IEEE 7th Annual Workshop on Workload Characterization The USAR Characterization Model Adriano Pereira, Gustavo Gorgulho, Leonardo Silva, Wagner Meira Jr., and Walter Santos Department of Computer Science Federal University of Minas Gerais (UFMG) Belo Horizonte - Brazil October, 2004

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory Presentation Index Introduction Goals Methodology –Characterization Model –Validation Model Case Study Conclusion and Ongoing Work

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory Introduction Understanding the nature and characteristics of Web workloads is a crucial step to improve the quality of the offered service –Design systems with better Performance and Scalability. Workload Characterization Methodologies and Techniques –Guidelines to characterize and generate workloads

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory Introduction Workload Characterizations typically do not consider reactivity aspects –Do not consider agreggate information from user and server-side –Generate the same workload despite the server response time Server Client Request Response

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory Goals Characterize and replicate the behavior related to user reactivity Analyze and model the way users react to variations in the quality of service provided. –Correlate user and server-side Validate the model through simulation.

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory Methodology When we look at the interaction process we have sequences of requests and responses –IAT (inter-arrival time): time between requests User-side related measure –Latency: time to process and answer the request Server-side related measure LATENCY IAT Server Client REQUEST RESPONSE

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory Methodology The USAR Characterization Model –Conceptual views USER (U) SESSION (S) ACTION (A) REQUEST (R) User: user behavior considering quality of service Session: actions between a threshold τ Action: clicks from the user Requests: objects associated with a action

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory User Level Characterization 1) Prepare log: generate a temporary log Lu by aggregating the sessions per user; 2) Analyze users from the following perspectives: –IAT and Latency ratio; –IAT and Latency difference; 3) Discretize IAT and Latency measures using a correlation function;

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory User Level Characterization - Discretization Model 7 User Classes (A – G) DIF k5 k6 RAT C B A E F G D 0k1k2 k3 k4 PatienceImpatience

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory User Level Characterization 4) Transform user sessions into sequences of user classes according to discretization criteria from item 3); –Ex: A A B F B G G G G A 5) Evaluate the sequences in order to group them using similarity; –Sequence mining (SPADE tool) or matching

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory User Level Characterization Classify them in User Profiles: – –Patientk5 k6 RAT C B A E F G D 0k1k2 k3 k4 – –Impatient – –Impatient Tendency – –Continuous – –Patient Tendency – –Inconstant

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory User Level Characterization 6) Process the log Lu applying a function f(Lu), which maps sequence of classes to groups defined in step 5; 7) Apply Clustering; 8) Analyze the clusters and classify them.

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory Methodology Validation using the USAR Simulation Model USARUSAR Inputs Users Types Behaviors and Profiles Session Length Dist. Action Popularity Object Size Object Popularity Parameters # User Sessions Log of User Reactions per Session

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory Case Study Proxy-cache of Federal University of Minas Gerais (UFMG); 4 weeks of logs.

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory Case Study Distribution of Sessions and User Profiles

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory Case Study Validation through simulation USAR

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory Conclusion We propose the USAR characterization methodology that comprehend the levels of request, action, session and user –Generic Methodology applicable to any workload Model reactivity aspects using inter-arrival time and latency that has been interesting and promising –Present a discretization model based on the radio and difference between IAT and latency Validate the model through simulation

e-Commerce, Systems Performance Evaluation, and Experimental Development Laboratory Ongoing Work We foresee the possibility to generate more realistic workload –Reduce the gap between the existing models and the actual workloads considering reactivity aspects