Potential of AI (Generative AI) in Business: Learnings and Insights
Cloud computing(bit mesra kolkata extn.)
1. Bit mesra ranchi,kolkata extn.
Under The Guidance of:
Ajanta De Sarkar
Soumya Ray
Presented By: Group No-24
Udita Chakarborty (MCA/3508/10)
Ashutosh Kumar (MCA/3539/10)
Puja Kumari (MCA/3543/10)
Shashi Ranjan (MCA/3545/10)
2. • Internet based computing
• Enables convenient on-demand network access to
a shared pool of configurable computing resources
e.g., networks, servers, storage, applications, and
services
• Virtualized computing platform
• Business Model
Cloud Computing
2
3. Cloud Computing(cont..)
• Cloud Infrastructure:
Public Cloud
Private Cloud
• Major cloud providers:
Amazon
Google
Microsoft
3
4. Some key aspects of cloud computing
• on-demand network
• Scalable use of computing resources
• Pay-per-use concept
4
9. Service Level Argreements(Cont..)
• Negotiation between service provider and service
consumers
• Service integrator offers an end-to-end SLA to its service
consumers
end-to-end SLA depends on the SLAs that the service integrator
has with its service provider
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11. Users and their Roles
• Four types of business roles:
A Cloud Service Consumer (CSC)
A Cloud Service Provider (CSP)
A Cloud Service Integrator (CSI)
A Cloud Service Broker (CSB)
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12. Advantages
• Easy to maintain
• Easy access
• Ideal for small business
• Location independence
• Provides flexibility 12
15. Why we need load balancing??
• The steady growth of the Internet
low response times
network congestion and
disruption of services
• For achieving Green computing in clouds
Limited Energy Consumption
Reducing Carbon Emission 15
17. Some key aspects of load balancing
• Network Load Balancing or Server Load Balancing
• Reassigning load to each individual node
• Provided by dedicated software or hardware
e.g. multilayer switch ,DNS server etc.
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18. Some key aspects of load balancing
(Cont..)
• Make resource utilization effective
• Improve the response time
• Dynamic in nature
• Load of resources considered can be:
CPU load,
amount of memory used,
delay or Network load
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19. Goals of load balancing
• Availability
• To improve the performance substantially
• To maintain the system stability
• To accommodate future modification in the
system
• Build a fault tolerant system by Creating backups 19
20. Types of load balancing algorithms
• Depending on who initiated the process:
sender Initiated
receiver Initiated
Symmetric
• Depending on the current state of the system:
static
dynamic
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21. Usefulness of study of simulation
• Creating and experimenting model of a physical system
• To test scenarios that might be particularly difficult or
expensive
• Provide graphical applications
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22. Need of Simulators
• Difficult to access exact cloud computing environment
• Easily mimicking cloud testbeds with different VMs
• To easily include modifications for complex scenario
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24. Scenarios
Scenario a: sole execution of sample application program
Scenario b: execution of sample application program
with lightly loaded application
Scenario c: execution of sample program with heavily
loaded application
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25. Why Green Cloud?
1. A simulation environment
2. No provisioning for observing clouds for their energy efficiency
3. Offers a thorough investigation of workload distributions
4. Minimise energy consumption
5. Packet-level simulations of communications in the data center infrastructure
27. Why iCanCloud?
• Used to simulate and model systems
•Optimizes the trade-off between cost and performance
• Lets the users to take an easy decision for paying
corresponding budget of machines
• Provides flexibility, scalability, performance and
usability
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28. Why iCanCloud?(Cont..)
• Customizable VMs can be used to quickly simulate uni-
core/multi-core systems
• Provides a user-friendly GUI
• Conducts large experiment
• provides a flexible global hypervisor for integrating any cloud
brokering policy
• reproduces the instance types provided by a given cloud
infrastructure 28
30. Why CloudSim?
• An extensible simulation toolkit that enables modelling
and simulation of Cloud computing systems and
application provisioning environments.
• Can test the performance of a newly developed
application service in a controlled and easy to set-up
environment.
• Requires very less effort and time to implement Cloud-
based application provisioning test environment
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31. Cont..
• Used for modelling and simulation of large scale computing
environments
• Facilitates simulation of federated cloud environment
• Supports simulation of network connections among the simulated
system elements
• Support for modelling and simulation of energy-aware
computational resources are also available
33. Comparison of Features
Parameters GreenCloud iCanCloud CloudSim
Platform NS2 OMNET,MPI _
Language C++/OTcl C++ Java
Availability Open Source Open Source Open Source
Graphical Support Limited(through
Nam)
Full Limited(through
CloudAnalyst)
Support for Power
Consumption
Yes Yes WiP
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36. Novel Load Balancing Approach
• Cloud Provider (Windows) and Resource Provider
(Linux)
• “Top” command executed on Resource Provider
• Getting the “Dynamic Resource Information” into
xml file
• Connection established between Cloud Provider and
Resource Provider through socket connection
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37. Novel Load Balancing Approach
(Cont..)
• Transferring xml file from Resource Provider to
Cloud Provider
• Cloud Provider checks xml file
• Resource Table is maintained by the Cloud
Provider
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48. Conclusion
• Concludes features, architectures and performance evaluation
graph of different existing cloud simulators
• Predict the outcome of each simulator under different
scenarios
• Compares the different simulators
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49. Conclusion(Cont..)
• Future Work:
Improvement from the cloud consumer sides
Service level agreements between cloud provider and cloud
consumer
• Limitations:
Message passing overhead
A part of the Approach
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50. Reference
1. Aarti Khetan, Vie Bhushan and Subhash Chand Gupta “A novel Survey
on Load Balancing in Cloud Computing” International Journal of
Engineering Research & Technology (IJERT) Vol. 2 Issue 2,February-
2013.
2. Anthony T.Velte, Toby J.Velte, Robert Elsenpeter, “Cloud Computing:A
Practical Approach”, TATA McGRAW-HILL Edition 2010.
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51. Reference(Cont..)
4. Kliazovich, D., Bouvry, P., Khan, S.U.: “iCanCloud: A Flexible and
Scalable Cloud Infrastructure Simulator.” J Grid Computing (2012)
10:185–209 DOI 10.1007/s10723-012-9208-5.
5. Mell, P.; and Grance, T. (2009, 7 10). The NIST Definition of Cloud
Computing, from NIST Information Technology Laboratory,
http://www.nist.gov/itl/cloud/upload/cloud-def-v15.pdf,retrieved
onApril 2011.
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