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CHAPTER 4

ENERGY CURVE MODEL BASED DYNAMIC VM

CONSOLIDATION TECHNIQUE

4.1 INTRODUCTION

This chapter discusses energy modeling for Virtual Machine (VM)

consolidation. Apart from meeting expectations as an Infrastructure as a

Service (IaaS) like model, this work stands out from the already exhibited

works, where the cloud resource provider supports and offers various types of

free long-running services. Each procurement transforms time-fluctuating

CPU utilization into easy execution of jobs. A model was required to study

the temporal validity of the execution of the job that is executed in a session

on a distributed infrastructure. The temporal validity based on the timing

constraints of an SLA should be modeled in order to study it on an energy

perspective. Hence from the arrival and service rates we defined the multi-

informative VM on a host in the previous chapter and how Energy curve is

developed through SLA and VM migration is presented in this chapter. To

model the energy curve we define a cost function and its hypothesis. This

chapter examines the disentangled issue of verifying the opportunity to

relocate a VM from an oversubscribed host to a different data store. This will

help minimize the cost of energy utilization, and the cost put forth by the

cloud supplier due to violation of the QoS prerequisites outlined in the SLAs

that is expended. Expenses brought in by cloud providers are characterized by

parametric evaluation and hence, different trials have been introduced. The

experiments were exhibited by continuous workload, and an energy modeled

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consolidation was carried out. This model is a representation of an IaaS cloud,

where different individual cloud customers instantiate VMs, and the supplier

is not aware of the sorts of jobs sent on the VMs. Consolidation of VM

through SLA and VM migration is done by the Energy curve. The efficiency

of VM consolidation lies in the fact to maximize the time intervals between

Virtual Machine Migration from overloaded host servers. Although VMs

experience variable workloads, the maximum CPU capacity that can be

allocated to a VM should be less than the overall maximum CPU capacity.

4.2 AIM AND OBJECTIVES

This chapter presents a set of heuristics for the problem of energy

and performance efficient dynamic VM consolidation, which apply statistical

analysis of the observed history of system behaviour to infer potential future

states. The proposed algorithms consolidate VMs when needed to minimize

incredible usage by transforming possessions under QoS commitments. The

target compute environment is an Infrastructure as a Service (IaaS), where the

supplier is unaware of procurements and workloads served by the VMs, and

can simply watch them from an external point of view. As a result of this

property, IaaS scenarios are implied as being pseudo-aware of applications.

The confinements of these techniques are that they facilitate sub-optimal

conclusions and don't license the system manager to explicitly set a QoS

objective. Hence, the execution as to the QoS passed on by the calculation can

simply be equalized by suggesting tuning parameters of the applied host

overload detection algorithm. Interestingly, the philosophy proposed in this

area enables the calculations and the Energy Model regulator to explicitly

specify a QoS goal in terms of a workload independent QoS metric. The

underlying analytical model allows a derivation of an optimal randomized

control policy for any known stationary workload and a given state

configuration (Beloglazov & Buyya 2012). The literature survey shows

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legacy algorithms such as minimum migration time where the RAM and

Bandwidth are taken into consideration and implemented. Once a host

overload is detected, the next step is to select VMs to offload from the host to

avoid performance degradation. The proposed work strategy for the rest of the

chapters is as shown in Figure 4.1.

Figure 4.1 Proposed Work Strategies

4.3 PROBLEM STATEMENT AND OVERVIEW

The chapter deals with a real world setting where a control

algorithm does not have the complete knowledge of future events, and

therefore, has to deal with an online problem. Online problems need to be

optimized since the optimization depends on a workload which is online in

nature. Online algorithms are designed for online problems. Competitive

analysis is the way to characterize the performance and the efficiency of the

online algorithms by using machine learning techniques. This has helped

PlanetLab Trace

Utilization (VM and Host)

CQR Analysis (machine learning)

Energy Curve Model Algorithm

(MPP)

Parameter (EIRP)

CoMoN Project Server

Modelling

Prediction (Cost Function and Hypothesis)

Host Oversubscript

Results Reliability Analysis for Real World Scenarios

Work 4

Work 5

Work 6

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improve the efficiency of the competitive analysis by applying the knowledge

of future events. Control and internal memory are an algorithm's internal state

whereas in a real world situation the configuration of the algorithms is the

algorithm’s state (Beloglazov & Buyya 2012).

There are a few related works reviewed in this chapter that are

close to the proposed research direction, which are, however, different in one

or more aspects. Approaches to dynamic VM consolidation are application-

specific, whereas the approach proposed in this chapter is application-

agnostic, which is suitable for the IaaS model. Verma et al (2008) focused on

static and semi-static VM consolidation techniques, as these types of

consolidation are easier to implement in an enterprise environment. In

contrast, this chapter investigates the problem of dynamic consolidation to

take advantage of one-grained workload variations. Other solutions proposed

in the literature are centralized and do not have a direct way of controlling the

QoS, which are essential characteristics for the next generation data centers

and Cloud computing systems.

