Adaptive Content Delivery for Scalable Web Servers Authors: Rahul Pradhan and Mark Claypool Presented by: David Finkel Computer Science Department Worcester Polytechnic Institute Worcester, MA, USA

Outline  Introduction Managing Load  Managing Load  Approach - Methodology - Design  Experiments  Client – Side Experiments  Future Work Conclusions

Introduction Enormous Growth of Web Exponential in number if users, pages, and sites New Web uses require different resources Static: Disk, Network Dynamic: CPU, Network Media streaming: CPU, Disk+, Network+

Web Server Overload Loaded server rejects connections, denying service Companies lose revenue Loaded server increases response times Slower pages viewed as less interesting

Current Approaches to Managing Load Over provisioning Beef up single server Can still become loaded during flash crowds Load balancing Server farm or CDN Individual servers may still become over-loaded Content adaptation Reduce resources needed upon heavy load

Content Adaptation Examples Using “thumbnails” instead of full, inline images Reducing the number of local links Reducing the number of embedded objects Changing the quality of images Current approaches manual! Our approach is to adapt content automatically

Our Approach Two versions of Web pages High quality to serve under normal load Low quality to serve under high load Monitor the server load A separate light weight standalone process Upon heavy load, server switches to low quality transparently Requires no modification to Server Browser http protocol

Adaptive Content Delivery System Architecture Load Monitor Adaptation Module Content Switching CPU, Disk, Network Web Server Disk Requests Response Base SystemOur System

Load Monitor Continuously monitors the utilization of the server and the observed response time Developed utilities to measure utilization CPU, Network, Disk Observed Response Time Used Linux /proc file system, but techniques general enough for any OS

Adaptation Module Input of load values from the load monitor Decides low or high load Low and high thresholds Threshold values determined by prior measurement  Scripts to induce load on server  httperf to generate requests  measured response time (using httperf)

Response Time vs CPU Util Thresholds 60% and 75%

Content Selector  Transparently switches content  Transparently switches content depending on the decision made by the adaptation module. symbolic links  We use symbolic links to make the same file point to different qualities of content. indexhigh.html index.html indexlow.html

Experiments Server P-III, 500,128 MB RAM, IDE, 10 Mpbs, Linux , Apache Workloads Static Workload Dynamic Workload Multimedia Workload Metrics Throughput (Responses/sec) Average Response Time Percentage of Errors Frame Rate (for Multimedia Clients)

Response Time (ms) vs Requests/sec

Percent Errors vs Requests/sec

Frame rate vs Number of MM Clients

Overhead For the Adaptive Content Delivery System

Client – Side Experiments real  Experiments on real servers to determine the impact of file size on response time. httperf  Used a modified httperf for our measurements to generate requests  Measured the response time along with the connection set up time and transfer time.

Conclusions Server load critical We present a mechanism to Quantify server load Adapat transparently to client Improves server performance: supports 25% more static requests supports twice as many Multimedia clients supports 15% more CGI requests

Future Work Adapting to heterogeneous client environment Clients may have different bandwidths Adding QoS features to the Web server Range of content quality at server Maximize QoS for user

Adaptive Content Delivery for Scalable Web Servers Authors: Rahul Pradhan and Mark Claypool Presented by: David Finkel Computer Science Department Worcester Polytechnic Institute Worcester, MA, USA

Extra slides past here ….

JPEG Quality : JPEG Quality Factor vs Percentage Savings in File Size

MPEG Quality : MPEG Q Scale Factor vs Percentage File Size Savings

Response Time vs Number of CGI Requests

Percentage Of CGI Requests Rejected vs Number of CGI Requests