Post on 28-May-2020
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CHAPTER 3
CLOUD GRAPH THEORETICAL WORKFLOW
MODEL AND PROPOSED ALGORITHMS
3.1 INTRODUCTION
In Cloud Computing, the Infrastructure as a Service (IaaS) layer,
the servers are distributed in nature and a set of analytical strategies are
required to study the layer in detail. This work focuses on IaaS Cloud
environments, e.g., Amazon EC2, where multiple independent users
dynamically provision VMs and deploy various types of applications. Cloud
provider should be able to handle variable workloads since it is not aware of
the nature of the workload. Since multiple workloads are possible, the types
of applications that can coexist are mixed in nature, a combination of High
Performance Computing (HPC) and Cloud load types. Response time and
throughput are valid for a specific set of situation where it defines the Energy
scheduling strategy. In this chapter we present the analytical strategies, a
graphical workflow analysis of the cost and performance characteristics of
algorithms. We discuss the basic structure of the Cloud Data Center by
analysing the arrival and service rates of the VM to an host. Workflow
analysis with the focus on the response time calculation is necessary for
proposing an algorithm, since the response time directly relates to the
efficiency of an algorithm. So it is necessary to analyze the VM
characteristics based on the response time characteristics. The Response time
characteristics are studied by a Multi-informative analysis of the problem of
energy and performance efficient dynamic VM consolidation. The response
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time of the VM is taken into consideration and various algorithms have been
tested, proved and presented for its merits and demerits. To minimize energy
consumption one way is to switch servers dynamically to the sleep mode
since energy consumption is the major component of operating costs.
Switching servers randomly is not a viable option. Identifying the hosts and
dynamically consolidating the executing VMs is the solution for the Energy
Performance trade-off issue.
3.2 MOTIVATION
To address the above mentioned issues, we propose a Cloud
Graphical Workflow Model (CGWM). From the CGWM we intend to
analyze the VM by response time characteristics and find Cloud Workload.
From the Workload information we have proposed RAM based Host
Oversubscription (RHO) and RAM based VM selection (RVS) techniques.
The methodology proposed in this postulation oversubscribes the server CPUs
by exploiting data on the continuous CPU usage; then again, it doesn't over-
commit RAM. In this work, the significant measure of RAM that could be
depleted by a VM is utilized as an imperative when putting VMs on servers.
One of the purposes behind is that RAM is a more discriminating asset
contrasted and the CPU, as a provision might fizzle because of inadequate
RAM. The inadequacy CPU might only ease off the execution of the
requesting VM failure. In a CPU, RAM generally does not turn into a
bottleneck resource, and consequently, does not restrict the amount of VMs
that could be instantiated on a server, as demonstrated in the research
literature. Hence in the proposed algorithms we consider RAM behaviour of
VM as an important factor. An alternate perspective recognizing the work
introduced in this proposal is modeling the design of the VM administration
framework. A viable VM administration framework is vital for extensive
scale Cloud suppliers, as it empowers the characteristic scaling of the
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framework when new process hosts are included. An outline of the
significance of versatility is the way that Rackspace, a well-known IaaS
supplier, has expanded the aggregate server number in the second quarter of
2012 to 84,978 up from 82,438 servers at the close of the first quarter. An
alternate profit by making the VM administration framework conveyed is the
enhanced blame tolerance by taking out single purposes of washout:
regardless of the fact that a process or controller host (or) provider fizzles, it
might not render the entire Cloud framework inoperable. Advanced resource
monitoring, analysis, and configuration tools can help address these issues,
since they bring the ability to dynamically provide and respond to information
about the platform and application state and would enable more appropriate,
efficient, and flexible use of the resources. Additionally such tools could be of
benefit to Cloud providers, users, and applications by providing more efficient
resource utilization in general.
Figure 3.1 Time - Space relation in a processing element
Our Cloud comprises of M heterogeneous processors. Jobs arrive at
in an irregular manner, the inter arrival time is exponentially appropriated
with an average of 1/ . The jobs are accepted to require service time time
exponentially dispersed by a mean 1/ . Job size includes program and data
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size under normal distributed nature with a given mean and variance. The jobs
here are the Cloudlets in VM which dwells in hosts according to the
requisition. The Cloudlets (or) jobs consolidate VM residing in a host to
analyze the host oversubscription and underused host in a cloud datacenter.
The workload data of the VM is essential to solidify the host over-burdening
and underused servers in the data center. The Cloud assets are geographically
dispersed and our scheduler enactments as a medium between the Cloud
clients and the suppliers. In view of the accessibility of assets the scheduler
happens with the submissions of jobs and to maintain equilibrium to fulfil the
SLA (Subrataet al 2008). Data center overhauls the data of its assets and they
are hard real runtime of servers where execution time is inversely
proportional to CPU working instances (Garget al 2009).
Figure 3.2 Complexity of finding resource and data-host match for a single job in a Cloud Workflow
Scheduling of the jobs in the workflow essentially concentrates on
a sum of the machine capacities and consolidation of them: minimizing the
sum is vital, minimizing the generally speaking cost (monetary cost) of
execution and data transfer, executing within the deadline and allocated
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budget. The mapping of the workflow jobs to minimize one of the objective
functions is a complex sub-problem of the general job scheduling problem.
The issue comes to be intricate with the expansion of imitating data sets with
jobs needing more than one file. Figure 3.2 presents the many-sided quality of
finding resources and data sets matched for a solitary job in a Cloud
environment. In Fig. 3.2, "a job" requires data cloudlets. These
information cloudlets are reproduced on data hosts. This gives us
conceivable sets of data set. Assuming that we have compute hosts, the
total conceivable asset might be . A lookup table arranges the total
number of syntheses conceivable for shifting number of cloudlets with settled
number of data hosts and compute hosts (Liu & Anderson 2011). The point
when the cloudlets are reproduced and a solitary job requires more than one
resource, the amount of correlations required to go to the best result (a
consolidation that gives a set of data hosts and compute hosts) increases. The
point when subordinate jobs are available, the issue of discovering the blend
of data hosts and compute hosts set turns into a non-trivial issue. This basic
issue comes to be remarkably perplexing when there are demands included,
such as heterogeneous resources, network costs, storage constraints, etc.
Before defining and analysing the algorithms, this chapter first
presents a workflow model and a resource model.
3.3 CLOUD GRAPHICAL WORKFLOW MODEL
We now describe the problem of data host selection and jobs to
resource mapping in the presence of large number of replicated cloudlets for
workflow applications.
Definition: CJSP (D,R,T,F,G,L,M) Given a set of resources R, a set
of jobs T, a set of cloudlets F (both input and output cloudlets of T), a graph
G that represents the data flow dependencies between jobs T, the Cloud-Job
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Scheduling Problem (CJSP) is a problem of finding assignments of jobs to
compute-hosts [job schedule ={ } T, r R], and the partial data set
( = { } ) to be transferred from selected data-hosts to assign
compute hosts = {{ }( , r R, |{ }| |R|}
for each job, simultaneously, such that: total execution time (and cost) at r and
data transfer time (and cost) incurred by data transfers { }for all the
jobs are minimized.
