Prototype PresentationReplication Process

Simulator

Academic Advisor:Dr. Eitan Bachmet

Technical Advisor:Mr. Assaf Natanzon

Project Team:Adiel AshrovEtai HazanBenny Michali

:// .http replicationsimulation wordpres. /s com

Prototype PresentationStorage Replication Simulation

PrototypeGoalsConclusionsHands On

Prototype Demonstration  

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?

Simulation results verification: In order to ensure our simulation produces accurate

statistics we have sought for an expert opinion Assaf Natanzon (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 – 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 – 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 – Conclusions

I/O behavior modeling and recognition We have modeled the burstiness behavior

with a parameter received from the user [0..100].

Time slots concepts: Say for example burstiness is 40:

Prototype – hands on

I/O I/O I/O I/Ot10 t9 t8 t7 t6 t5 t4 t3 t2 t1

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 on

6 20 12 8 7 17 30 15 10 40t10 t9 t8 t7 t6 t5 t4 t3 t2 t1

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.

Entropy Volume0.3488 12.9627 22.9560 31.2457 42.2907 51.6243 6

2.29655 72.3000 81.5774 91.4892 10

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 on

Different Flow Control Alog’ Support cont.

Prototype – hands on

Series1

0300600900

Lan

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Flow Control Algo’ examples:when(received(NEW_IO){

If (X < 80){//Threshold is 80send(TransferReady);

}else{

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

send(TransferReady);}

}

Prototype – hands on

wait(((X-80)/20) * Derivative);//Version 2

Questions?