Cloud computing and ant colony optimization

1. CLOUD SIMULATOR AND ANT
COLONY OPTIMIZATION
ALGORITHM
PRESENTED BY:
SYED BILALALI
ADEENA HUSSAIN

2. WHAT IS CLOUD SIMULATOR
CloudSIM provides a generalized and extensible
simulation framework that enables modeling,
simulation and experimentation of emerging cloud
Computing infrastructure and application services

3. WHAT IS LOAD BALANCER?
• Load balancing is a computer networking method
to distribute workload across multiple computers or
a computer cluster, network links, central processing
units, disk drives, or other resources.
• GOALS OF LOAD BALANCING:
• Achieve optimal resource utilization
• Maximize throughput
• Minimize response time
• Avoid overload
• Avoid crashing

4. ANT COLONY OPTIMIZATION
ALGORITHM FOR LOAD BALANCING
• A probabilistic technique for
solving computational problems
which can be reduced to finding
good paths through graphs.
• A member of swarm intelligence
methods, and it constitutes some
metaheuristic optimization

5. RESULTS BY USING ACO TECHNIQUE:
• Through using ACO algorithm we accomplished 3 parameters which are important for
load balancing purpose:
1. RESOURCE UTILIZATION:
An ACO is a better approach to provide the higher great ability in terms of usage of
virtual machine, bandwidth, number of clouds, memory, etc.
2. RESPONSE TIME:
An ACO load balancing is an efficient technique used to distribute workloads over
resources in a way that improve response time or make it minimum.
3. THROUGHPUT:
This increases the throughput of ACO algorithms on runtime reconfigurable meshes. The
increased throughput is used for repeated runs of ACO algorithms on a given set of
problem instances which significantly improves the obtained solution quality.

6. CONCLUSION
• ACO is a recently proposed metaheuristic approach for solving hard combinatorial
optimization problems.
• Artificial ants implement a randomized construction heuristic which makes
probabilistic decisions.
• ACO can find best solutions on smaller problems.

7. THANK-YOU