This chapter assumes the following pre-conditions that are
associated with definition:
1. Data cloudlets are replicated across multiple data hosts in a
data center
2. Each job requires more than one data file of the Cloud
application
3. Total time and cost are bounded by L and M, respectively,
where L signifies the end time and M denotes the maximum
amount that can be spent on executing all the jobs in T of a
cloud-flow graph G.
A workflow is represented by a directed acyclic graph = ( , ),
where = { , , … } and E represent the vertices and edges of the graph,
respectively (Kwok & Ahmad, 1999). Each vertex represents a job t and there
are n jobs in the workflow. The edges maintain execution precedence
constraints. We define edges in Cloud Datacenters as Data dependency and
largely forms a subset of Latency and Bandwidth of the underlying network.
Having a directed edge from to , x, y N means that cannot start to
execute until is completed. The components are described as a set of
job = { , , … , }, set of cloudlets = { , , … , }, set of Hosts and
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Resource (Datacenter) = { , , … }. A job requires a set of Cloudlets
= { , , … , } to be staged in for execution. Each cloudlet required by
a job is hosted by multiple data-hosts. Partial segments of each file
that need to be transferred for a job assigned to a data center r is
denoted by a set = {{ }( ,r R, }|
|R|}.
This chapter does not consider conditions cycles in the workflow
structure. This leads to an assumption that the cycles and conditions in the
workflow can be represented in the form of a Directed Acyclic Graph (DAG)
for the scheduling system to execute them, whenever necessary. To simplify
the problem for understanding, it can be broken down into two stages. To
begin with, a set of data that has the needed cloudlets for the jobs in the
workflow ought to be discovered. The choice of the optimal set of data has to
be in the vicinity of the extensive number of repeated cloudlets for a single
work. This choice of the set with optimal number of cloudlets gives a solution
to solve a set-coverage aspect.
There are just three exceptional cases for which there exist optimal
polynomial-time calculations.
These cases are:
1. Scheduling tree-organized work diagrams with uniform
reckoning expenses on a discretionary number of servers;
2. Scheduling self-assertive work diagrams with uniform
processing expenses on two Hosts; and
3. Scheduling an interim requested job development. In any case,
these results expect correspondence between jobs to take a step
back.
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The mapping of jobs in a workflow to the resources in our case is
fundamentally unique in relation to the general mapping issue. Given the
repeated cloudlets and various data hosts, fitting choice of data hosts and
compute hosts ought to be made for each job in the workflow such that the
subordinate jobs will profit from determining set of its parents. The best case
is the point at which every job gets the optimal information data host and
compute host for execution so the make span and expense are optimal. The
naive case is when the resource set is chosen irrespective of the dependencies
for each job.
Figure 3.3 Graph Components in a Cloud Workflow System
Figure 3.3 shows a simple workflow with compute-hosts and
data-hosts , assigned to each job (the separation of data-hosts and compute-
hosts are for clarity only). We first consider the information exchanges
happening because of the determination of compute hosts and data hosts for
job ‘a’ and job ‘c’. Since the jobs are mapped to two distinctive compute
hosts, the yield cloudlet from job ‘a’ need to be exchanged between the two
processes has an expense of time and cost with it. Job ‘a’ has cost and job
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‘c’ has cost for transfer between two data host based on space shared
policy. Therefore the optimal result might select a mix compute and data host
to minimize the bandwidth cost and execution time (Fakhar et al 2012). The
main strategy is by acknowledging the closeness of data host as far as system
separation with the set of compute hosts. The second strategy is by attempting
to amplify the co-connection between two set of data hosts, for some set of
jobs that require same set of cloudlets. The foremost strategy dependably
seeks the whole set of data hosts and compute hosts to discover one combo
that fulfils the destination capacity. However, it doesn't consider that jobs
could be offering the same set of cloudlets. Also, past emphases may have as
of recently discovered the data host and process host set consolidation that
could be connected an ensuing set of jobs. Case in point jobs ‘a’ and ‘c’ could
be needing same set of cloudlets, so the register concentrated determination of
competitor data host sets and compute host might be stayed away from for the
second occupation. The second way might practically select the same set of
data host by attempting to expand their co-connection. In the recent case,
when the amount of jobs increments, both the compute hosts and data hosts
got over-burdened, as the vast majority of the jobs are mapped to these
restricted resources. This expands the waiting time of all jobs mapped to these
resources. Subsequently a legitimate determination calculation might as well
convey the burden and fulfil the destination capacity. We propose the multi-
informative VM analysis to estimate the Cloud Workload. Next we limit the
consolidation of VMs by our proposed algorithms within the stipulated
response time. This helps in an efficient solution of Efficient Energy strategy
in Cloud Data Center.
3.3.1 Multi-informative VM Analysis
A workflow’s makespan and overall cost depends on the selection
of data sources and mapping of jobs to resources. Selection of ‘best’ data
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sources before irrespective of compute sources, as is done in existing
scheduling algorithms, does not give time and cost efficient schedules when
the size of data is comparatively larger than the computation time of jobs.
Significant bandwidth is required to stage-in and stage-out these data prior to
the execution of the jobs in a workflow. Similarly, if the data is to be re-used,
the scheduling policy must select nearer (in terms of network distance)
compute resources as selecting them affects the time/cost of transferring
output data, and hence the overall execution time/cost. If the compute host
and data-host are closer in terms of network distance, the transfer time is
significantly reduced. The challenges that need to be faced are:
1. How to select data hosts and compute hosts such that the job-
resource mappings give a schedule that optimizes overall
Energy, Time and SLAV?
2. How to estimate cloudlet execution time and data-transfer
times by the workload information?
3. How to adjust job-resource mappings to fit the execution
environment at run-time?
To solve the aforementioned challenges from the Workload
information we analyze VMs and then propose VM consolidation algorithms
for an environmentally sustainable data center. The distribution of the
cloudlet on a VM to be efficient is necessary and VM analysis has to be done
for better scheduling. The temporal validity based on the timing constraints of
SLA should be modelled in order to study it on an energy perspective. Hence
from the arrival and service rates we define the multi-informative VM on a
host and how Energy curve is developed through SLA and VM migration.
The objective is to finish the workload in an estimated double threshold
(avg. response time), thus indicating the accountability of the metric based on
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application performance rather than the infrastructure performance. The
equilibrium of the workload is maintained to achieve SLA. Since response
time and throughput are the major criteria of purpose in SLA. The response
time based analysis helped to find a better way for scheduling to maximize
throughput as shown in Figure 3.4.
Figure 3.4 Server Load (Courtesy: www.eventsratio.org)
We decided to analyze VM scheduled for MIPS in Host based on
the response time. Energy Consumption increases when the Response time
increases. It is necessary to efficiently handle VMs before the Response time
becomes unacceptable. This reduces the SLA violation. VM migration and the
reduction in SLA violation will in turn reduce Energy consumption. Hence in
this section we analyze the response time of VM and propose VM selection
and Host Oversubscription consolidation Algorithms.