The issue of VM designation could be divided into two parts: the

first part is the attestation of new requests for VM provisioning and putting

the VMs on hosts, though the second part is the streamlining of the present

VM allocation. The first part could be seen as a canister pressing issue with

variable compartment sizes and costs. To settle it we apply an alteration of the

Best Fit Decreasing (BFD) figuring that is showed to use close to 11/9 • OPT

+ 1 s (where OPT is the measure of holders given by the optimal effect) (Yue

1991). In our conformity, the Modified Best Fit Decreasing (MBFD)

estimations, we sort all VMs in lessening solicit of their present CPU

utilizations, and disseminate each VM to a host that outfits the base construct

of energy use due to this dissemination. This allows leveraging the

heterogeneity of possessions by picking the most powerful capable data

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centers first. The complexity of the scheduling part of the MBFD algorithm is

n * m, where n is the measure of VMs that must be designated and m is the

measure of hosts. The change of the present VM placement is completed in

two steps: at the first stage we select VMs that must be moved; at the second

stage the picked VMs are situated on the hosts using the MBFD calculation.

To verify when and which VMs ought to be moved, we present three two-fold

limit VM choice arrangements. The basic idea is to set upper and lower

utilization thresholds for hosts and to keep the total utilization of the CPU by

all the VMs allocated to the host between these thresholds. The focus is to

recover free holdings in order to neutralize SLA violations due to the

hardening in scenarios during the utilization by VMs additions. The

complexity between the old and new courses of action structures a set of VMs

that must be reallocated. The new position is refined using live introduction of

VMs. In the next section we discuss over the proposed VM determination and

arrangement policies.

Online Problems without any knowledge of future events can be

solved using Competitive analysis of Online Algorithms (Borodin & El-Yaniv

1998). An online algorithm is presented with a request sequence online

without any knowledge of future requests while the goal is to serve entire

service request while the cost is small. An adversary generates requests and

an online algorithm has to service the request.

An online algorithm is c-competitive if there is a constant ,

such that for all finite sequences :

( ) = . ( ) + (4.1)

where ( ) is the cost incurred by for the input I; ( ) is the cost of an

optimal offline algorithm for the input sequence ; and is a constant. This

means that for all possible inputs, incurs a cost within the constant factor

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of the optimal offline cost plus a constant . can be a function of the

problem parameters, but it must be independent of the input . If is c-

competitive, it is said that attains a competitive ratio .

There is single physical server, or host, and VMs designated to

that host. Time is discrete in this issue and could be sliced into n time periods,

where every time span is 1 second. The resource supplier pays the expense of

energy devoured by the physical server. Energy for every unit time is the

expense of energy that is paid by the Cloud supplier. CPU execution is a

parameter used to express the Capacity of the Host and the VM procured

resource. The CPU use by a VM subjectively updates over the long run which

implies that VM encounters alert workloads. Assuming that the aggregate

CPU request surpasses the limit of the CPU, the host is oversubscribed. It

implies that the VMs demand their most extreme permitted CPU execution. A

resource supplier and client secure an SLA violation when the requested VM

limit surpasses the CPU limit. An SLA violation causes penalty to the

supplier, which is figured as a result of the expense of SLA violation for

every unit of time and time span of the SLA violation predominant between

them. At any point in time, an SLA violation happens and proceeds until

stopping time. However, because of the over-membership and variability of

the workload encountered by VMs, due to the over-subscription and

variability of the workload experienced by VMs, at the time v the overall

demand for the CPU performance exceeds the available CPU capacity and

does not decrease until stopping time. It is expected that as per the problem

definition, a solitary VM could be relocated out of the host. This relocation

expedites a lessening of the interest for the CPU execution and makes it lower

than the CPU limit. We define stopping time, which is equal to the latest of

either the end of the VM migration or the beginning of the SLA violation

(Beloglazov & Buyya 2012). A migration takes some time and during which

an extra host is used to accommodate the VM being migrated, and therefore,

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the total energy consumed during a VM migration is twice the cost of energy.

The issue is to confirm the time m when a VM relocation ought to be started

to minimize the sum cost comprising of the energy cost and the expense

initiated by a SLA violation in the event that it happens.

