1. QoS Enabled Application Server The Controller Service Giovanna.Ferrari@ncl.ac.uk Bologna, February 19 th 2004

2. Outlines Objective: Controller Service for JBoss Function: to adapt the system to changing workloads by online optimization of the application server CTRL SERVICE: Micro level control of an individual application server CONFIGURATION SERVICE: Macro level control of cluster The Controller MBean Dynamic Tuning On Selecting the Configuration Experimental Analysis ECperf Benchmarking: some samples

3. Controller MBean 1/2 JBoss open-ended middleware Extend services dynamically deploying new components Service components are plugged into a JMX-based “server spine” Declared as MBean Services that are loaded into the server and administered through JMX Implement TAPAS Middleware Services as MBeans, plugged in the “server-spine”

4. Controller MBean 2/2 COMPARATOR At the end of each CI, checks data on completing requests from the server If any metric has been violated decides actions to take CONFIGURATION DECIDER Produces the newConf with the best QoS To find the best one, selects between a group of different configurations and asks the PERF.INT to calculate the related QoS PERFORMANCE INTERPRETER Calculates QoS for each Conf, applying a model to data on avg Arrival Rate of req and Service Demand at each device AR: computed using data on arrival process of req. during a CI SD: computed using Device Utilization and the Throughput during a CI: SDcpu=Ucpu/Th

5. Tuning the Configuration Modification of the configuration may be applied at each of the levels, dynamically, at run time, or statically, at set-up time Modifications affect the configuration and the performance of layers above Select the option –server - Xms128m -Xmx512m, to set the Java VM at 128M of initial and 512M of maximum Java heap size Run the server with the –c option, to specify the type of server configuration: minimal, default, all

6. The Web Console 1/2

7. The Web Console 2/2

8. Dynamic Tuning Numeric RW configurable attributes exposed by MBeans: jboss.invoker.pooled MaxPoolSize (300) Backlog (200) jboss.jca.ManagedConnectionPool.XAJDBCWrapperDS MaxSize (20) Performance features: Thread Pools: maintain live server threads accepting requests from the waiting queue and handling simultaneous client sessions DB Connection pools: maintain live physical database connections that can be reused in order reduce the overhead of opening and closing database connections With large pool: Pro: more threads/connections to fulfil requests Cons: slower access on the server resources/connection table

9. Selecting the Configuration Difficult to estimate and plan the size and capacity needed to provide the desired QoS How to select the best configuration and predict the effects System performance model Analytical Model that uses formula and computational algorithm to generate performance metrics from model parameters System load testing Benchmarking tool that produces a series of simulated events for measuring performance and scalability of a system

10. Analytic Model 1/2 Queuing Network Model Single tier Multiple tier

11. Analytic Model 2/2 It must be a good approximation of the reality It does track the measured values It aids in on-line optimization Aim: to employ System load testing to understand the parameters to tune, and the provoked effects Comparison of measured and predicted response time in Apache Web Server

12. System Load Testing Use benchmarking tool to generate artificial workloads on the system and to measure the characteristics of a system Results aid to identify and isolate system bottlenecks, fine-tune the components and predict the behaviour Advantages Provides accurate performance data Provides good approximations of the environment’s sizing and capacity Disadvantages Quite expensive and time-consuming Testing carried out in a separate environment, so not always directly address the issues in the overall environment

13. ECperf Benchmark 1/2 EJB Benchmark: incorporates e-commerce, B2B and supply chain management transactions Business model: 4 domains, referring to a logical entity that describes a distinct business sphere of operation Producer-consumer relationships and transactions are defined for each different domain, between domains in the company and to outside suppliers and customers

14. ECperf Benchmark 2/2 Throughput: made of activities on the SUT of the Driver Agents (Order/Manufacturing agents) Injection Rate: rate at which business transactions requests from the OrderEntry application are injected BBops/min: #Transactions in Customer Domain + #WorkOrders in Manufacturing Domain / minute

15. Experiments 1/2: the Environment Oracle DB Server JBoss 3.2.0 Server Ludgate.ncl.ac.uk: Pentium III – 931MHz 1 GB RAM Mill64.ncl.ac.uk: Pentium 4 – 2.40GHz 512MB RAM Mill59.ncl.ac.uk: Pentium 4 – 2.40GHz 512MB RAM 100Mbps Switch Client: ECperf Driver SUT

16. Experiments 2/2: Samples On tuning the MaxPoolSize in JBoss.Invoker.Pool ed Number of Server Threads waiting for client requests Experience shows that configurations with too many threads spend much more time in the kernel than those that are well tuned

17. Conclusions So far: Design issues of the Controller MBean Tuning the configuration Instruments used Future works: Conduct further experiments (collecting data on the devices performance as well) Use the experiments to validate the Analytical Model of the application server Apply the model for predicting the behaviour of JBoss application server in changing of the conditions

18. References X. Liu, L. Sha, Y. Diao, S. Froehlich, J.L. Hellerstein, Sujay Parekh, “Online Response Time Optimization of Apache Web Server”. 11 th International Workshop on Quality of Service, Monterey, CA, 2003 D. A. Menasce, D. Barbar, R. Dodge, “Preserving QoS of e-commerce sites through self-tuning: a performance model approach”, Proceedings of the 3rd ACM conference on Electronic Commerce, Florida, 2001. Sun Microsystems, “The ECperf 1.0 Benchmark. Specification”, June2001 http://java.sun.com/j2ee/ecperf/