The VM arrival rate and service rates for cloud workload to a
cloud provider are such that,
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> (3.1)
where arrival rate is greater than the service rate
In order for stability
< (3.2)
where is the number of Host (or) CPU.
The total arrival rate should be less than the total service rate as in
Equation (3.2)
The set of VMs where cloudlets have been executed is a workload
and VM migration takes place with a Host alive for service transaction.
Average response time is given by Equation (3.3)
= 1/ ( ) (3.3)
Where is the number of VM
= time at which VM arrives
= time at which VM leaves the system
The cloud scheduler admits more VM and maintains a SLA based
on and least and scheduled to hosts with min deadline andwith
maximum free processing availability. We propose the workload aware
placement model for VM to a host so that VM selection can be consolidated.
For every estimated workload interval load estimation( , )
is taken. Each host calculates its period at instant as shown in Equation
(3.4)
( ) = ( ) + (1 ) (3.4)
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here = probability of possible arrival rate
( ) being Estimated arrival rate for cloudlet at time
Actual number of jobs (VM) arrive on host at time
( ) = ( ) + (1 ) (3.5)
here = probability of possible service rate
( ) being Estimated service rate for cloudlet at time
Actual number of jobs (VM) depart from host at time
( ) = ( )/ ( ) (3.6)
Hence from Equation (3.7) we get the Workload on VM,
, ( + ) = ( ) ( )( ) ( )
(3.7)
where i=1,2,3,………..m-1.
( ) Expected numbers of jobs (VM) arrive at a host with a
processing capacity to finish processing of a cloudlet at time
( )Expected numbers of jobs (VM) depart from a host with a
processing capacity to finish processing of a cloudlet at time
The workload information as shown in Equation (3.7) is calculated
to know the status of the scenario to be evaluated and therefore schedule it to
a free processor. The workload information lets us know the Response time of
the VM in an instant of time.
Minimum of (3.8) is the objective for the workload information.
( ) =
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such that
( ( ( ) )( ( ) ) / )! 1 (3.8)
where is the arrival rate estimation factor
( ) =
such that
( ( ( ) )( ( ) ) / )! 1 (3.9)
where service rate estimation factor
( . . ) (3.10)
The type of VM and the cloudlet in VM , involved has to be
analyzed to form coalesce. A workload coalesce in a data center is a set of job
scheduler successful if = isexecutedon as { |
w}, where w is a condition which states that jobs are executed on a FREE
processor or host.
= | w} (3.11)
T = { t + t } (with service and reply time factor)
T = { t }(with arrival time factor)
Then the workload response time is
= (3.12)
Assuming in a data center available is the maximum
availability of processing capacity is given by calculating , ( + )and
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the is not active or no available active hosts due to variable
dimensions on a status of Hosts in a data center. The min TR subject to
(3.13)
where, = =
If free host is not available then the host should be found with no
job running on it. For generosity we define T = 0 and min given by
min = { {( )}, (3.14)
where, = 0if = 0; 1ifF > 0
The abundance of workload information for a cloudlet on a VM
caters to need of a processor where the success rate of the application to be
executed gets higher, provided the processor is active. Equation (3.15) gives
the maximum profit in the processing of VM with a available host, Maximum
profit can be obtained by finding the alive hosts and with maximum free
processing availability. Processing time factor is the sum of reply rate and
service rate factor + . Hence maximum profit is given by
Max{T } (3.15)
where = { {( )}
where, = 0if = 0; 1ifF > 0, the processing time factor.
From the available workload information we have to decide the
purpose of the algorithm to schedule VMs. VM consolidation is done to
minimize the number of active physical hosts, the individual inter-migration
time interval has to be maximized since in a time of frames the mean
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number of hosts that are alive is inversely proportional to the efficiency of
VM consolidation. The system responses have been made more precise and
efficient to manage the fluctuating cloud traffic scenario. We have our data
centers broker and cloudlets created followed by identification of utilization
values of all hosts. If any of these utilization values exceed statistically
determined value of threshold utilization host is declared as overloaded. The
overloaded hosts assign an array namely Hoj. VM consolidation techniques
have been introduced at VM selection level and host oversubscription
detection level. Algorithms RHO has been implemented in host
oversubscription detection part and RVS have been implemented in VM
selection part. These algorithms and the statistical analysis involved in each
are discussed here. Before explaining our algorithm we discuss on the existing
methods (or) legacy procedures to harness a data center for VM consolidation.
3.4 LEGACY METHODS
3.4.1 Median of Absolute Deviation
The idea is to produce estimators that are not exorbitantly
influenced by small departures from model surmises. The Median of Absolute
Deviation (MAD) is a measure of statistical dispersion. It is a more robust
estimator of scale than the sample variance or standard deviation, as it carries
on better with disseminations without a mean or change, for example the
Cauchy distribution. The MAD is a robust statistic, being more flexible to
outliers in a data set than the standard deviation. In standard deviation, the
distances from the mean are squared leading to large deviations being on
normal weighted all the more vigorous. This implies that outliers might
altogether impact the quality of standard deviation. In the MAD, the extent of
the separations of a minor number of outliers is superfluous. For a uni-variate
data set, the MAD is outlined as the average of irrefutably the deviations from
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the average of the information set, that is, the MAD is the average of
unquestionably the qualities of deviations (residuals) from the information's
average. In the proposed oversubscription identification calculation, the CPU
usage limit is demarcated. Parametrical evaluation has been carried out, the
parameters permits the conformity of the security of the system: an easier
quality of effects in a higher tolerance to variety in the CPU usage, while
potentially expanding the level of SLA violations brought about by the
consolidation.
3.4.2 Interquartile Range
This section proposes a method for setting an adaptive CPU
utilization threshold based on another robust statistic. In descriptive statistics,
the Interquartile Range (IQR) (Beloglazov & Buyya 2012), also called the
Mid-Spread or Middle Fifty is a measure of statistical dispersion. It is equal to
the difference between the third and first quartiles. Unlike the (total) range,
the interquartile range is a robust statistic, having a breakdown point of 25%,
and thus, is often preferred to the total range. For a symmetric distribution
(i.e., such that the median equals the average of the first and third quartiles),
half of the IQR equals the MAD.
3.4.3 Local Regression
The next heuristic is based on the Loess method (from the German
loss – short for local regression) proposed by Cleveland (1979). The main
idea of the local regression method is fitting simple models to localized
subsets of data to build up a curve that approximates the original data. The
observations (xi, yi) are assigned neighbourhood weights using the tri-cube
weight function the weight function gives the most weight to the data points
nearest the point of estimation and the least weight to the data points that are
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furthest away. The use of the weights is based on the idea that points near
each other in the explanatory variable space are more likely to be related to
each other in a simple way than points that are further apart. Following this
logic, points that are likely to follow the local model best influence the local
model parameter estimates the most. Points that are less likely to actually
conform to the local model have less influence on the local model parameter
estimates. The traditional weight function used for LOESS is the tri-cube
weight function as shown in Equation (3.16),
( ) = (1 | | ) [| | < 1] (3.16)
However, any other weight function that satisfies the properties
listed in (Cleveland & Loader 1996) could also be used. The weight for a
specific point in any localized subset of data is obtained by evaluating the
weight function at the distance between that point and the point of estimation,
after scaling the distance so that the maximum absolute distance over all of
the points in the subset of data is exactly one.
