1. ENERGY EFFICIENT AND TRAFFIC
AWARE VIRTUAL MACHINE
MANAGEMENT IN CLOUD COMPUTING
PROJECT ADVISOR :
Dr. Melody Moh
COMMITTEE MEMBERS :
Dr. Robert Chun
Mr. Giridhar Jayavelu
SWETHA KOGATAM

2.  INTRODUCTION
 PROBLEM & SOLUTION
 MAIN ALGORITHMS
 RESULTS
 CONCLUSION & FUTURE STUDIES

3.  Cloud computing has revolutionized the technology industry by enabling on-
demand provisioning of elastic computing resources
 Funded by Google, Lawrence Berkeley National Laboratory investigated the
energy impact of cloud computing

4. Cloud data centers still consume huge amounts of electrical energy resulting in
 High Operating Costs
 Carbondioxide (CO2) emissions
 Energy consumption by data centers worldwide has risen
by 56% from 2005 to 2010
 Around 1.1% - 1.5% of the global electricity usage in 2010

5.  INTRODUCTION
PROBLEM & SOLUTION
 MAIN ALGORITHMS
 RESULTS
 CONCLUSION & FUTURE STUDIES

6. Problem :
Need for the cloud data centers to reduce the power consumption to
save the costs and reduce CO2 emissions
How can this be solved ?
 CPU Utilization is the only dynamic resource
Focusing on optimizing the resource allocation and utilization
But..Performance is not relied only on CPU Utilization

7. With applications that require high communication, the communication cost
can influence the overall performance.
Ex: 3- Tier Web Application
One of the solutions to improve the effective utilization of resources and reduce
energy consumption is “Dynamic consolidation of Virtual Machines”
We propose the dynamic consolidation of virtual machines which
considers both the traffic and power among the VMs by keeping
the SLA violations at its lowest possible

8. Beloglazov has proposed the Dynamic VM consolidation algorithms
based on the analysis of historical data of the resource usage by VMs.
The whole dynamic consolidation problem is broken down into 4 small
problems i.e.,
1. Host Underload detection
2. Host Overload detection
3. VM Selection
4. Placement algorithms

9. Recently Huang , Wu and Moh has extended the work of Beloglazov
and proposed new dynamic VM consolidation heuristics and proposed :
 Least increase in Power by sorting the Hosts as placement policy
 Best Fit Host allocation policy
 Best fit VM allocation policy
Trong Vu also extended the work of Beloglazov and proposed a
mechanism considering power and the communication latency between
the VMs
 But considered only the VMs from underutilized hosts and not the over
utilized hosts.

10.  INTRODUCTION
 PROBLEM & SOLUTION
MAIN ALGORITHMS
 RESULTS
 CONCLUSION & FUTURE STUDIES

11. 1. Developed clustering mechanims to cluster the VMs based on the
communication/traffic factor
2. Enhanced the existing Least Increase in Power with Host Sort allocation
policy by implementing the clustering mechanism – LIP-VMCL (HostSort)
3. Adopted and enhanced two VM placement heuristics , the Best Fit Host for
the VMClusters (BFH-VMCL) and the Best Fit VMClusters (BF-VMCL)
placement algorithms
4. Developed a new dynamic mechanism to create new hosts

12. 1. A mechanism to find the over utilized hosts and underutilized hosts based on
the CPU utilization
2. A selection policy which selects the VM clusters to be migrated
3. Three different allocation policies
I. Least Increase in Power with Host Sort Allocation policy
II. Best Fit Host for VM Clusters Allocation policy
III. Best Fit VM Cluster Allocation policy.
4. Designed a dynamic mechanism to create a new host

14. LEAST INCREASE IN POWER WITH HOST SORT FOR VM
CLUSTERS : LIP_VMCL(HOST SORT)
Input : Migrating VM list , AvailableHostList
Step1 : Sort hosts in descending order of current CPU utilization and select the
VMs to be migrated
Step 2 : Cluster the VMs using VM Clustering algorithm
Step 3 : Calculate and store the increased power of the host by allocating the
VMCluster
Step 4 : Find the host with the least increase in power
Output : Migration Map

15. Input : Migrating VM list , AvailableHostList
Step1 : Sort hosts in descending order of current CPU utilization and select the
VMs to be migrated
Step 2 : Cluster the VMs using VM Clustering algorithm
Step 3 : Calculate predicted utilization of the host if that VM Cluster is allocated
using local regression method.
Step 4 : Find the host with the highest predicted utilization (and <1)
Output : Migration Map

16. Input : Migrating VM list , AvailableHostList
Step1 : Sort hosts in descending order of current CPU utilization and select the
VMs to be migrated
Step 2 : Cluster the VMs using VM Clustering algorithm
Step 3 : Find optimal combination of VMClusters for that host using dynamic
programming.
Output : Migration Map

17.  Step 1: Finding over-utilized and underutilized hosts
If host is over-utilized then
 Step 2: Select the VMs to be migrated from over-utilized hosts and use VM
Clustering Algorithm to cluster the VMs
 Step 3: Select the VMCluster(s) to be migrated from Migrating VMCluster List
using VMCluster Selection algorithm
 Step 4: Placement of VMCluster(s) from over-utilized hosts to Available host(s)
using Energy and Traffic aware VM Placement algorithm

18.  Step 5: Find the most under-utilized host and select all VMs from that host and
generate VMCluster(s) using VMClustering Algorithm
 Step 6: Placement of VMCluster(s) from the under-utilized host to other host(s)
using Energy and Traffic aware VM placement algorithm

19.  INTRODUCTION
 PROBLEM & SOLUTION
 MAIN ALGORITHMS
RESULTS
 CONCLUSION & FUTURE STUDIES

27.  INTRODUCTION
 PROBLEM & SOLUTION
 MAIN ALGORITHMS
 RESULTS
CONCLUSION & FUTURE STUDIES

28.  Number of migrated VMs reduced significantly for BFH_VMCL and
BFV_VMCL compared to LIP-HostSort_VMCL.
 The SLA violations reduced by almost 50%.
 BFH_VMCL has performed better considering minimal SLA violations and
better efficiency in terms of energy.
 BFV_VMCL is the best energy efficiency model at the price of high SLA
violations.

29.  Further work can be concentrated on improving the algorithms considering
the Storage IOPS
 Further research can be done towards the betterment of BF_VMCL algorithm
to reduce the SLA Violations
 Current approach is not yet implemented in real time data centers. The real
time situations might demand different kind of approaches.
This can be considered as potential future work towards an efficient green
cloud computing solution.