This document presents a multi-objective virtual machine (VM) placement approach using Particle Swarm Optimization (PSO) and CloudSim. PSO is applied to optimize multiple objectives like energy consumption, load balancing, and resource utilization when placing VMs on physical machines in a cloud computing environment. CloudSim, a cloud simulation tool, is used to test the PSO-based VM placement approach without using real cloud infrastructure and resources. The approach involves initializing VM demands and particle positions, evaluating fitness values, and updating particle velocities and positions through iterations to determine the optimal VM placement solution.

1. MULTI-OBJECTIVE VM PLACEMENT
USING CLOUDSIM
PRESENTED BY:
ANSARI KHALID FAISAL

2. What is cloud Computing?
Cloud computing is the on-demand delivery of compute power, database,
storage, applications, and other IT resources via the internet with pay-as-you-
go pricing.
Fig. 1 Cloud Computing[1]

3. How Cloud Computing Works?
1. Stores information about
resources
2. Registry for Datacenters
Make Registry
Submit Tasks to
Datacenter
Directly interact with
datacenter
C1, C2
C1 C2

4. Why VM Placement Required?
The virtual machine placement problem can be treated as a bin packing problem,
where:
 Physical machines/hosts -- bins
 Virtual machines -- objects.

5. Objectives
 Minimizing Energy Consumption
 Maximizing Load Balancing
 Minimizing resource wastage(CPU,RAM and bandwidth)
 Maximizing profit
Then what is multi-objective VM Placement?

6. CloudSim (Cloud Simulator)[2]
 Performing Practical implementation directly is impossible in case of cloud
computing, because to access infrastructure one have to pay real
currency.
 CloudSim is the tool which simulates exact real world scenario on
Machine

7. Advantages of CloudSim
1) Test their services in repeatable and controllable environment
2) Free of cost
3) Tune the performance bottlenecks before deploying on real Clouds

8. Particle Swarm Optimization[3]
 The method was discovered through simulation of a simplified social
model.
 PSO has roots in two main component methodologies:
 Genetic algorithms
 Evolutionary programming

9. Fig. 2 Working of PSO[1]

10. PSO in Cloud Computing
 Each particle has two parameters (velocity V, position P).
 Each position is associated with a fitness value to evaluate the quality of
the solution.
 At the start of the PSO algorithm, each particle is positioned randomly in
the search space of the problem and an optimal solution is then
determined by iterating the initial solution.
 The particle moves depending on: the velocity, the local best position, and
the global best position.

11. Workflow for PSO
 The PSO receives the VM demands as an input.
 The algorithm starts from an initial population P of N particles, which contains a feasible
VM placement.
 Based on this initial population, the algorithm follows iterations to converge to a global
best position that represents the optimal placement of VMs.
 The algorithm has three principle steps:
 Initialization
 evaluation
 updating.
 The algorithm is executed sequentially until the maximum iteration number is done.

12. Conclusion and Future Work
 Upto now Multi-objective PSO is best approach to optimize multiple
objectives in cloud computing[4].
 Other Multi-objectives can be used for optimization.

13. ANY
QUESTIONS?

14. References
[1] https://en.wikipedia.org/wiki/Particle_swarm_optimization
[2] R. Buyya, R. Ranjan and R. N. Calheiros, “Modeling and Simulation of Scalable Cloud Computing
Environments and the CloudSim Toolkit: Challenges and Opportunities,” IEEE International Conference, 2009.
[3] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” IEEE International Conference, 1995.
[4] K. BRAIKI and H. YOUSSEF, “Multi-Objective Virtual Machine Placement Algorithm Based on Particle
Swarm Optimization”, IEEE International Conference, 2018