The distance from to , and let ( )( ) be these distances in
order from most modest to biggest. At that point, the neighbourhood weight
for the perception ( , ) is described by the weight ( ). For such that
( ) < ( ) , where is the amount of perceptions in the subset of
information limited around . The measure of the subset is described by a
parameter of the strategy called the Bandwidth. For instance, if the level of
the polynomial fitted by the strategy is 1, the parametric group of capacities is
y = a + bx, y is the CPU utilization which is a dependent variable and x being
time is the independent variable. The line is fitted to the information using the
weighted least squares strategy with weight ( ) at( , ). The qualities of
a and b are considered by minimizing the capacity. In the proposed
calculation, this approach is connected to fit a pattern polynomial to the final
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observation of the CPU utilization, where = [ /2]. A polynomial is fit
for a single point, the last observation of the CPU utilization (i.e., the right
limit of the data set). The problem of the boundary region is well-known as
leading to a high bias. According to Cleveland, fitted polynomials of degree 1
typically distort peaks in the interior of the configuration of observations,
whereas polynomials of degree 2 remove the distortion but result in higher
biases at boundaries. Hence, for host overload identification, polynomials of
degree 1 are decided to lessen the bias at the boundary. Let be the final
perception, and be the kth perception from the right boundary. In the
proposed Local Regression (LR) algorithm, using the described method
derived from Loess, a new trend line is found for each new observation
(Beloglazov & Buyya 2012). This trend line is used to estimate the next
observation. If the inequalities are satisfied, the algorithm detects a host
overload, requiring some VMs to be offloaded from the host and any of the
VMs allocated to the host.
3.4.4 Robust Local Regression
The version of Loess is vulnerable to outliers that can be caused by
heavy-tailed distributions. To make Loess robust, Cleveland (1979) proposed
the addition of the robust estimation method bi-square to the least-squares
method for fitting a parametric family. This modification transforms Loess
into an iterative method. The initial fit is carried out with weights defined
using the tri-cube weight function.
Using the estimated trend line, the method described in Section
3.4.3 is applied to estimate the next observation. If the referred inequalities
are satisfied, the host is detected to be overloaded. This host algorithm is
denoted Local Regression Robust (LRR).
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3.4.4.1 VM Selection
Once a host overloading is detected, the next step is to select VMs
to offload from the host to avoid performance degradation. This section
presents three policies for VM selection.
3.4.4.2 The Minimum Migration Time Policy
The Minimum Migration Time (MMT) policy migrates a VM v that
requires the minimum time to complete a migration relatively to the other
VMs allocated to the host. The migration time is estimated as the amount of
RAM utilized by the VM divided by the spare network bandwidth available
for the host j. Let Vj be a set of VMs currently allocated to the host j. The
MMT policy finds a VM v that satisfies the conditions formalized.
3.4.4.3 The Random Selection Policy
The Selection Choice (RS) policy randomly selects a VM to be
migrated from the host according to a uniformly distributed discrete random
variable, whose values index a set of VMs allocated to the host j.
3.4.4.5 The Maximum Correlation Policy
The Maximum Correlation (MC) policy is based on the idea
proposed by Verma et al (2009). The idea is that the higher the correlation
between the resource usage by applications running on an oversubscribed
server, the higher the probability of the server oversubscription. According to
this idea, those VMs are selected to be migrated that have the highest
correlation of the CPU utilization with the other VMs. To estimate the
correlation between the CPU utilization of VMs, the multiple correlation co-
efficient is applied. It is used in multiple regression analysis to assess the
quality of the prediction of the dependent variable. The multiple correlation
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coefficient corresponds to the squared correlation between the predicted and
the actual values of the dependent variable. It can also be interpreted as the
proportion of the variance of the dependent variable explained by the
independent variables. A set of random variables represent the CPU
utilization of VMs allocated to a host. The objective is to evaluate the strength
of the correlation between remaining random variables. An augmented matrix
containing the observed values of the independent random variables is the
vector of observations for the dependent variable. The matrix is called
augmented because the first column is composed only of one. A vector of
predicted values of the dependent random variable is obtained. Having found
a vector of predicted values, it is now possible to compute the Multiple
Correlation Co-efficient, which is equal to the squared coefficient of
correlation between the observed values of the dependent variable and the
predicted values.
3.5 RAM BASED HOST OVERSUBSCRIPTION (RHO)
One of the simplest overload detection algorithms is dependent
upon a thought of setting a CPU utilization limit recognizing the non-over-
loading and over-loading status of the host. The point when the calculation is
summoned, it thinks about the present CPU utilization of the host with the
characterized edge. In the event that the edge is surpassed, the calculation
distinguishes a host over-loading. This section presents a heuristic algorithm
for auto-adjustment of the utilization threshold based on statistical analysis of
historical data collected during the lifetime of VMs. The algorithm applies a
robust statistical method, which is more effective than classical methods for
data containing outliers or coming from non-normal distributions. The
proposed adaptive-threshold algorithm adjusts the value of the CPU
utilization threshold depending on the strength of the deviation of the CPU
utilization. The higher the deviation, the lower the value of the upper
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utilization threshold. This is explained by an observation that a higher
deviation increases the likelihood of the CPU utilization reaching 100% and
causing an SLA violation.
In this chapter we propose an efficient energy aware technique by
combination of two techniques RHO and RVS and we consolidate based on
RAM for an efficient energy analysis in a data center using Local Regression.
In recent cloud computing research the data center analysis is done in
performance and thermal analysis and better energy efficient techniques have
to be implemented. We consider a data center with number of hosts and
allocate VMs to each host according to the kind of applications or work load
fed to the system. The system load can be defined by the kind of PlanetLab
workload that we have fed in the sample code using CloudSim simulation
software. Here we have analyzed a situation involving number of hosts each
host having a capability of holding number of VMs. The VM utilization
depends on the multi-dimensional tuple characteristic of its evolution. VM,
Host and Cloudlet are multi-dimensional and depend on various dimensions.
The host utilization and VMs utilizations are calculated periodically and
updated and saved in an array named and expressed in Equation (3.17).
