1. Modelling LIT Cloud Infrastructure at JINR and Evaluating the Model
Reporter: Vagram Airiian
Master’s student,
Dubna State University
GRID’

2. Motivation
To create a model comprising LIT Cloud Infrastructure allowing to scrutinize its functioning to develop and apply a virtual machines migration scheme for freeing a maximum amount of computing resources in order to reutilize them.

3. Problem Model Current configuration
30 x: 4 CPU cores, 8 GB RAM, 250 GB HDD
3 x: 2 CPU cores, 4 GB RAM, 800 GB HDD
1 x: 4 CPU cores, 8 GB RAM, 2330 GB HDD
4 x: 12 CPU cores, 36 GB RAM, 1000 GB HDD
1 x: 24 CPU cores, 24 GB RAM, 1000 GB HDD
Total: 202 CPU cores, 428 GB RAM, GB HDD
Model

4. Frameworks Comparison
CloudSim
GreenCloud
iCanCloud
SimGrid
Platform
any
NS2
OMNET, MPI
Language
Java
C++/OTcl
C++
C, Lua, Java, Ruby
Availability
Open Source
Graphical Mode
Limited (CloudAnalyst)
Limited (Nam)
Full
Limited
Physical Models
None
(plugin)
Parallel Experiments No
Yes
Energy Consumption
VM Live Migration

5. Frameworks Comparison (2)
CloudSim
GreenCloud
SimGrid
Language
Java
C++/OTcl
C, Lua, Java, Ruby
Distribution
Source code
Source code / prebuilt VM
Source code / prebuilt binary
Stable Release
Latest Release
Flexibility
Some
Full

6. SimGrid http://simgrid.gforge.inria.fr/documentation.php
SimGrid Functional Organization:
MSG: user-friendly syntaxic sugar
Simix: processes, synchronization
SURF: resources usage interface
LMM: linear systems solver

7. SimGrid Example http://simgrid.gforge.inria.fr/documentation.php
Platform description:
public class Simulation
extends Process {}
Main() routine:
org.simgrid.msg.Process

8. Simplified Model of Cloud
Model configuration
5 host types
4 HDD and 4 RAM types
39 hosts combined
1 router connecting all hosts
1 link type – 1 Gbps
39 routes (780 auxiliary routes)

9. Migration Algorithm
Pre-copy algorithm (SimGrid’s default)

10. Model Evaluation
Migration game

11. Bin Packing Problem
Given: n objects need to be placed in bins of capacity V each. Object i requires Vi units of bin capacity.
Object: determine the minimum number of bins needed to accommodate all n objects.

12. To Do
Extend the model with a definition of host capacity and develop our own migration scheme based on bin packing algorithms in respect of long-time statistics.
Investigate OpenVZ and KVM usage in the cloud and apply the developed scheme to them.

13. Thank You for Attention!

14. References
Plattner René. A Comprehensive Grid and Network Simulation Tool for Workflow based Applications – Master’s Thesis in CS, Distributed & Parallel Systems Group, Institute of Computer Science, the University of Innsbruck, 2007.
William Voorsluys, James Broberg, Srikumar Venugopal, Rajkumar Buyya. Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation – arXiv: [cs.DC], 9 December 2011.
Anton Beloglazov, Jemal Abawajy, Rajkumar Buyya. Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing – Future Generation Computer Systems, Vol. 28, Issue 5, May 2012, Pages 755–768.
Anton Beloglazov. Energy-Efficient Management of Virtual Machines in Data Centers for Cloud Computing – PhD Thesis in CS, Department of Computing and Information Systems, the University of Melbourne,
Astrikov D.Yu., Kuz'min D.A., Panasyuk A.I. Modelirovanie sistemy planirovaniya raspredelennogo vysokoproizvoditel'nogo vychislitel'nogo kompleksa [Simulation of a scheduling system of the distributed high-performance computing system]. Doklady Akademii Nauk Vysshei Shkoly Rossiiskoi Federatsii – Proceedings of the Russian Higher School Academy of Sciences, 2014, no. 2-3 (23-24), pp
P.A. Mihailov, G.I. Radchenko. Modeling and Performance Evaluation of Cloud Systems – Bulletin of the South Ural State University, 2014, Vol. 3, no. 3, pp. 109–123
Xin Lu and Zhuanzhuan Zhang, A Virtual Machine Dynamic Migration Scheduling Model Based on MBFD Algorithm – International Journal of Computer Theory and Engineering, Vol. 7, No. 4, August 2015