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Prototype Presentation Replication Process Simulator. Academic Advisor: Dr. Eitan Bachmet Technical Advisor: Mr . Assaf Natanzon. Project Team : Adiel Ashrov Etai Hazan Benny Michali. http://replicationsimulation.wordpress.com/. Prototype Presentation Storage Replication Simulation.

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prototype presentation replication process simulator

Prototype PresentationReplication Process Simulator

Academic Advisor:

Dr. EitanBachmet

Technical Advisor:

Mr. AssafNatanzon

Project Team:

AdielAshrov

EtaiHazan

Benny Michali

http://replicationsimulation.wordpress.com/

prototype presentation storage replication simulation

Prototype PresentationStorage Replication Simulation

Prototype

Goals

Conclusions

Hands On

Prototype Demonstration 

prototype goals
Prototype – Goals
  • Development of the prototype was designed to overcome the risks we discovered with our requirements.
  •  We identified the following points as risks :
  • How can we make sure simulator is modeling the system accurately?
  • Making the system perform similarly to the real-life.
  • Statistics handling and transfer between different layers.
  • How to represent calculated statistics in the Presentation level
  • How to model I/O behavior and how to recognize behavior
  • How to operate on different algorithms/policies?
prototype conclusions
Prototype – Conclusions

Simulation results verification:

  • In order to ensure our simulation produces accurate statistics we have sought for an expert opinion
  • AssafNatanzon (EMC) inspected the results and approved our simulation results are coherent with real world behavior.

Statistical data Handling

  • We’ve experimented with the idea of using CSV files in order to pipeline the results from the application to the presentation layer.
prototype conclusions1
Prototype – Conclusions

Data plotting and GUI

  • We have explored several external libraries solutions for plotting the data and displaying GUI.
  • We are now writing the GUI in C# and plot the Graph using a sample graph template we will embed as a part of the UI.

I/O behavior modeling and recognition

  • We model the bursts with a random choice of time slots, each burst will be set randomly on time slot i, so all I\O operations scheduled before slot i will be sent at slot time i.
  • We use Entropy in order to recognize each host’s level of I\O activity.
prototype conclusions2
Prototype – Conclusions

Different Flow Control Alog’ Support

  • Flow Control algorithm:
    • We thought of and wrote a small flow control algorithm which can regulate the rate in which the cache is increasing capacity and the bandwidth utilization.
  • Dynamic Algorithm selection:
  • Switching a flow control algorithm dynamically will destroy the results of a simulation.
  • we decided to neglect this idea.
  • The flow control algo’ is chosen before the simulation starts(a given parameter) and doesn’t change.
prototype hands on
Prototype – hands on

I/O behavior modeling and recognition

  • We have modeled the burstinessbehavior with a parameter received from the user [0..100].
  • Time slots concepts:
    • Say for example burstiness is 40:
prototype hands on1
Prototype – hands on

I/O behavior modeling and recognition cont.

  • How did we identify a volume/host in a burst:
  • Entropy and time slot data capacity:
    • Let’s look at time slot and the amount of data transferred:
  • .
  • This is the probability that an I/O event happened in .
prototype hands on2
Prototype – hands on

I/O behavior modeling and recognition cont.

  • Entropy measure of uncertainty 
  • )
  • The lower the entropy ,it is more likely that the current Volume is in a burst.
prototype hands on3
Prototype – hands on

Different Flow Control Alog’ Support cont.

  • The main purpose of our project is allowing the user to compare between different flow control algo’.
  • After we have the ability to identify trends/burst in I/O behavior we now have the ability to regulate the flow of data and cache allocation.
  • Let’s see an example of such algorithm
prototype hands on4
Prototype – hands on

Different Flow Control Alog’ Support cont.

prototype hands on5
Prototype – hands on
  • wait(((X-80)/20) * Derivative);//Version 2

Flow Control Algo’ examples:

when(received(NEW_IO){

If (X < 80){//Threshold is 80

send(TransferReady);

}

else{

wait(((X-80)/20) * LatencyParam);

send(TransferReady);

}

}