= and = (3.17)
Here the matrices and represent the utilization values of all
the hosts and VMs within these hosts at ‘ ’ time instants. We have the values
of these host utilization arranged in oldest to the latest fashion and with
respect to time, the same applies to VM utilization values. Our algorithm
aims at limiting the utilization values to suitable levels so as to protect our
hosts from running in to over utilized states. The utilization values are
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calculated by taking RAM as parameter. The total requested RAM within a
host and the total requested RAM by all the cloudlets running within a VM
are being considered for calculation of utilization limits for Hosts and VMs
respectively. The newly allocated VMs are to be placed inside hosts with
minimum utilization care must also be taken to prevent the hosts from running
into over utilized state by suitably adding VMs into a migratableVMs list by
considering various parameters. The MigartableVMs list is represented as a
linear array with respect to a particular host given by ‘ ’. The simulation
software used by us is CloudSim and we have incorporated real time work
load samples from PlanetLab. The VM placement and migratable VM
selections are completely based on statistical analyzes using most efficient
statistical approaches namely Median of Absolute Deviation (MAD)
technique and Minimum Utilization Technique (MUT). We have our data
centers broker and cloudlets created followed by identification of utilization
values of all hosts. If any of these utilization values exceed our statistically
determined value of utilization we declare that host to be overloaded. The
overloaded hosts assign an array namelyH . These overloaded hosts are being
considered for running VM selection algorithms that selects the VMs that if
migrated from these hosts can help them from getting overloaded.
The value of utilization of each host is calculated using the concept
of total number of VMs running within a data center and the current amount
of RAM requested by all of them. This quantity when divided by the total
RAM allocated to the host under consideration is done we obtain the
utilization of that particular host. The utilization of host-1 at a time instant is
given by . Here represents the RAM capacity of each VM within Host-1.
‘ ’ gives the total RAM allocated to host-1.
= (3.18)
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Hence we obtain the matrix of utilization history of all hosts for ‘ ’
particular time instants. This utilization matrix is used for calculating the
MAD (Median of absolute deviation) for each host. The process is explained
in Equation (3.19).
= (3.19)
At any particular time instant say we consider utilization values
of all hosts from the first column represented by the variable =
, , , … , . We calculate the median of array ‘ ’ and name it as
‘ ’.We calculate the deviation array and name it as delta ‘ ’. is
represented as = [ ; ; … , … , … , ] . We
calculate the median of the deviation array which in turn is represented by
‘ ’. Similarly we calculate the value for all the ‘n’ time instants
represented as = { ; ; … ; … ; … ; } . At a particular instant we
compare the values of utilization for all hosts i.e., , , , … , with
and all those hosts with utilization value greater than is declared as
over-utilize. Hence we set an array of over-utilized hosts named
comprising all those hosts with its utilization value greater than , i.e., >
.This array of over-utilized hosts is deducted from the list of total hosts to
obtain the array of underutilized hosts. The array of underutilized hosts is
being considered while allocating new VMs to the data centers. This list of
underutilized hosts will also be considered for switching off of data centers
thereby reducing power depletion by allocating the current VMs in these data
centers to other hosts so that the jobs performed are unaffected. From the
array of over-utilized hosts we fix upon the hosts which has to be subjected to
VM selection algorithms there by helping these hosts from going in to over-
utilize state.
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Pseudocode for RHO
Input :Utilization History of Host;
Output: Migratable VM list
For each host all the VMs are loaded in to an array
For each host in overutilizedhostlist do
If host.availableRAMutilization>vm.requestedRAMthreshold do
Update overutilizedHostlist
Hostlist.add(sortedHostlist)
If host in underloadedhostlist do
MigratableVMslist.add(DecreasingOrderutilizazion)
Migration map.list(update)
Return migration map
Figure 3.5 Pseudo code and Flow Diagram for RHO
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The pseudo code flow diagram for the proposed RHO is shown in
Figure 3.5. The RHO finds the host based on incoming VMs as shown in the
pseudocode. The utilization of RAM on every set of VMlist is estimated. The
under-utilized HostList is analyzed for the utilization of RAM and is sorted in
decreasing order. In the Migration Map the host is selected in the sorted order
and the VMs are assigned to it. The over utilized hosts are identified by
comparing the under-utilized host with respect to the threshold Th which for
our simulation is taken as 80% as given by PlanetLab trace. For all over-
utilized hosts RVS algorithm is implemented to find the VMs in the list that
creates the over-utilization. The VMs are selected from over-loaded hosts and
migrated to a capable host identified by migration map and equilibrium is
maintained between over-loaded and under-loaded hosts. The complexity of
the algorithms is m * n where m and n are the product of number of Hosts and
VMs.
The performance improvement can be attributed to the efficiency
with which we could predict the future requirement of the host. By
applying median of absolute deviation and calculating for any time instant
helped us in grouping hosts in to Ho list thereby increasing the energy
efficiency by improved RAM allocation. We can see that by implementing
RHO algorithm for host oversubscription detection we could significantly
reduce the overall energy consumed by the data centers. But there is a trade-
off between average SLA violation and energy as it is evident from graph.
We have implemented the RAM consolidation for utilization
calculation in the pseudo code of RHO as shown in Figure 3.5. When
simulated for the MAD statistical technique we obtained results that showed
significant improvements in terms of energy.
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3.6 RAM CONSOLIDATED VM SELECTION (RVS)
To the above list of over utilized hosts we apply the VM
migration policy so that we fix upon the VMs that has to be migrated from
these hosts so as to prevent them from causing SLA violations or at least
reduce the SLA violations. We have implemented minimum utilization
technique to establish the migratableVMs list. The migratableVMs list is
indicated by the variable . If we have ‘k’ number of overloaded hosts, the
VM migration policy is applied to VMs within these ‘k’ hosts. Let each of
these hosts have ‘n’ number of VMs. Then we can approximate the utilization
values of these VMs for a single host-1 as a matrix shown in Equation
(3.20).
= (3.20)
where the column matrix represented by , , … ,
Refers to utilization values of ‘v’ number of VMs in host-1 at a particular
time instant say ‘ ’.
From these values [ , , …, ], we calculate the one with minimum
utilization for migration. This can be proved with help of the theorem of
minimum achievable utilization for fault tolerant processing of periodic jobs.
3.6.1 Minimum Achievable Utilization Theorem
The theorem states that for any job set utilization factor is given by
‘U’ which can be represented by an Equation (3.21).
= + + + (3.21)
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where the factors and represent the execution time of the job and period of the job respectively.
The theorem declares a job set ‘S’ consisting of ‘n’ jobs. The job set ‘S’ can be defined by = { , , } represent the release time. The same utilization concept has been implemented into the cloud concept by calculating utilization of a VM by adding the RAM requirement of all cloudlets running in a VM divided by the total RAM capacity of the VM is given in Equation (3.22).
= (3.22)
Here represents the RAM utilized by each cloudlet if there is a total of ‘n’ Cloudlets running in this VM under consideration. ‘ ’ represents
the total RAM allocated to the VM under consideration. represents the utilization of VM-1 at time instant . Thereby we obtain the utilization values of all the VMs inside the host under consideration, and select VMs with minimum utilization values for migration by mapping them into a migrationmap which will be migrated to under-loaded hosts.
