Cloud sim & greencloud
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First National Workshop of Cloud ComputingAmirkabir University of Technology
Persented by: Neda [email protected]
CloudSim: A Toolkit for Modeling and Simulation of
Cloud Computing Environments
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OutLine
• Introduction• Related Work• CloudSim Architecture• CloudSim Modelings• Design and Implementation• CloudSim Steps• Conclusions and Future works• Green Cloud
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Introduction(1/2):Cloud
• 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 requirements.
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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: Simulation Tool
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Related Works
Grid simulators: GridSim SimGrid OptoSim GangSim
But none of them are able to isolate the multi-layer service abstractions(SaaS/PaaS/IaaS) differentiation and model the virtualized resources required by Cloud. So:
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Main Contribution: CloudSim
A holistic software framework for modeling Cloud computing environments
And Performance testing application services.
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Features & AdvantagesFeatures• Discrete Time Event-Driven• Support modeling and simulation of large scale Cloud
computing environments, including data centers• Support simulation of network connections among
simulated elementsAdvantages• Time effectiveness• Flexibility and applicability• Test policies in repeatable and controllable environment• Tune system bottlenecks before deploying on real
clouds
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Layered CloudSim Architecture(1/7)
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Modeling in Cloudsim (1/5)
Modeling DataCenter Modeling VM Allocation Modeling Network Behavior Modeling Dynamic Workloads Modeling Power Consumption
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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
Availa
ble D
atace
nter
s
Allocation
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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
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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)
VMo MIPS, pesNumber(no. of cpu), Ram(MB), BW(MB/s)
Cloudleto Length (MI), pesNumber, input Size, output Size
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VM Allocation Modeling• Time Shared policy• Space Shared Policy
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Simulation Setup:
========== OUTPUT ==========Cloudlet ID STATUS Data center ID VM ID Time Start Time Finish Time
0 SUCCESS 2 0 2 0.1 2.12 SUCCESS 2 0 2 0.1 2.11 SUCCESS 2 1 2 0.1 2.13 SUCCESS 2 1 2 0.1 2.1
*****Datacenter: Datacenter_0*****User id Debt
3 2051.2
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
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Network Modeling
• Latency Matrix
Delay time from entity i to entity j
Entity i Entity j
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Dynamic Workload Modeling• The Strategy is to Vary VM
Utilization!
25% 43% 60% 30% 10% 90% ….
Delay= not all the time, CPU is
utilized
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Design and Implementation(1/2)CloudSim Class Design Diagram
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Design and Implementation(2/2)Simulation Data Flow
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Design and Impelementation(3/4) CloudSim Sequence Diagram
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Conclusion
Time effectiveness
Flexibility and applicability
Test services in repeatable and controllable
environment
Tune system bottlenecks before deploying
on real clouds
Experiment with different workload mix
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Green Cloud
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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 “Revolutionizing Data Center
Energy Efficiency” :• A typical data center consumes as much energy as 25,000
households!!!
• The total energy bill for data centers in 2010 was over $11 billion and energy costs in a typical data center doubles every five years.
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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.
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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
Power
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Levels of Power Consideration(1/2):System level
System level
DPMs
DVS
DPS
DVFS
DCD
SPMs Low Level Design: Gates,Transistor
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.
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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 = * Vdd2 * f
• Vdd : the supply voltage• f : the number of clock cycle
• An example
5.02
10ms 25ms
deadlinepower
power deadline
10ms 25ms
(a) Supply voltage = 5.0 V (b) Supply voltage = 2.0 V
2.02
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Levels of Power Consideration(2/2): DataCenter Level
Data center level
Virtualization
System resources
Target systems
Goal
Power saving techniques
Workload
Yes
No
Multiple resources
Single resource
Homogeneous
Heterogeneous
Minimize power / energy consumption
Minimize performance loss
DVFS
Meet power budget
Resource throttling
DCD
Arbitrary
Real-time applications
HPC-applications
Workload consolidation
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A Key to Power Saving!
Power On Power Off
Pool of physical computer
nodes
Virtualization layer (VMMs, local resources managers)
Consumer, scientific and business applications
Global resource managers
User User User
VM provisioning SLA negotiation Application requests
Virtual Machines
andusers’
applications
Consolidation
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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
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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)
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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
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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.
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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/planetlab").getPath();
String outputFolder = "output"; String workload = "20110303"; // PlanetLab workload
Number of Samples
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References R. Buyya, A. Beloglazov, J. Abawajy,
Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges, 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 Computing Systems , 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 Cloud-oriented Data Centers, Journal of Parallel and Distributed Computing, 71(6):732-749, Elsevier Press, Amsterdam, The Netherlands, June 2011.
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Thanks for your attention!
Any Questions , Suggestions and Comments?