4.4 THE COST FUNCTION

Cost caused by the SLA violation and the cost of the extra energy

consumption is the total cost. Energy consumed by the destination host is the

extra energy consumption, where a VM is migrated to, and the energy

consumed by the source host after the beginning of the SLA violation

(Borodin & El-Yaniv 1998). All the energy consumption is taken into

account, the energy consumed by the source host from the time when the

process starts to the time SLA violation starts. The reason is that this part of

energy cannot be eliminated by any algorithm by the problem definition.

Another restriction is that the SLA violation cannot occur until a migration

can be completed. VM migration can start before or after SLA violation and it

also depends on the stopping time. The algorithms derived are from the

Competitive online algorithms of (Beloglazov & Buyya 2012) and we have

implemented the MEP algorithm.

To dissect the issue, we characterize a cost function as follows. The

total cost incorporates the expense brought on by the SLA violation and the

expense of the added energy utilization. The added energy utilization is the

energy depleted by the additional host where a VM is moved to, and the

energy devoured by the main host after the start of the SLA violation. Hence,

all the energy utilization is considered with the exception of the energy

depleted by the main host from the starting time to SLA violation time. The

reason is that this part of energy cannot be wiped out by any algorithm by the

problem definition. An alternate confinement is that the SLA violation cannot

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happen until a migration might be completed. As per the problem

proclamation, we outline the cost function.

The Cost Function C outlines three cases, C1 depicts the situation

when the movement happens after the event of the SLA violation, yet the

relocation begins not later than T soon after the start of the SLA violation.

Thus the expense is the expense of energy devoured by the additional host

from the start of the VM migration to the start of the potential SLA violation.

There is no expense of SLA violation, as consistent with the issue explanation

the stopping time is precisely the start of the potential SLA violation, so the

duration of the SLA violation is nil. C2 portrays the situation when the

relocation happens after the event of the SLA violation, yet the migration

begins later than T soon after the start of the SLA violation. C2 holds three

terms: (a) The expense of energy expended by the additional host from the

start of the relocation to the start of the SLA violation; (b) The expense of

energy depleted by both the primary host and the added host from the start of

the SLA violation to stopping time (c) The expense of the SLA violation from

the start of the SLA violation to the closure of the VM movement. C3

portrays the situation when the movement begins after the start of the SLA

violation. Thus the expense comprises of three terms: (a) The expense of

energy expended by the primary host from the start of the SLA violation to

stopping time; (b) The expense of energy devoured by the additional host

from the start of the VM migration to stopping time; (c) The expense of SLA

violation from the start of the SLA violation to stopping time.

With respect to the single VM movement issue (Beloglazov &

Buyya 2012), an SLA violation happens when the aggregate demand for the

CPU execution surpasses the accessible CPU limit. The most extreme number

of VMs apportioned to a host when they request their most extreme CPU limit

is VMs experience variable workloads, the most extreme CPU limit that

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could be dispensed to a VM is Th . The total number of VMs is . VMs

might be moved between hosts utilizing live migration with a migration time

. We in this segment investigate a more unpredictable issue of dynamic

VM consolidation acknowledging various hosts and numerous VMs. There

are h homogeneous hosts, and the limit of every host is . The expense of

power is , and the expense of SLA violation for every unit of time is .

Without loss of generality, we can describe = 1 and C = s, where s R+.

This is proportional to demarcating = 1/s and = 1.

We expect that when a host is idle, i.e. there is no executable VMs,

it is exchanged off and depletes no force, or exchanged to the sleep mode with

negligible power consumption. We define non-idle hosts active. The total cost

C is demarcated as shown in Equation (4.2)

= ( + ) (4.2)

where t is the initial time; T is the total time; a {0,1} indicating whether if the

host i is active at time t; v {0,1} indicating if the host j is encountering a SLA

violation at time t. The issue is to confirm what time, which VMs and where

ought to be relocated to minimize the total cost C as shown in Equation 4.2.

4.4.1 The Optimal Online Deterministic Algorithm

Theorem 1

The upper bound of the competitive ratio of the optimal online

deterministic algorithm for the dynamic VM consolidation problem is

> 1 + /2( + 1).

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Proof

In a single VM relocation issue, the optimal online deterministic

algorithm for the dynamic VM consolidation problem relocates a VM from a

host when an SLA violation happens at this host. The algorithm dependably

merges VMs to the base number of hosts, guaranteeing that the allotment does

not make an SLA violation. The omnipresent malicious adversary creates the

CPU request by VMs in a manner that cause host loading however as much as

could reasonably not to exceed and create an SLA violation, while keeping

however as many hosts active, i.e. depleting energy. Nevertheless, an SLA

violation happens at a host regardless of m + 1 VMs are designated to this

host, and these VMs request their most extreme CPU limit A . Subsequently,

the maximum number of hosts that experience an SLA violation

simultaneously is n (Beloglazov & Buyya 2012).