An Efficient Energy aware algorithm by introducing RAM consolidation for VM selection policies has been proposed. RVS has proved to be highly effective in combination with RAM consolidated host oversubscription detection algorithms as they are capable of reducing energy requirement significantly. We have our data centers broker and cloudlets created followed by identification of utilization values of all VMs with in a host under consideration. VM with minimum RAM utilization will be considered for migration. MigratableVMs are saved in a linear array named Migratable VM list. RVS when applied in combination with IQR (Inter Quartile Range), MAD (Median Of Absolute Deviation), and LR (Local Regression) gave competitive results than established VM selection
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algorithms like MC (Maximum Correlation), MMT (Minimum Migration Time) and MU (Minimum Utilization).
Pseudo code for RVS
Input : Utilization History of VMs
Output: Migratable VM list;All the hosts are loaded in to an arrayFor each host in hostlist dohostlistadd(host.RAMutilization)
RAMutilizationHostlist.updateOverutilizedhost.list(update)
If host not an element of overutilizedhost.list doUnderutilized hostlist.add(hosts)underutilizedhost.list(update )
Return migration map;
Figure 3.6 Pseudo code and Flow Diagram of RVS
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The pseudo-code for the RVS algorithm for the over-utilization is
as shown in Figure 3.6. The calculation sorts the agenda of VMs in the
diminishing request of the CPU utilization. By then, it is over and again looks
through the program of VMs and finds a VM that is the best to move from the
host. The best VM is the specific case that fulfils two conditions. To begin
with, the VM might moreover have the utilization higher than the
qualification between the host's general use and the upper usage limit.
Second, if the VM is moved from the host, the qualification between the
upper limit and the new use is the base over the qualities outfitted by all the
VMs. Furnished that there is no such VM, the computation picks the VM with
the most astounding use, removes it from the plan of VMs, and comes back to
an alternate cycle. The computation stops when the new utilization of the host
is underneath the upper utilize edge. The diverse nature of the calculation is
comparing to the after-effect of the measure of over-utilized hosts and the
measure of VMs allotted to these hosts.
We use the above algorithms to form groups by randomly adding
hosts until coalesce is met. We create pairs to achieve coalesce between the
best Cloud character and the best energy heuristics. We handle this parameter
to leverage the energy consumed in a data center where VMs are acquired in a
host by the CPU. The host oversubscription has to be minimized by
maintaining the VM migration to be processed at a host which is not
overloaded. We have implemented the RAM consolidation and for
utilization calculation in the algorithms mentioned. When simulation aimed at
the MAD statistical technique we obtained results that showed significant
improvements in terms of energy. The improvement in energy can be
explained by the effectiveness of implementing a greater number of
instructions per second which in turn has been attributed by the introduction
of . The performance improvement can be attributed to the efficiency with
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which we could predict the future requirement of the host. By applying
median of absolute deviation and calculating for any time instant helped us
in group hosts in to list thereby increasing the energy efficiency of
improved Ram allocation. In RVS algorithm we have applied in such a
way that the current request RAM of VMs when divided by yields
utilization of that VM. These utilization factors are taken into consideration
for adding the VMs into a migratableVMs list ( ). We have approximated
the cloudlets RAM requirements at any particular time instant into a
variable . The sum of all running in a VM when divided by net RAM of
the VM yields its utilization. These utilization values represented by
helping us in mapping VMs to migratable list thereby, preventing the
hosts from running into over-utilized states. For various statistical methods
we have simulated our algorithms and results have been exhibited. The
algorithms are used to find the relationship of cloud character model and find
an efficient scenario where cloud works best. Performance and energy
consumption depends on the availability of efficient resources and scarcity of
efficient resources burdens time of SLA violation and VM migration.
3.7 A BENCHMARK SUITE
A set of workload traces holding information on the CPU
consumption or utilization, gathered at regular intervals from more than a
thousand PlanetLabVMs deployed on servers placed in more than 500 spots
around the globe. A set of execution measurements catching the
accompanying perspectives: overhead of VM merging; overhead of VM
consolidation in terms of the number of VM migration; and execution time of
the consolidation algorithms. Assessment technique recommending the
methodology of getting ready examinations, conveying the framework,
producing workload utilizing the PlanetLab trace, and also handling and
breaking down the outcomes. The accessibility of such a benchmark suite will
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encourage and expedite research endeavours and anticipated headways in the
region of dynamic VM combining. Furthermore, specialists are urged to
expose and offer the actualized combination calculations to improve
execution correlations with anticipated results. Contributed algorithms are
added as class policies and the traces are used for an extensive research. The
accompanying segments give more data on the workload follow, execution
measurements, and assessment technique of the proposed benchmark suite. In
this chapter, IaaS environment focuses on a framework which is an expansive
scale data center with N heterogeneous physical hubs (or) hosts. The CPU
execution of every hub is described in Millions Instructions Per Second
(MIPS), outlined in measure of RAM and bandwidth speed. There is no
nearby memory in the servers and Network Attached Storage (NAS) is the
space to empower live migration of VMs (Beloglazov et al 2011).
Heterogeneous VMs say are solicited by Cloud autonomous customers
which are portrayed by necessities to prepare a VM characterized in MIPS,
measure of RAM and bandwidth (Kliazovich et al 2010) to a host (or) hub.
Different VMs on a solitary physical hub is blended since the cloud provider
supervise VMs. HPC and web-applications, which use the assets concurrently
which is the blended workload is framed by different sorts of provisions. The
client secures SLAs with the asset supplier to formalize the QoS conveyed.
The supplier pays a punishment to the provider in instances of SLA
violations. A local and global manager is sandwiched as the programming
layer of the framework. The nearby supervisors dwell on every hub as a
module of the Virtual Machine Manager (VMM). Their goal is the consistent
following of the hub's CPU usage, resizing the VMs consistent with their
asset needs, and choosing when and which VMs ought to be moved from the
hub.
The Effect of the VM estimate regarding the VM migration and
Energy has been investigated for a day at the PlanetLab where the data center
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is of 800 heterogeneous hosts 50% of which 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 (Corporation, 2012). In DVFS the limited number of states that
can be set to the frequency and voltage of a CPU is the possibility. The
voltage and performance scaling cannot applied to other system components,
such as memory and network interfaces. However, modern servers have
heavy memory and modeling of the multi core processors is a complex
research problem. Therefore, instead of using an analytical model of power
consumption by a server, we utilize real data on power consumption provided
by the results of the SPECpower benchmark as shown in Table 3.1. The
server utilization for a level of load is depicted for 100% load HP Proliant G4
consumes 117 watts.
