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Mean Value Analysis of a Database Grid Application

Mean Value Analysis of a Database Grid Application. Dale R. Thompson Computer Science and Computer Engineering University of Arkansas. Introduction. The analysis of a queueing network is important for predicting the performance of a system.

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Mean Value Analysis of a Database Grid Application

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  1. Mean Value Analysis of a Database Grid Application Dale R. Thompson Computer Science and Computer Engineering University of Arkansas University of Arkansas

  2. Introduction • The analysis of a queueing network is important for predicting the performance of a system. • A database grid application was modeled using a queueing network. • The queueing network was analyzed by using an approximate mean value analysis algorithm called the Bard-Schweitzer algorithm or the proportional estimate (PE) algorithm. • Several different types of record flows were modeled. For example, uniform, non-uniform, etc. • A system in which the batch and interactive requests are segregated was modeled. University of Arkansas

  3. queue client server [Single class queueing server] queue client1 client2 client3 server [Multiple class queueing server] Queueing Network System University of Arkansas

  4. When Queueing Server, When Delay Server, MVA algorithms when class c is a batch processing, Zc=0 • Mean Value Analysis calculates throughput (Xc), response time (Rc), and queue length (Qd,c)of each class. • It can be classified with the number of client – open or closed. • It also can be classified with how to get the values Exactly or Approximately. • Single class or multiple classes? University of Arkansas

  5. Classification of MVA algorithms University of Arkansas

  6. Comparison of MVA algorithms University of Arkansas

  7. Database Grid Application Database Link Application Example High-level Overview of System University of Arkansas

  8. Database Grid Application Cont. A high-level view of the grid The Flow of Records University of Arkansas

  9. Current Queueing Model Queueing Model University of Arkansas

  10. CPU and Network Demand • Record size - 500bytes, Ethernet - 26bytes, IP - 20bytes, TCP - 20bytes. Total actual record size - 566bytes • Service demand1 : computers in the clients, the director grid, and database grid. • Service demand2 : network cards in the clients, the director grid, and database grid. University of Arkansas

  11. Maximum Throughput and Block size • Maximum attainable throughput : 79.5Mega record/hr • The block size : • Batch class : 1150 records • Interactive class : 1 record. University of Arkansas

  12. Uniforms Distributions • Each record was equally likely to go to any of the computers in the database grid, • Block size : varying University of Arkansas

  13. Non-uniform Distribution • Non-uniform distribution of demands was created by assuming that • 20 clients : 10,15,20 director computers : 70 database computers • 80% of the requests from clients (16 clients) => 20% of the database grid (14 Computers). • The remaining 20% of the requests (4 clients)=> the remaining 80% of the database grid (56 Computers) University of Arkansas

  14. Uniform : Number of Clients • Uniform : 1150 records • Varying number of Clients : 20, 40, 60 University of Arkansas

  15. Proposed Change to application • It was assumed that there were two updates per request • This proposed change was modeled by having 5% of the clients (1 client out of 20) require demand from two different database grid computers. • Block size : 1150 records University of Arkansas

  16. Segregation of batch and interactive classes • This model is for the actual system. • 20 clients : 16 director computers : 70 database computers. • There are 12 clients batch and 8 clients interactive record. • Batch 12 clients => 12 directors • Interactive 8 clients => 4 directors. • This reduces the mean delay per record to better serve the interactive clients • The database link application could use the 0.0002 s/record parameter as a design parameter University of Arkansas

  17. Conclusions • The number of directors should be approximately equal to the number of clients to obtain the maximum throughput of the system. • The bottleneck device in this system is the network. • The proposed application change that caused 5% of the records to require service from two database grid computers did not significantly decrease the performance of the system. • Segregating the batch and interactive classes at the director level causes the response time of the interactive classes to decrease. The decreased response time comes at the price of lowering the overall throughput of the system. As discussed, the model can be used to determine the trade offs of decreased response time versus increased throughput. University of Arkansas

  18. Future Work • Traffic analysis of submitted records • Simulation of alternate configurations • Scheduling of grid computers • Modeling/Simulation of different applications • Grid-enable applications that run in different locations and organizations • Others? University of Arkansas

  19. Contact Information and Copy of this Presentation Dale R. Thompson 311 Engineering Hall Fayetteville, Arkansas, USA 72701 Phone: +1 (479) 575-5090 E-mail: drt@uark.edu WWW: http://csce.uark.edu/~drt University of Arkansas

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