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Approximate Mean Value Analysis of a Database Grid Application. Dale R. Thompson Computer Science and Computer Engineering University of Arkansas. Introduction Queueing Network System MVA algorithms Comparison of AMVA Proposed System Current Queueing Model CPU and Network Demand
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Approximate Mean Value Analysis of a Database Grid Application Dale R. Thompson Computer Science and Computer Engineering University of Arkansas
Introduction Queueing Network System MVA algorithms Comparison of AMVA Proposed System Current Queueing Model CPU and Network Demand Maximum Throughput and Block Size Uniform Distribution Nonuniform Distribution Uniform : number of Clients Proposed change to application Segregation of batch and interactive classes Conclusion Future Work Contents
Introduction • A database grid application is modeled using an approximate mean value analysis algorithm. • The system is represented by a queueing network. • The analysis of a queueing network is important for predicting the performance of a system. • Several algorithms will be explained and compared. • Database grid application is introduced and the performance objectives are defined and analyzed by using an approximate MVA algorithm called the Bard-Schweitzer algorithm or the proportional estimate (PE) algorithm. • Several models will be modeled. For example, uniform, non-uniform, etc. • A system in which the batch and interactive requests are segregated is modeled. • Conclusions of analysis.
queue client server [Single class queueing server] queue client1 client2 client3 server [Multiple class queueing server] Queueing Network System
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?
Proposed System Database Link Application Example High-level Overview of System
Cont. A high-level view of the grid The Flow of Records
Current Queueing Model Queueing Model
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.
Maximum Throughput and Block size • Maximum attainable throughput : 79.5Mega record/hr • The block size : • Batch class : 1150 records • Interactive class : 1 record.
Uniforms Distributions • Each record was equally likely to go to any of the computers in the database grid, • Block size : varying
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)
Uniform : Number of Clients • Uniform : 1150 records • Varying number of Clients : 20, 40, 60
Proposed Change to application • It assume that there are two times update (new geo. and old geo.) • 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
Segregation of batch and interactive classes • This model is for the real 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
Conclusions of Work • First, the number of directors should be approximately equal to the number of clients to obtain the maximum throughput of the system. • Second, 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.
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?