In an instance of a synchronous SLA violation at n hosts, the

amount of hosts not encountering an SLA violation is n = n n . The

method of the adversary is to make an online calculation keeping all the hosts

active constantly and making n hosts experience an SLA violation 50% of

the time. To show how this is actualized, we part the time into times of

length2t . Then T t = 2t , where .. The adversary acts as two

parts for each period. Throughout the first t , the adversary sets the CPU

request by the VMs in a manner to distribute precisely m + 1 VMs ton hosts

by relocating VMs from n hosts. As the VM migration time is t , the total

cost in this time is t nC , as all the hosts are dynamic throughout migration,

and there is no SLA violation. Throughout the following t , the adversary

sets the CPU request by the VMs to the most extreme making an SLA

violation at n hosts. The online calculation responds to the SLA violation,

and moves the vital number of VMs over to n hosts. Throughout this time,

the sum expense is t (nC + n C ), as all the hosts are again active, and

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n hosts are encountering an SLA violation. Hence, the total cost throughout a

period 2t is defined in Equation (4.3)

= 2 + ( + ) (4.3)

Since VM consolidation is done to minimize the number of active

physical hosts, the individual intermigration time interval has to be

maximized since in a time of frames the mean number of hosts that are alive

is inversely proportional to the efficiency of VM consolidation. Consolidation

of VM through SLA and VM migration is done by the Energy curve. The

efficiency of VM consolidation is conceptualized by maximizing the time

intervals between Virtual Machine Migration from overloaded host servers.

Although VMs experience variable workloads, the maximum CPU capacity

that can be allocated to a VM should be less than the overall maximum CPU

capacity.

4.5 ENERGY CURVE MODEL

Hence, we limit the problem formulation to a single VM migration

as in Figure 4.2, i.e., the time span of a problem instance is from the end of a

previous VM migration where is the time when a VM migration starts;

is the CPU utilization threshold defining the host oversubscription;

( , ) is the time during which the host has been over-loaded,

which is a function of and ; is the total time, during which

the host has been active, on and to the end of the next VM migration. At some

point in time , an SLA violation occurs and continues until time

SLAV interval. An SLA violation in our terms is based on the QoS metrics

where both throughput and response time delivery is taken into account by the

organizing Cloud. The hosts which are alive and consume 100% utilization

and the performance degradation due to migration the product of SLATAH

and PDM gives our SLAV as shown in Equation (4.4) (Deboosereet al 2012).

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= % = ( )

( )= (4.4)

SLAV toll starts Tslav

SLAV toll ends

VM migraton endsVM migration starts TVM

Least VMM point Tstop

Actual Energy curve(assumed to be convex)

Least SLAV point

Linear approximation of the VMM and SLAV

VM migration

Figure 4.2 The Objective time function in terms of SLAV and VM migration

If is the number of hosts, % is the total time during which the

host has experiences the utilization of 100% leading to an SLA violation.

is the total of the host being in the VM feeder state, is the

number of VMs; ( ) the estimate of the performance degradation of the

VM caused by migrations; ( ) is the total CPU capacity requested

by the VM during its lifetime (Beloglazov & Buyya 2012). In other words,

due to the over-subscription and variability of the workload experienced by

VMs, at the time the overall demand for the CPU performance exceeds

the available CPU capacity and does not decrease until . It is assumed

that according to the problem definition, a single VM can be migrated out of

the host. This migration leads to a decrease of the demand for the CPU

performance and makes it lower than the CPU capacity (Dobber et al 2009).

We define to be the stopping time, which is equal to the latest of either

the end of the VM migration or the beginning of the SLA violation. A VM

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migration takes time . The problem is to determine the target time, when

a VM migration should be initiated to minimize the total cost consisting of the

energy cost and the cost caused by an SLA violation if it takes place. During a

migration an extra host is used to accommodate the VMs being migrated, and

therefore, the total energy consumed during a VM migration is twice the cost

incurred for one host. Let be the remaining time since the beginning of the

SLA violation, i.e. = .

The product of the objective time function and the power depleted

due to the time is the proposed Energy Curve Model. As time increases for

resource scarcity, the energy cost increases and this in turn increases the

power consumed by the battery where the energy cost includes the cost for the

normal operation and deadline time (Maleki et al 2012). The stopping time,is

the least of either the end of VM migration or start of SLA violation. A VM

migration takes a particular time and the extra host that is alive for the VM

migration to complete. This in turn involves two hosts to stay alive for a

single VM migration which involves twice the expense of power 2CpT. Hence

we tried to minimize the time for how long a VM migration should take place.

The sorted ascending ordered linear approximation of the time slope helped

us to minimize the time a VM migration would take place thereby reducing

the performance degradation due to migration at a particular SLA time per

active host.

