Mean value analysis of a database grid application
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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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Mean Value Analysis of a Database Grid Application

Dale R. Thompson

Computer Science and Computer Engineering

University of Arkansas

University of Arkansas


  • 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




[Single class queueing server]






[Multiple class queueing server]

Queueing Network System

University of Arkansas

When Queueing Server,

When Delay Server,

MVA algorithms

when class c is a batch processing,


  • 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?

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Classification of MVA algorithms

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Comparison of MVA algorithms

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Database Grid Application

Database Link Application Example

High-level Overview of System

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Database Grid Application Cont.

A high-level view of the grid

The Flow of Records

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Current Queueing Model

Queueing Model

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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.

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Maximum Throughput and Block size

  • Maximum attainable throughput : 79.5Mega record/hr

  • The block size :

    • Batch class : 1150 records

    • Interactive class : 1 record.

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Uniforms Distributions

  • Each record was equally likely to go to any of the computers in the database grid,

    • Block size : varying

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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)

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Uniform : Number of Clients

  • Uniform : 1150 records

  • Varying number of Clients : 20, 40, 60

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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

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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

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  • 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

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

Contact Information and Copy of this Presentation

Dale R. Thompson

311 Engineering Hall

Fayetteville, Arkansas, USA


Phone: +1 (479) 575-5090

E-mail: [email protected]


University of Arkansas

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