It was held in first conference of Amirkabir university in 31october and 1 november by neda maleki.

1. First National Workshop of Cloud Computing
Amirkabir University of Technology
Persented by: Neda Maleki
nedamaleki87@gmail.com
CloudSim: A Toolkit for Modeling and
Simulation of
Cloud Computing Environments

2. OutLine
• Introduction
• Related Work
• CloudSim Architecture
• CloudSim Modelings
• Design and Implementation
• CloudSim Steps
• Conclusions and Future works
• Green Cloud

3. Introduction(1/2):Clo
ud
• Cloud computing delivers:
XaaS
• X :{Software, Platform,
Infrastructure }
So users can access and
deploy applications from
anywhere in the Internet
driven by demand and QoS

4. Introduction(2/2):Why
Simulation?
Cloud Providor Challenges:
•Maintain Quality of Service
•Efficient Resourse Utilization
•Dynamic Workload
•Violation of Service Level Agreement
•Difficulties in Testing
It’s not possible to perform benchmarking
experiments in repeatable, dependable, and
scalable environment using real-world Cloud.
Possible alternative: SimulationTool

5. Related Works
Grid simulators:
GridSim
SimGrid
OptoSim
GangSim
But none of them are
able to isolate the
multi-layer service
abstractions(SaaS/Pa
aS/IaaS)
differentiation and
model the virtualized
resources required by
Cloud. So:

6. Main Contribution:
CloudSim
 A holistic software framework for
modeling Cloud computing environments
And
Performance testing application services.

7. Features & Advantages
Features
• Discrete Time Event-Driven
• Support modeling and simulation of large scale
Cloud computing environments, including data
centers
• Support simulation of network connections among
simulated elements
Advantages
• Time effectiveness
• Flexibility and applicability
• Test policies in repeatable and controllable
environment
• Tune system bottlenecks before deploying on real
clouds

8. Layered CloudSim Architecture(1/7)

9. Modeling in Cloudsim (1/5)
 Modeling DataCenter
 Modeling VM Allocation
 Modeling Network Behavior
 Modeling Dynamic Workloads
 Modeling Power Consumption

10. CloudSim Steps(1/2)
a
broker
(VMs , Apps)
Cloud
Information
Service(CIS)
Is Registered all
Datacenters and
their
characteristics
Cloud
Datacenter A
Cloud
Datacenter B
Cloud
Datacenter C
Query
AvailableDatacenters
Allocation

11. Allocation Policies: Enough
Capacity,Ram,Storage,Bandwidth
VM1,V10,VM6,VM7
VM2,VM4
VM9,V3,VM5
VM8
Scheduling Policies: Sharing of Host Mips
between VMs
• Space Shared
•Time Shared

12. DataCenter Modeling
 Number of Hosts, VMs and Cloudlets (tasks)
o Host(mips, ram, storage, bandwidth)
o Datacenter(arch, os, vmm, hostlist, cost
mem/bw/storage)
 VM
o MIPS, pesNumber(no. of cpu), Ram(MB),
BW(MB/s)
 Cloudlet
o Length (MI), pesNumber, input Size, output

13. VM Allocation Modeling
• Time Shared policy
• Space Shared Policy

14. Simulation Setup:
========== OUTPUT ==========
Cloudlet ID STATUS Data center ID VM ID Time Start
Time Finish Time
0 SUCCESS 2 0 2
0.1 2.1
2 SUCCESS 2 0 2
0.1 2.1
1 SUCCESS 2 1 2
0.1 2.1
3 SUCCESS 2 1 2
0.1 2.1
*****Datacenter: Datacenter_0*****
 1 datecenter
 1 dual-core host, each core'mips: 1000
 2 vm, mips:1000
 4 cloudlets, length: 1000mips
 core1 deal with two cloudlets(t1 and t2), and core2 deal with
the other two cloudlets(t3 and t4), so, all cloudlets should
finished at 2.1s

15. Network Modeling
• Latency Matrix
Delay time from entity i to
entity j
Entity i Entity j

16. Dynamic Workload Modeling
• The Strategy is to Vary VM Utilization!
25% 43% 60% 30% 10% 90% ….
Delay= not all the
time, CPU is utilized

17. Design and Implementation(1/2)
CloudSim Class Design Diagram

18. Design and Implementation(2/2)
Simulation Data Flow

19. Design and Impelementation(3/4)
CloudSim Sequence Diagram

20. Conclusion
 Time effectiveness
 Flexibility and applicability
 Test services in repeatable and
controllable environment
 Tune system bottlenecks before
deploying on real clouds

21. Green Cloud

22. Power(1/4):Powering Cloud
Infrastructure
• Modern data centers, operating under the
Cloud computing model, are hosting a variety
of applications ranging from those that run for
a few seconds (e.g. serving requests of web
applications such as e-commerce and social
networks portals) to those that run for longer
periods of time (e.g. large dataset
processing).
• So, Cloud Data Centers consume excessive
amount of energy:
• According to McKinsey report on “Re vo lutio niz ing
Data Ce nte r Ene rg y Efficie ncy” :
• A typical data centerconsumes as much energy as
25,000 households!!!