4.6 MINIMUM PROCESSING POWER POLICY (MPP)

The energy curve helps to estimate the efficient use of the available

resource, SLAV and VM migration time is calculated. The energy consumed

by the processor in a data center is measured by our proposed MPP parameter

energy per instruction which is given by the ratio between the power and the

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performance. We define the MPP parameter as Energy per instruction rate

performance for a host having VMs as shown Equation (4.5)

= (4.5)

( + ) = (4.6)

( ) = (4.7)

( ) = ( ) (4.8)

( ) = ( ) (4.9)

= ( ) = (4.10)

Energy curve is the extent of time for the SLA violation and VM

migration which being the trade-off parameters, this extent of time is

multiplied with the expense of power for which the CPU is active or reliable.

The performance of all capable hosts are studied. Our model addresses the

major trade-off that Cloud suffers, that is trade-off between power

consumption and performance. Performance and energy consumption depends

on the availability of efficient resources and scarcity of efficient resources

burdens time of SLAV and VM migration. The Minimum Processing Power

(MPP) policy migrates a VM from = { , … , } that requires the

minimum processing power to complete a migration relatively to the other

VMs allocated to the capable host based selected. MEP algorithm

concatenates the Energy Curve model explained below. For every new VM

i.e., sum of incoming and the VMs in migratable VM list are taken into

account. Processing VM are considered which are the active VMs in a host.

The energy curve is usually non-linear. Total energy cost can be leveraged by

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harnessing the least costs of SLAV and VM migration. We introduce six steps

for achieving this-

Select new host identification for every migratable VM to reside

The host list is the Capable Hostlist.

Energy Slope of the time curve which can be achieved by

Equation (4.11)

=

(4.11)

Linear approximation of the Energy slope is performed.

The Sorted VM with minimum energy slope will be assigned to

EIRP rated host.

Minimum performance hosts are identified using Energy slope.

The MEP is iterated in parallel for all the VM in the hosts and

critical parallel parts are identified and hence allocation of

critical VM to critical host is realized in parallel in our

simulation.

Since host has been handling incoming VM’s host oversubscription

is possible and hence the above policy is used to iterate the following

algorithm to avoid the host oversubscription. The repute of a host can vary

with time due to various factors like the fluctuating load, malicious behaviour,

power shutdown and various other factors. The allocation of VMs =

{ , … , } of , to = { , … , } of , , hosts. The

algorithms defined below randomly assigns hosts to various group of VM in

host considered for N heterogeneous physical nodes, maximum available

hosts which is the least individual number of under-loaded host in

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the host list and minimum available hosts which is the least

individual number of over-loaded hosts in the hosts list.

Lowest Energy

VM migration

SLA violation

VM

Migration

Figure 4.3 The Energy Curve illustration

From the above mentioned MPP policy we analyzed the VM

selection and Host oversubscription algorithms and implemented the Energy

Performance aware algorithms. The pseudo code and flowchart for the

proposed MEP algorithms is shown in Figure 4.4.

Algorithm (Pseudo code): The Minimum Energy Performance Algorithm

(MEP)

Input: CapableHostlist, vmlist Output: MEP host

foreach host in capableHostlist() do

ec

update capableHostlist()

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Sort

ec) = MEPhostupdate add.NewVMlist

return MEPhost

Figure 4.4 Pseudo code and Flowchart for MEP algorithm

CapableHostlist

For every h in CapableHostlist

Add and Update NewVMlist (EIRP rated Host)

Sort EnergySlope ec

Deduce Energy Curve

Do LinearApproximation

Find EnergySlope ec

Find Min (EnergySlope ec )

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4.7 RESULTS AND DISCUSSION

The above discussed models and systems have been contemplated

and the outcomes have been examined in detail. The consolidation of the

VMs by the Energy Curve and the VM selection procedures has lessened

Energy utilization by 20 to 30% and this has despite the fact increased the

AvgSLAV. The AvgSLAV increased because of consolidation of the VM

selection time and thus increasing the proficiency of the VM processed by the

host in a data center as indicated in Figure 4.5. The analysis of Energy and

Average SLA Violation is as shown in Figure 4.5. The Energy efficiency is

comparatively improved in most of the days of the PlanetLab workload

compared to the Provisioned systems. As the Average SLA violation

consolidated system by our proposed algorithm proves better during all the

days to about 10 to 14%. A better Energy efficiency when an efficient

Average SLA Violation was achieved. For simulation level examinations, it is

indispensable to direct tests utilizing certifiable trace from a genuine real

world system accessible. We have conducted experiments from genuine trace

taken from CoMon venture (Park & Pai, 2006), an overseeing base of

PlanetLab. Throughout simulations every VM is arbitrarily relegated to a

workload trace from one of the VMs from corresponding day since the

workload is on a days' CPU usage by more than 1000 VMs from 500 places

around the world. The workload trace is in an interim of 300 seconds and for

our simulations we have taken trace gathered throughout March and April 2011.

