

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

  2. Introduction (1/2) • Today’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

  3. Introduction (2/2) • Overload 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_contentand/degraded_content • Static and dynamic

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

  5. QoS Adaptation Architecture (2/4) • Load 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

  6. QoS Adaptation Architecture (3/4) • Utilization 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

  7. QoS Adaptation Architecture (4/4) • The Utilization Controller • A good value : 85% • Use well-known integral controller

  8. QoS Management (1/3) • Performance Isolation • Service Differentiation • Excess Capacity Sharing

  9. QoS Management (2/3) • Performance 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

  10. QoS Management (3/3) • Service Differentiation • Support client prioritization lower priority clients are degraded first • The capacity should be made available to clients in priority order • Sharing Excess Capacity • The excess capacity is made available to other virtual servers

  11. Evaluation (1/5) • Environments • Testing tool : httperf • Clients : 4 WSs, connected to 100M switched ethernet • Estimating Service Time • T(x) = a + bx, a = 1.604, b= 0.063

  12. Evaluation (2/5) • Request 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

  13. Evaluation (3/5) • Performance 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

  14. Evaluation (4/5) • Service Differentiation • 2 classes • B : basic class • increasing • P : premium class • 100 req/s

  15. Evaluation (5/5) • Excess 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

  16. Conclusion • Conclusion • Content adaptation enables a server to provide a smooth range of client degradation • Performance isolation • Service differentiation • Sharing excess capacity • Future 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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