23. Power (1/2)
 Data centers are not only
expensive to maintain, but
also unfriendly to the
environment.
 High energy costs and huge
carbon emission are incurred
due to the massive amount of
electricity needed to power and
cool the numerous servers
hosted in these data centers.

24. Power Consumption in the Datacenter
Compute resources and
particularly servers are
at the heart of a
complex, evolving
system! They
Consumes most power.
Where Does the Go?
Google Datacenter
2007
Pow
er

25. Levels of Power
Consideration(1/2):
System level
 The objective of PA computing/communications is to improve
power management and consumption using the awareness of
power consumption of devices.
 Recent devices (CPU, disk, communication links, etc.) support
multiple power modes.

26. DVS(Dynamic Voltage Scaling)
• DVS (Dynamic Voltage Scaling) technique
– Reducing the dynamic energy consumption by lowering the supply voltage at the
cost of performance degradation
– Recent processors support such ability to adjust the supply voltage dynamically.
– The dynamic energy consumption = α * Vdd2
* f
• Vdd : the supply voltage
• f : the number of clock cycle
• An example
5.02
10ms 25ms
deadline
power
power deadline
10ms 25ms
(a) Supply voltage = 5.0 V (b) Supply voltage = 2.0 V
2.02

27. Levels of Power
Consideration(2/2):
DataCenter Level

28. A Key to Power Saving!

29. WWW: Three Sub Problems
• When to migrate VMs?
• Host overload detection algorithms
• Host underload detection algorithms
• Which VMs to migrate?
• VM selection algorithms
• Where to migrate VMs?
• VM placement algorithms

30. Algorithms in each w
 Host overload detection
 Adaptive utilization threshold based algorithms
 Median Absolute Deviation algorithm (MAD)
 Interquartile Range algorithm (IQR)
 Regression based algorithms
• Local Regression algorithm (LR)
• Robust Local Regression algorithm (LRR)
 Host underload detection algorithms
 Migrating the VMs from the least utilized host
 VM selection algorithms
 Minimum Migration Time policy (MMT)
 Random Selection policy (RS)
 Maximum Correlation policy (MC)
 VM placement algorithms
 Heuristic for the bin-packing problem – Power-Aware Best Fit
Decreasing algorithm (PABFD)

31. Performance Metrics
SLA violation metrics
• Overloading Time Fraction (OTF) - the time
fraction, during which active hosts experienced
the 100% CPU utilization
• Performance Degradation due to VM Migrations
(PDM)
• A combined SLA Violation metric (SLAV):
SLAV = OTF * PDM
A combined metric that captures both energy
consumption and the level of SLA violations,
Energy and SLA Violation (ESV):
ESV = Energy * SLAV

32. Real Workloads
• Workload traces from more than 1000 VMs from
servers located in more than 500 places around the
world.
• The data were obtained from the CoMon project, a
monitoring infrastructure for PlanetLab
• PlanetLab is a distributed execution environment for
doing benchmarked experiments . Totally it is a
global research network that supports the
development of new network services.
• A Data Center consisting 800 heterogeneous
physical servers containing HP ProLiant ML110 G4
and HP ProLiant ML110 G5 servers.
• More than 1000 Heterogeneous VMs corresponding
to Amazon EC2 instance types.

33. Content of WorkLoad Files
 These files contain CPU utilization values measured
every 5 minutes in PlanetLab's VMs for one day so:
One day=24 hours= 5minutes*288
 CloudSim contain a class called :
UtilizationModelPlanetLabInMemory
which can be used to read those workload traces.
 An example: String inputFolder =
Dvfs.class.getClassLoader().getResource("workload/pla
netlab").getPath();
 String outputFolder = "output";
 String workload = "20110303"; // PlanetLab workload
Number of
Samples

34. References
 R. Buyya, A. Beloglazov, J. Abawajy,
Energy-Efficient Management of Data Center Resources for Cloud Compu
, Proceedings of the 2010 International
Conference on Parallel and Distributed
Processing Techniques and Applications
(PDPTA2010), Las Vegas, USA, July 12-
15, 2010.
 A. Beloglazov, R. Buyya, Y. Lee, A.
Zomaya,
A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Com
, Advances in Computers, Volume 82, 47-
111pp, M. Zelkowitz (editor), Elsevier,
Amsterdam, The Netherlands,March
2011.
 S. Garg, C. Yeo, A Anandasivam, R.
Buyya,
Environment-Conscious Scheduling of HPC Applications on Distributed Cl
, Journal of Parallel and Distributed
Computing, 71(6):732-749, Elsevier
Press, Amsterdam, The Netherlands,
June 2011.

35. Thanks for your attention!
Any Questions , Suggestions and
Comments?