We have simulated the cloud model for the best conceivable QoS

bearing the trade-off between the performance and power consumption. The

Effect of the VM estimate regarding the VM migration and VM selection time

has been investigated for a day of real time PlanetLab trace is as shown in

Figure 4.6 where the data center is of 800 heterogeneous hosts 50% of which

121

are HP Proliant ML 110 G4 servers and other half are HP Proliant ML 110

G5 servers. The server use and power expended by these servers are taken

from true data from SPECPower benchmark instead of a diagnostic model of

a server which makes the simulation more effective (Corporation 2012).

Figure 4.5 For the PlanetLab workload – analysis for Energy and AvgSLAV

Figure 4.6 For the PlanetLab workload – Analysisof Energy Consumption and VM selection time

0

5

10

15

20

0

50

100

150

200

03-03-2011, 1052 VM

06-03-2011, 898 VM

22-03-2011, 1516 VM

25-03-2011, 1078 VM

03-04-2011, 1463 VM

09-04-2011, 1358 VM

11-04-2011, 1233 VM

12-04-2011, 1054 VM

20-04-2011, 1033 VM

Avg

SLAV

(%)

Ener

gy C

onsu

mpt

ion

(kW

h)

Energy Consumption LR MMT (kWh) Energy Consumption LR MPP (kWh) Average SLA violation LR MMT (%) Average SLA violation LR MPP (%)

0

0.002

0.004

0.006

0.008

0.01

0.012

0 20 40 60 80

100 120 140 160 180 200

03-03-2011, 1052 VM

06-03-2011, 898 VM

22-03-2011, 1516 VM

25-03-2011, 1078 VM

03-04-2011, 1463 VM

09-04-2011, 1358 VM

11-04-2011, 1233 VM

12-04-2011, 1054 VM

20-04-2011, 1033 VM

VM S

elec

tion

Tim

e (S

econ

ds)

Ener

gy C

onsu

mpt

ion

(kW

h)

Energy Consumption LR MMT (kWh) Energy Consumption LR MPP (kWh) VM mean selection time LR MMT (sec) VM mean selection time LR MPP (sec)

122

VM selection time in our simulation has increased and it

reciprocates the consolidation of VM due to the proposed algorithm

implemented. The Energy consumption due to the workload has been

efficiently reduced and an efficiency of 20% to 30% has been achieved.

Energy consumption due to data center workload is analyzed and the VM

selection time and the time before migration behaves differently due to the

consolidation due to the proposed algorithm. Figure 4.5 shows that

Consolidated VM selection time is almost consistent and it outperforms the

total provisioned algorithms. VM mean selection time is less than 0.002

seconds and this helps in better performance and the reduction in Energy

Consumption as on 6th March, 2011. Energy consumption is less amounting to

80 kWh which is 30 to 40% by our proposed system shows better efficient

results. The VM selection algorithms MMT and proposed MPP is coupled

with the LR to select host overloading and the results have been discussed

below. The VM migration time depends on the time the VM gets migrated

from a host through a broker which consolidates based on our proposed

algorithm. The proposed consolidated results are shown in Figure 4.5.

The temporal validity of the cloudlets with respect to VM

considers the RAM requested at the maximum from a host. This when

compared to the complexity of the algorithm seems a trivial task. The data

center simulated handles the algorithm for different PlanetLab workloads for

ten days between March and April 2011. The simulation results for the

number of VM migrations and average SLA violation has been tabulated as

shown in Table 4.1, trend shows that the average SLA violation for our

proposed VM consolidation method LR MPP is not as efficient as compared

to LR MMT.

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Table 4.1 For the PlanetLab workload – Analysis for Number of VM migrations and AvgSLAV

PlanetLab Trace for 10 days

Number of VM migrations Average SLA violation

LR MMT LR MPP LR MMT (%)

LR MPP (%)

03-03-2011, 1052 VM 21052 1162 10.09 14.9206-03-2011, 898 VM 21025 2106 10.15 12.04

22-03-2011, 1516 VM 21922 1830 10.61 15.0925-03-2011, 1078 VM 26301 2494 10.14 11.5803-04-2011, 1463 VM 25370 1487 10.34 14.9609-04-2011, 1358 VM 20907 1498 10.71 15.5911-04-2011, 1233 VM 30654 2770 10.41 10.4912-04-2011, 1054 VM 26282 2484 10.24 11.9420-04-2011, 1033 VM 18299 1203 12.53 17.73

