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The document describes an energy efficiency technique for cloud environments called Brownout/Lowest Utilization First Component Selection Policy (LUFCSP) implemented using CloudSim in Java. It determines the most underloaded and overloaded virtual machines (VMs) using utilization data and performs live migration to balance the workload and reduce overall energy usage. The process involves creating VMs, allocating cloudlets, determining the lowest and highest utilized VMs using LUFCSP, performing live migration for balancing using an algorithm, and running the simulation for results.

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Sunny Kushal
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40 views1 page

Readme

The document describes an energy efficiency technique for cloud environments called Brownout/Lowest Utilization First Component Selection Policy (LUFCSP) implemented using CloudSim in Java. It determines the most underloaded and overloaded virtual machines (VMs) using utilization data and performs live migration to balance the workload and reduce overall energy usage. The process involves creating VMs, allocating cloudlets, determining the lowest and highest utilized VMs using LUFCSP, performing live migration for balancing using an algorithm, and running the simulation for results.

Uploaded by

Sunny Kushal
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as TXT, PDF, TXT or read online on Scribd
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# Energy efficiency in cloud environment using Brownout/Lowest Utilization First

Component Selection Policy(LUFCSP) using CloudSim(Language-JAVA).

1.Our project uses CloudSim environment run in Eclipse(preferred) or any JVM


supported platform
. The aim of the project is to use the brownout algorithm and calculate the
individual Virtual Machine Instances(VM's)
/Hosts Energy Utilization.

2.Upon the data on the Utilizations of all the VM's/Host, the algorithm now
determines the overloaded
and the underloaded VM's/Host and accordingly does the live migration for balacing
the overall
overhead that otherwise results in energy loss/problems.

-----------------------------------------------------------------------------------
--------

#step 1(optional)://
Create the amount of VM's as per requirement by creating vm's using the vmlist
class in java,
allocate each of the vm's to the corresponding cloudlets and then by using the
broker object in java
that is responsible for task assignments to the vm's, bind the vm to the
cloudlet.

#step 2:
Using the runnerAbstract(java file), containing Adaptive
Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual
Machines in
Cloud Data Centers, which uses our algorithm of LUFCSP,detemines the lowest and
the overloaded Vm's.The individual energy Utilizations are determined.

#step 3:
Using the iqrmc(java file), containing the main vmallocation and selection policy
responsible for livemigration for energy conservation
, we perform the balacing.

#step 4:
Lastly, we customize our total simulation time(here 1500) in constant(java file)
and for taking user input of the number of vm's and hosts we modify the
randomrunner(java file).

// Run the iqrMc file for the results detemination//

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