Table 3.1 SPECwork Benchmark Power Consumption (Watts) of servers at different Load Levels, Feb 2011
Server 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%HP ProLiant
G4 86 89.4 92.6 96 99.5 102 106 108 112 114 117
HP ProLiant G5 93.7 97 101 105 110 116 121 125 129 133 135
Virtual Machine Managers are the paramount part of our trials
since they perform genuine resizing and movement of VMs and also updates
in force modes of the hubs. The global master resides on the master hub and
gathers data from the nearby administrators to keep up the general perspective
of the use of assets, it likewise issues orders for the streamlining of the VM
position. Since we utilize CloudSim (Beloglazov & Buyya 2012)which is the
MIPS rating is given by the server CPU recurrence: 1860 MIPS every center
of the HP Proliant Ml110 G5 server and 2660 MIPS every center of the HP
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Proliant Ml110 G5 server. Every server is modeled to have 1 Gb/s system
transmission capacity. The qualities of the VM sorts compare to Amazon EC2
instance types with the main exemption that all the VMs are single-center,
which is clarified by the way that the workload information utilized for the
reproductions hail from single-center VMs as given in Beloglazov & Buyya
(2012). For the same explanation for why the measure of RAM is partitioned
by the amount of centers for every VM sort: High-CPU Medium Instance
(2500 MIPS, 0.85 GB); Extra Large Instance (2000 MIPS, 3.75 GB); Small
Instance (1000 MIPS, 1.7 GB); and Micro Instance (500 MIPS, 613 MB). VM
sort's asset prerequisites characterize the VM allotment; VMs use less
resource as per the workload information, making chances for dynamic
consolidation. In this chapter, IaaS environment focuses on a framework
which is an expansive scale data center with N heterogeneous physical hubs
(or) hosts. We have simulated the cloud model for the best conceivable QoS
bearing the trade-off between the performance and power consumption.
3.8 REAL TIME WORKLOAD TRACES
To make simulations reproducible, it is essential to depend on a set
of trace to dependably produce the workload, which might permit the
analyzes to be rehashed the same amount times as fundamental. It is likewise
significant to utilize workload trace gathered from a true framework as
opposed to falsely created, as this might serve to imitate a reasonable
situation. This part utilizes workload trace information furnished as a part of
the CoMon venture, a monitoring infrastructure of PlanetLab. The trace
incorporate information on the CPU utilization gathered for a day from more
than a thousand VMs sent on servers found in 500 places as far and wide as
possible. 10 days of workload trace gathered throughout March and April
2011 have been haphazardly picked, which brought about the sum of 11,746
24-hour long trace. The full set of workload follow is freely accessible on the
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web. The workload from PlanetLabVMs is illustrative of an IaaS nature's
domain, for example Amazon EC2, as in the VMs are made and supervised
by numerous autonomous clients, and the foundation supplier is not familiar
with what specific requisitions are executing in the VMs. Moreover, this
infers that the general framework workload is made out of various
autonomous heterogeneous provisions, which likewise relates to a nature's
turf. In any case, there is distinction from an open Cloud supplier, for example
Amazon EC2. The contrast is that PlanetLab is a base basically utilized for
exploration purposes; consequently, the provisions are possibly closer to the
HPC sort, instead of web administrations, which are normal open Clouds.
HPC requisitions are normally CPU-escalated with easier progress in the asset
use contrasted and web administrations, whose asset utilization hinges on
upon the amount of client solicitations and might fluctuate over the long haul.
HPC workload is simpler to handle for a VM union framework because of
slower variety in the asset use. Hence, to stretch the framework in the
examinations, the definitive workload follow have been sifted to leave just the
ones that show high variability. Specifically, just the follow that fulfil the
accompanying two conditions have been chosen: (1) no less than 10% of time
the CPU usage is lower than 20%; and (2) no less than 10% of time the CPU
utilization is higher than 80%. This essentially diminished the amount of
workload follow bringing about just 33 out of 11,746 24-hour trace left. The
set of chosen trace and separating script are accessible online. The resulting
amount of trace was sufficient for the analyzes, whose scale was restricted by
the span of the testbed portrayed in Section 3.8. In the event that a bigger
number of trace is instructed to fulfil bigger scale tests, one approach is to
unwind the states of sifting the definitive set of trace. An alternate approach is
to arbitrarily inspect with replacing from the restricted set of trace available.
In the event that an alternate set of suitable workload trace comes to be openly
accessible, it could be incorporated in the benchmark suite as an elective.
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3.9 PERFORMANCE METRICS
For effective performance evaluation and comparison of algorithms
it is essential to define performance metrics that capture the relevant
characteristics of the algorithms. One of the objectives of dynamic VM
consolidation is the minimization of energy consumption by the physical
nodes, which can be a metric for performance evaluation and comparison.
However, energy consumption is highly dependent on the particular model
and configuration of the underlying hardware, efficiency of power supplies,
implementation of the sleep mode, etc. A metric that abstracts from the
mentioned factors, but is directly proportional and can be used to estimate
energy consumption, is the time of a host being idle, aggregated over the full
set of hosts. Using this metric, the quality of VM consolidation can be
represented by the increase in the aggregated idle time of hosts. However, this
metric depends on the length of the overall evaluation period and the number
of hosts.
SLA Violation (SLAV) and Performance Degradation due to
Migration (PDM) as given in Equation (3.23) and (3.25).
= (3.23)
= % (3.24)
= ( )
( )(3.25)
SLA time per Active Host (SLATAH) is given by the Equation
(3.24) where the number of hosts is , % is the total time during which
the host has experienced the utilization of 100% leading to an SLA violation.
is the total of the host being in the VM feeder state.
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PDM is given in Equation (3.25) where the number of VMs is ;
( ) the estimate of the performance degradation of the VM caused by
migrations; ( ) is the total CPU capacity requested by the VM
during its lifetime.
The threshold for our simulation is fixed at 80% for over-utilized
hosts or processors. Over-utilized hosts are the difference between total active
hosts and the under-utilized hosts. VMs are identified and updated into
migratableVMlist. Reason behind host overloading is SLATAH which is a
negative parameter and this leads the hosts to an inefficient state.
3.9.1 Average SLA Violation
Average SLA violation is the SLA violation for the whole
simulation time. The SLA violation time till the end of the simulation eg: 24
hours (1 Day) is calculated.
3.9.2 VM Migration
VM migration is the transfer of VM on NAS between hosts (or)
CPU. It is done by Live Migration where a running VM is migrated without
disconnecting the client or application.
3.9.3 Energy Consumption
The Energy Consumption is the energy consumed by two hosts for
one VM migration. The amount of Energy consumed by the data center for a
day is calculated.
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3.10 PERFORMANCE EVALUATION METHODOLOGY
The following step is composing scripts for planning the
framework for an experiment, which incorporates beginning up the needed
administrations, booting VM instances, and preparing them for starting the
workload generation. While the greater part of the specified steps are trifling,
workload generation is confounded by the necessity of synchronizing the time
of beginning the workload generation on all the VMs. An alternate critical
part of workload generation is the way workload trace are executed out to
VMs. Normally, the wanted conduct is running out a special workload trace
out of the full set of trace to every VM. At last, it is essential to make and
administer a particular level of CPU usage for the entire interim between
progressions of the CPU utilization level, characterized by the workload trace
for every VM.
This issue is tended to utilizing a consolidation of a CPU load
generation program, and a workload distribution web administration and
customers conveyed on VMs. The point when a VM boots from a
preconfigured picture, it immediately begins a script that surveys the focal
workload appropriation web administration to be relegated a workload trace.