Due to the consolidation of the VM selection techniques and the

energy curve the number of VM migrations has to be reduced or in other

words has to be efficiently consolidated. The average SLAV is almost higher

but this has given efficient reduction in the Number of VM migration as

shown in Table 4.1. As we compare the VM selection time from the

consolidation algorithms and multi informative VM analysis has proved to

decrease the energy consumption and efficient VM consolidation has been

obtained. As in Figure 4.7, the LR MPP consolidated number of VM

migration has been reduced efficiently and SLA performance due to migration

also has been noted for efficiency. This contributed to the decrease inEnergy

consumption for a 24 hour simulation for each day with varied VM loads

compared to LR MMT. The CPU utilization has been considered as the

PlanetLab Workload for certain days in the month of March and

April 2011.

124

Figure 4.7 For the PlanetLab workload – Analysis for Number of VM migrations and SLAPDM

The SLA performance degradation due to migration can be seen in

the Figure 4.8. It can be depicted that Consolidated LR MPP method

outperforms the legacy LR MMT method and63% efficiency on an average

has been achieved. The mean value of the sample means of the time before a

host is switched to the sleep mode for the LR-MMT algorithm combination is

864 seconds with the 95% CI: (820, 908). Performance Degradation is higher

since there is more utilization of resources under constraints unlike the hosts

being overused and more servers left underused or not used at all.

From Figure 4.8 the energy consumption of LR MPP is efficient

but SLA PDM has increased for LR MMT the efficiency has been about 47%.

There is an increase in SLA PDM and it is due to proper consolidation of the

physical source and the time for the VM Migration to take place and attains a

lower SLAV. This time has induced a PDM due to the workload executed by

the host.

0

0.02

0.04

0.06

0.08

0.1

0

5000

10000

15000

20000

25000

30000

35000

03-03-2011, 1052 VM

06-03-2011, 898 VM

22-03-2011, 1516 VM

25-03-2011, 1078 VM

03-04-2011, 1463 VM

09-04-2011, 1358 VM

11-04-2011, 1233 VM

12-04-2011, 1054 VM

20-04-2011, 1033 VM

PDM

(%)

Num

ber o

f VM

mig

ratio

ns

Number of VM migrations LR MMT Number of VM migrations LR MPP SLA perf degradation due to migration LR MMT (%) SLA perf degradation due to migration LR MPP (%)

125

Figure 4.8 For the PlanetLab workload – Analysis for Energy consumption and SLAPDM

Table 4.2 For the PlanetLab workload – Analysis for Energy Consumption and SLATAH

PlanetLab Trace for 10 days

Energy ConsumptionSLA time per active

hostLR MMT

(kWh)LR MPP (kWh)

LRMMT(%)

LRMPP(%)

03-03-2011, 1052 VM 176.16 106.13 5.21 18.83

06-03-2011, 898 VM 133.84 80.56 5.3 29.8

22-03-2011, 1516 VM 118.72 105.08 4.5 25.23

25-03-2011, 1078 VM 164.47 96.18 5.45 30.27

03-04-2011, 1463 VM 160.02 141.25 4.44 29.48

09-04-2011, 1358 VM 124.61 113.43 4.43 20.05

11-04-2011, 1233 VM 189.71 113.98 5.42 28.13

12-04-2011, 1054 VM 164.26 97.38 5.41 32.11

20-04-2011, 1033 VM 185.58 76.24 5.4 23.15

0

0.02

0.04

0.06

0.08

0.1

0

50

100

150

200

03-03-2011, 1052 VM

06-03-2011, 898 VM

22-03-2011, 1516 VM

25-03-2011, 1078 VM

03-04-2011, 1463 VM

09-04-2011, 1358 VM

11-04-2011, 1233 VM

12-04-2011, 1054 VM

20-04-2011, 1033 VM

PDM

(%)

Ener

gy C

onsu

mpt

ion

(kW

h)

Energy Consumption LR MMT (kWh) Energy Consumption LR MPP (kWh) SLA perf degradation due to migration LR MMT (%) SLA perf degradation due to migration LR MPP (%)

126

It is observed that the SLATAH has been substantially increased

and this has led in a decrease in the energy consumption as in Table 4.2 of the

physical source (or) hosts involved in the experiment. But in Figure 4.9 the

host shut down of our LR MPP method has been substantially made

consistent through various simulations of the workload traces involved. The

number of host shut down has been fluctuating throughout our experiment for

the legacy LR MMT method in a data center. Whereas applying various

strategy and involving our own system with the proposed algorithm, gave

better results as shown in Figure 4.9. This has led to the reduction of the

energy consumed.

