1. Extension to PerfCenter: A Modeling and Simulation Tool for Datacenter Application Nikhil R. Ramteke, Advisor: Prof. Varsha Apte, Department of CSA, IISc 27 th May 2011

2. Multi-tiered Networked Applications DB server Auth servers web server Important Performance Metric Response time Utilization Throughput Waiting time Queue length Arrival Rate Blocking probability Average service time

3. Flow of a request through such a system 3 Machine 1Machine 2 Machine 3 “login” Web Server Auth ServerWeb Server Auth Server Web ServerDB Server Web Server Machine 4Machine 5 Machine 6 Machine 7Machin 8 Machine 9

4. PerfCenter Fig: PerfCenter tool structure Performance measurement tool, it builds and solves the system model It takes the system details as an input and built the system model. System model is built as a network of queues. Built model is solved either by simulation or by analytical methods. Open source, available at: http://www.cse.iitb.ac.in/perfnet/softp erf/cape/home/wosp2008page

5. PerfCenter (Input Language)Input Language Host Specification: host machine1[2] ram 1000 cpu count 1 cpu buffer 99999 cpu schedP fcfs cpu speedup 1 disk count 1 disk buffer 99999 disk schedP fcfs disk speedup 1... end Server Specification: server web thread count 1 thread buffer 9999 thread schedP fcfs thread size 0.610 staticsize 100 requestsize 0.5 task node2 task node5 task node9... end

6. Feature Enhancements to PerfCenter (Problem Definition) Among various enhancements possible, our contribution is following: Memory Model: Memory can be bottleneck while deploying server on host. Individual server utilization on a device: PerfCenter can predict the device utilization of host. But can not estimate the which server has contribution in what amount, This feature enables the user to find bottleneck server quickly Timeout and Retries: Aimed at capturing the user behavior like “stop-reload”.

7. Memory Usage Modeling PerfCenter System Model for memory usage: Servers: Static size of server Per thread memory usage Per request memory usage (increases with queue length) Host: RAM size for each host Input language specification: Server web staticsize 80 thread size 2 requestsize 2 end Host host1 ram 2000 end Per server util = (Static Size + Thread size * total threads + Request size * Avg. Queue length of request queue)/ RAM size Metrics: util(host_name:ram) //overall RAM util util(host_name:server_name:ram) //RAM util by a server

8. Software Design Changes Required for Memory model and Individual Server Utilization Memory Model  Added members static size, thread size, request size to the server class software server,  Added members ram size to host class,  No change required to dynamic statistics calculation in simulation  Use average queue length calculated at the end of simulation Individual server utilization of host devices:  Must keep track of who is issuing request to device  Class member update: total busy time, utilization variables into software queue class.  Some additional bookkeeping during simulation (per server statistics) Server RRRRR S1 S2S1S3S2

9. Timeouts and Retries: Characteristics of real users of server systems Impatience: users abandon if response is not received within their expected time Retries: Users often retry just after abandoning a request (E.g. “stop-reload” behavior on Web browser) This behavior is common in client-server based applications. Timeout may affect system performance in following ways: Reduction in Throughput, Completed requests may have already timed out – need to count successful requests separately, Utilization may decrease due to less throughput, Average response time decrease due to increase in request timeouts,

10. Timeouts and Retries: When request is submitted to an application, one of the following things can happen: Server Drop [Drop rate (D)] Timeout in Buffer [Timeout in buffer Rate (Tb)] Timeout during service [Badput (B)] Successfully completed [Goodput (G)] Arrival of request Request does not leave the queue immediately, When it is picked by s/w server then it is counted as failed. Request processing is not aborted immediately, processing goes to completion, but request counted as failed Possibility of Retry

11. Timeouts and Retries: (PerfCenter system model) Mean timeout value is taken as an input with certain distribution, timeout value of each request is set according to it. Input language: loadparams timeout distribution_name(distribution_parameters). end Eg: loadparams timeout exp(0.5) end

12. Timeouts and Retries: (PerfCenter system model: ) Overall G, B, D and Tb can now be estimated with PerfCenter as follows, Output Language: - gput() //overall Goodput - bput() // overall Badput - buffTimeout() // overall Timeout in buffer rate - droprate() // overall drop rate

13. Timeouts and Retries: Software Design Changes: Added members timeout flag, mean timeout in to the Request class, Added number of request processed, number of request timeout in buffer, number of request timed out in service, Goodput, Badput, drop rate, timeout in buffer rate to the Scenario simulation class. No extra events are added.

14. Validation: Validation done using sanity checks  Results should follow expected rules and trends Scenario used for validation: Input File Type of System : Open Service rate : 100 Arrival rate : Varied from 10 to 100 Timeout rate : 10 Timeout distribution : Exponential Requests simulated : 1000000 Number of repetition : 20

15. Results Fig : RAM utilization v/s Arrival Rate

16. Results Fig : G, B, Tb, D v/s Arrival Rate More request timed out in buffer Goodput Decreases

17. Results Fig : Utilization, Throughput v/s Arrival Rate Utilization curve follows Throughput (G + B) Starts decreasing because more requests are timing out in buffer

18. Results Fig : Individual server utilization v/s Arrival Rate Utilization decreases due to more request time outs

19. Results Fig : Average Response Time v/s Arrival Rate Avg. Response time decreases due to timeouts

20. Summary of Work Done Before Midterm: Background Study Queuing theory, Simulation modeling, Performance issues of multi-tiered systems, PerfCenter After Midterm: Developed an abstraction, an input language and updated PerfCenter simulation engine for Adding memory model, Updating utilization model for Individual server utilization on device, Adding Timeout and Retries model.

21. Conclusion: PerfCenter is performance measurement tool, and can now be used by performance analysts with few more useful features added, most important one being timeouts and retries. Validated our model using test experiment. Illustrative results shows how PerfCenter can be used for estimating application performance in presence of following features. Memory model Individual server utilization Timeout and retries model. As results show, this can change data center sizing plans. Future work: Predicting G, B, Tb, D for individual queuing systems, More validation is needed to increase confidence in the tool, More features need to be added to increase power of the tool.

22. References: 1.R.P. Verlekar, V. Apte, PP. Goyal, and B. Aggarwal. Perfcenter: A methodology and tool for performance analysis of application hosting centers. MASCOTS '07: Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, 2007, pages 201—208. 2.Supriya Marathe, Varsha Apte, and Akhila Deshpande. Perfcenter: Perfcenter: A performance modeling tool for application hosting centers. WOSP '08 Proceedings of the 7th international workshop of Software and Performance, 2008 3. Kishor~S. Trivedi. Probability and Statistics With Reliability, Queuing, and Computer Science Applications. PHI Learing Private Limited, Eastern Economy edition, 2009.

23. References: 4. Averill M. Law and W. David Kelton. Simulation Modeling and Analysis. Tata Mcgraw-Hill, 2000. 5. Daniel A. Menasce and Virgilio A. F. Almeida. Scaling for E-Business, Technologies, Models, Performance and Capacity Planning. Prentice Hall PTR, 2000. 6. Supriya Marathe. Performance Modeling for Distributed Systems. Master's thesis, IIT Bombay, Mumbai, India, June 2008. 7. Puram Niranjan Kumar. Validation, Defect Resolution and Feature Enhancements of PerfCenter. Master's thesis, IIT Bombay, Mumbai, India, June 2008.