At first, the workload dispersion web administration drops asks for from
customers sent on VMs to hold up for the minute when all the needed VM
occasions are booted up and primed for creating workload. When all
customers are primed, the web administration accepts an order to begin the
workload follow circulation. The web service begins answering to customers
by sending each of them an exceptional workload follow. Upon getting a
workload trace, each customer starts the CPU load generator and passes the
appropriated workload follow as a contention. The CPU load generator
peruses the furnished workload follow record, and begins producing CPU
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usage levels relating to the qualities specified in the workload trace index for
every time frame.
3.11 RESULTS AND DISCUSSION
We have our data center broker and cloudlets created followed by
identification of utilization values of all hosts. If any of these utilization
values exceed our statistically determined value of utilization we declare that
host to be overloaded. The overloaded hosts assign an array namely Hoj. The
Hosts consumption for each VM is higher when the VM density gets higher.
The VM characteristic component utilization gets higher when the utilization
of the virtual component in the VM such as Processor, RAM or Memory gets
higher. Here we have shown that by implementing RHO algorithm for host
oversubscription detection we could significantly reduce the overall energy
consumed by the data centers. But there is a trade-off between average SLA
violation and energy still exists as it is evident from graph that when we were
successful in reducing the energy we had an increase in SLAV which in turn
proves the fact that energy and SLAV are inversely related. From the Figure
3.7 we can see that the RHO has 27.22% better performance in terms of
energy when compared to an IQR algorithm for host oversubscription
detection.
The Energy consumed by the proposed Host Overloading detection
based on the energy consumption of the system showed varied results as in
Figure 3.7. We harnessed the requested RAM characteristics and found the
Energy efficiency was achieved. The Energy Efficiency in turn had to deal
with an inefficient nature of average SLA violation. Here we can see that
RHO has significantly better performance in terms of PDM when compared
to other host oversubscription detection algorithms. Here again we can see
that PDM and SLAV has a trade-off, as we try to improve PDM we have
compromises in SLAV. For RHO there is 83% improvement in PDM when
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compared to IQR. Decrease in PDM was due to the lesser VM migration
caused due to the proposed method.
Figure 3.7 Analysis of SLAV and Energy of the proposed RHO and RVS with respect to MMT and other VM consolidationalgorithms for 1052 VMs and 800 hosts
VM migration is much higher in other methods. The Hosts which
are alive should necessarily maintain equilibrium between the VM migrations
and Host Oversubscription. Virtual Machine Manager should be designed to
handle workloads as and when the Host gets overloaded.
3.11.1 Energy Vs SLAV (MC)
For VM selection algorithm MC (Maximum Correlation) as in
Figure 3.9 we have compared the performance of host oversubscription
detection algorithms RHO, IQR, LR, MAD. From the observations we can
see that RHO has been able to give 37.26 % improvement over IQR. Here
again we can see a trade-off between SLAV and Energy. As we try to
improve upon energy factor we have a trade-off in SLAV. The average SLA
1
5
25
125 RHOMMT
IQRMMT
LRMMT MADMMT
RHORVS
ENERGY (kWh)
SLAV (%)
99
violation when compared to other techniques our proposed algorithm
decreased.
Figure 3.8 Analysis of SLAV and PDM of the proposed RHO and RVS with respect to MMT other VM consolidation algorithms for 1052 VMs and 800 hosts
This efficiency was evident in our proposed method. RAM based
consolidation proved more efficient than other Host oversubscription and VM
selection techniques. RAM utilization in VM and its MIPS space occupied on
a host depends on the Host characteristics. Hence RAM consideration being
an important parameter was harnessed for an Efficient Energy strategy.
0.0001
0.01
1 RHOMMT
IQRMMT
LRMMT MADMMT
RHORVS
PDM (%)
SLAV (%)
100
Figure 3.9 Analysis of SLAV and Energy of the proposed RHORVS with respect to other MC and other VM consolidation algorithms for 1052 VMs and 800 hosts
3.11.2 PDM Vs SLAV(MC)
Here we tabulate the performance of MC along with host
oversubscription detection algorithms like RHO, LR, MAD, IQR. We can see
that there is a trade-off between PDM (power degradation due to migration)
and SLAV. As we try to improve up on PDM we have a faceoff in SLAV. We
have attained 74% improvement in terms of PDM by implementing RHO
over IQR. Comparing other methods of combination of VM placement and
Host Oversubscription techniques the proposed RAM based consolidation
proved better. The PDM decreased and a meager increase in SLAV was
observed. The number of VM migration reduced to a 73% which helped us to
efficiently harness the estimate of performance degradation. The performance
degradation reduction helped in energy reduction due to a small SLATAH
time higher than the legacy method. SLA violation and the PDM analysis is
depicted in Figure 3.10.
1
5
25
125 RHOMC
IQRMC
LRMC MADMC
RHORVS
ENERGY (kWh)
SLAV (%)
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Figure 3.10 Analysis of SLAV and PDM of the proposed RHORVS with respect to MC and other VM consolidation algorithms for 1052 VMs and 800 hosts
The mean value of the sample means of the time before a host is switched to
the sleep mode for the RHO RVS algorithm combination is 645.5 seconds
with the 95% CI: (689, 603). The mean value of the sample means of the time
before a VM is migrated from a host for the same algorithm combination is
20.26 seconds with the 95% CI: (19.9, 20.62). The mean value of the sample
means of the execution time of the RHO RVS algorithm on a server with an
Intel Core i7 (2.40 GHz) processor and 2 GB of RAM is 0.14 ms with the
95% CI: (0.13, 0.15).
3.12 SUMMARY
Taking into account the literature in Chapter 2, this part proposed
novel heuristics for distributed dynamic VM consolidation that depend on an
investigation of verifiable information on the CPU utilization by VMs to
leverage the predictability of the workload. Cloud providers need to apply
energy productive resource management procedures, for example dynamic
0.0001
0.01
1 RHOMC
IQRMC
LRMC MADMC
RHORVS
PDM (%)
SLAV (%)
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VM consolidation with switching idle servers to power-saving modes to
maximize their Return On Investment (ROI). However, such consolidation is
not trivial, as it may result in violations of the SLAs negotiated with
customers. Re-enactments of an expansive scale data center utilizing
workload trace from more than a thousand PlanetLabVMs have demonstrated
that the proposed proximity based host oversubscription estimation. With the
MMT and MC, VM consolidation essentially outflanks other dynamic VM
consolidation calculations with respect to the performance metrics because of
a significantly decreased level of SLA violations and the amount of VM
migrations. Energy efficiency of about 27 % was achieved due to the
proposed algorithm RHO RVS. The proposed algorithms had trade-off
between SLA violation and performance degradation due to migration. Hence
there was a necessity to model a hypothesis for an energy perspective for the
trade-off parameters. Cost model was necessary to define SLA violation and
VM migration timing constraints. Energy Curve model due on the cost
function based on VM migration and SLA violation has been presented in the
chapter 4.