Figure 4.9 For the PlanetLab workload – Analysis for Energy Consumption and Number of Host shutdown

The simulation environment of the data center created, harnesses

the Energy consumption using the proposed algorithm. Number of Host

shutdowns has been analyzed as shown in the Figure 4.9 and the proposed LR

MPP method shows better results. The proposed methods have helped in

handling the number of host shutdown and a consistent lower level has been

0

1000

2000

3000

4000

5000

6000

7000

0

20

40

60

80

100

120

140

160

180

200

03-03-2011, 1052 VM

06-03-2011, 898 VM

22-03-2011, 1516 VM

25-03-2011, 1078 VM

03-04-2011, 1463 VM

09-04-2011, 1358 VM

11-04-2011, 1233 VM

12-04-2011, 1054 VM

20-04-2011, 1033 VM

Num

ber o

f Hos

t shu

tdow

ns

Ener

gy C

onsu

mpt

ion

(kW

h)

Energy Consumption LR MMT (kWh) Energy Consumption LR MPP (kWh) Number of host shutdowns LR MMT Number of host shutdowns LR MPP

127

maintained and a better scaling of energy consumption is obtained. A

consistent level of the Host Shutdowns is possible and this has led to a

reduced Energy consumption as well.

As shown in Figure 4.10 Number of VM migration involved in the

legacy system has been very much high for the trace simulated. We by

applying our LR MPP module, the system behaved very well consolidating

the VMs and scheduling in an efficient manner. Thereby a drastic efficient

confinement of number of VM migrations has been achieved which decreased

the energy consumed.

Figure 4.10 For the PlanetLab workload – Analysis for Number of VM migrations and Energy Consumption

The mean value of the sample means of the time before a host is

switched to the sleep mode for the LR MPP algorithm combination is 953

seconds with the 95% CI: (902, 1053). The SLATAH and PDM vary as

Energy of a simulation having 1052 VMs and 800 heterogeneous nodes the

lesser the Energy consumption, higher is the SLAV and this is reflected in

SLATAH. Consolidation of the hosts in turn increases the Host Shutdown

0

5000

10000

15000

20000

25000

30000

35000

0 20 40 60 80

100 120 140 160 180 200

03-03-2011, 1052 VM

06-03-2011, 898 VM

22-03-2011, 1516 VM

25-03-2011, 1078 VM

03-04-2011, 1463 VM

09-04-2011, 1358 VM

11-04-2011, 1233 VM

12-04-2011, 1054 VM

20-04-2011, 1033 VM

Num

ber o

f VM

mig

ratio

ns

Ener

gy C

onsu

mpt

tion

(kW

h)

Energy Consumption LR MMT (kWh) Energy Consumption LR MPP (kWh) Number of VM migrations LR MMT Number of VM migrations LR MPP

128

increasing the Energy requirement of the available hosts and Migration Time

lessens which increases SLAV.

4.8 SUMMARY

To help increase the profitability the Cloud resource provider

targets striking an efficient model between Energy and SLA violation. With

least resources we focus on giving quality management to its clients and this

might be made conceivable just with effective resource assignment algorithm.

We have executed new resource designation algorithms by working in host

oversubscription recognition and VM determination. Our investigations have

demonstrated that huge Energy changes could be accomplished when

contrasted with the existing power wary resource assignment algorithms. This

Energy harnessing has achieved an efficiency of 34% when compared to LR

MMT method by trading off with SLAV by looking after the performance

with in safe limits. The SLA and QoS measurements prompt the trade-off

between issue of energy and performance effective dynamic consolidation of

VMs. The effects have ended up being superior to the accessible conventional

strategies in the Energy point of view in the present Cloud foundation and

keeping up the server usage to an improved level and maintaining a strategic

distance from variance of servers or have in this manner diminishing the

energy consumption of the over-provisioned servers. Performance and energy

consumption depends on the availability of efficient resources and scarcity of

efficient resources burdens time of SLAV and VM migration. Further works

could be pointed at transforming better Energy SLAV trade-off with the goal

that the cloud situation could be made more proficient and vigorous. A

hindrance of the proposed calculations is the inability to unequivocally

indicate a reliability obligation: the execution of the algorithms concerning

reliability can just be balanced by tuning the parameters of the calculations on

alive hosts. Chapter 5 explores a host over-burden location dependent upon

129

CQR, which permits the articulate detail of a server overloading by using

machine learning techniques. We have conveyed the Cloud energy proficient

methods by dynamic union of VM and forecast of Host oversubscription

through the machine learning techniques on the workload trace. The real time

workload trace taken from CoMon infrastructure has been tested for our

simulations. The server usage and power devoured by the servers taken from

genuine data from SPECPower benchmark has served to make our simulation

trustworthy.