live virtual machine migration based on future prediction of resource requirements in cloud...

8/12/2019 Live Virtual Machine Migration Based on Future Prediction of Resource Requirements in Cloud Datacenter

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Project Guide:Dr. G R Gangadharan

Institute for Development & Research in BankingTechnology, Hyderabad

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Project Trainee:

Tapender Singh Yadav

B.Tech IIIrd Year,

Department of Computer Science & Engineering,

Indian Institute of Technology, Patna


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Virtual Machine Software-based emulation

Creation and Management done by Hypervisor (alsoknown as Virtual Machine Manager)

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Hardware real physical machine

Virtual Machine Manager (VMM)

Virtual Machine 1 Virtual Machine 2

Operating System 2Operating System 1

APP APP APP APP APP APP APP APP


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VM Migration and its need Migrating VM from one host to another host is known

as Virtual Machine Migration

Why we need VM Migration?Dynamically changing workloads

Maintenance of Host Server

Server downtime due some fault

Ease in migrating OS + Applications from an outdatedhost to new host.

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Current Scenarios

Lot of research has been done on:

Workload balancing based on % CPU utilization

Migration of VM(s) over LAN or WAN

VM placement based on resource demand prediction But there are some factors where the work is not much

significant.

Less focus on dynamic on-demand requests for

applicationsLess focus on providing better Quality of Service (QoS)and minimizing the number of Service Level Agreement(SLA) violations

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Our Idea and Problem Statement

There are MVirtual Machines (VMs) and their correspondingresource usage based on the number of cores (processing elements)

used by them, in the past few days on each hour of the day. Basedon this historical data, we have to forecast the future resourcedemand for number of cores required by all the MVM(s) in thedatacenter using the Trend Seasonality Model.

Based on the forecasting result, we need to optimize the dynamic

allocation of VM to a best-fit host for migration from N availablehosts.

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Methodologies

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Forecasting Method

Future Demand of the VM(s) are computed based on thehistorical resource utilization of the VM(s)

Trend Seasonality Model used

Advantages of Trend Seasonality Model:

Small size of dataset

Efficient Prediction

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Forecasting Method Continued

Trend is periodic change in the series which evolves slowly.

Seasonality is the periodic recurrence of a pattern for each periodover the time.

The raw historical data is composed of various componentssuch as seasonality, trend, irregularities and cyclic oscillations.

In order to forecast the future resource demand efficiently, weneed to get rid of these components i.e., we need to decompose

(deseasonalize) the raw historical data first.

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Forecasting MethodDeseasonalizing theraw data Following steps are followed for deseasonalizing the raw

data:

1. Compute a centred 24-period moving average for all possibleperiods for all given days.

2. Compute the ratio of actual resource demand in each period tothe centred moving average obtained in Step 1.

3. Average the above ratios for periods 1-24 for all given days.4. Round-off of the averaged ratios from Step 3 to obtain a 24-

period seasonal index values.

5. Divide the actual resource usage by the seasonal indexes to get

the deseasonalized resource usage levels. 9


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Forecasting MethodTrend Line Equation To find the trend component from the raw data, we use trend

line equation.

Trend Line equation is obtained by applying the Simple LinearRegression (SLR) on the deseasonalized data and time variablet.

Trend Line equation is of the form:

= + where, A = vertical-intercept of the trend line

B = slope of the trend line

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Forecasting MethodFinal step

Multiply the resource demand trend level from previous stepwith the seasonal index for that period to include the seasonalityeffect and get the final forecast of the resource demand.

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Result of Prediction (Forecast)

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Obtained MSE 1.3

Forecast

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2

4

6

8

10

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1 4 7 10 13 16 19 22 1 4 7 10 13 16 19 22 1 4 7 10 13 1619 22 1 4 7 10 13 16 19 22 1 4 7 10 13 16 19 22

Day 1

Day 2Day 3

Day 4

Day 5

Forecast

No. of CPU Cores (a)

Resource Utilization of VM 1 (in # of cores used)

No.ofCPUcores

Resource Utilization of VM 1 (in # of cores used)

No.ofCPUcores


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ResultPrediction Step

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Period Hour Seasonal Index Trend

Component Forecast

Day 5 1 0.11 5.40 1

2 0.25 5.40 1

3 0.24 5.40 1

4 0.37 5.40 2

5 0.43 5.40 2

6 0.24 5.40 17 0.49 5.39 3

8 1.10 5.39 6

9 1.11 5.39 6

10 0.99 5.39 5

11 1.48 5.39 8

12 1.38 5.39 7

13 1.34 5.39 7

14 1.91 5.39 10

15 1.52 5.39 8

16 1.55 5.39 8

17 2.13 5.39 11

18 1.35 5.39 7

19 0.95 5.38 5

20 1.71 5.38 9

21 1.72 5.38 9

22 1.32 5.38 7

23 0.37 5.38 2

24 0.27 5.38 1


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Best optimized VM-host mapping and LiveVM Migration Algorithm

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Overview of the algorithm

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For each vmin thedatacenter, predictthe future demand

using trendseasonality model

Based on the futuredemands, create a list

of vm(s) which areneeded to be

migrated

Find the best-fit mapfor the vmto host,

and start themigration process


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Expected decrease in the power consumption due to switching offof the un-utilized hosts

Less server downtime during migration

Less Migration Time

More scalable and robust

Best for small and large scale expanding businesses

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Advantages of the proposed algorithm


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Conclusions We have proposed a resource demand forecasting technique, a

key step in optimizing the VM-host mapping before the actualLive VM migration could be triggered.

Our prediction technique employs data mining and statisticalmethods for forecasting (predicting) the future resource demandof the VM(s).

We proposed a Live VM migration algorithm, which based onthe future demands, will find the best host for the VM to meetits future demands.

Special care has also been taken in case of un-utilized hostswhich would be running unnecessarily and consuming power, toswitch off those hosts in order to save the power consumption

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Future Work

Future work on this problem includes experimental

implementation and testing of the proposed Live VM migrationpolicy using some of the known techniques and proposing anoptimized version of these techniques.

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Thank You

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live virtual machine migration based on future prediction of resource requirements in cloud...

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