Post on 09-Mar-2018
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CHAPTER 2
SYSTEMATIC LITERATURE REVIEW
2.1 INTRODUCTION
Cloud Computing data center is our topic of importance. In this
chapter we focus on addressing the power and performance trade-off. The
trade-off is vital since the available processing elements consume a lot of
energy as discussed in chapter 1. The inherent unreliability of the distributed
systems poses a major challenge in this research. To begin with, in today's
data centers, a power control strategy might come straight from a server
source (e.g., IBM) and is actualized in the administration processor firmware,
without any information of the provision programming running on the server
(Wang et al 2011b). The server is virtualized in a cloud environment and the
virtualized instances have to be handled with care to use the processing
machines (or) the hosts efficiently. Hence a proper VM consolidation is
necessary to solve the trade-off.
2.1.1 Eco-efficient Data center Management
Eco-efficiency can be directly linked to being environmentally
friendly. It is about how to manage the data centers of the clouds in ways that
have less impact on the energy consumed, as well as on carbon dioxide (CO2)
emissions. Therefore, mechanisms and policies should be put in place to help
understand how green the data center of a cloud is. The dramatic increase in
greenhouse gas emissions is having a detrimental effect on the global climate,
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like increasing temperatures, dryness, and floods. Also, the ICT industry has
been contributing to this growth; as has been stated by Smarr (2010), who
explains that the carbon emission from this sector is expected to triple from
2002 to 2020. Most of the literature reviewed towards energy efficient data
center focuses on the proper VM consolidation and the machine status in a
data center.
2.1.2 VM Allocation and Effect on Energy Consumption
Firstly, a study by Corradi et al (2012) states that Virtual Machine
(VM) consolidations can be used as a means of reducing the power
consumption of cloud data centers. To illustrate, this technique tries to
allocate more VMs on less physical machines as far as possible to allow
maximum utilization of the running of physical machines. For instance, when
there are two VMs, instead of allocating each one to a physical server that has
not been fully modeled, this technique tries to allocate both VMs on one
physical server and switch the other server off to save energy. Therefore,
using this technique in a data center can reduce the operational costs and
increase the efficiency of energy usage. However, it is important to note that
the number of VMs in one physical machine should not be too high to the
extent that it may degrade the performance of VMs. VM allocation involves
both VM selection and VM placement. Most of the literature conducts
experiments on the reliable processing elements available in a data center.
The distributed system has a property of its own inherent unreliability of the
computing machines available.
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2.1.3 Machine-Learning Algorithms
The status of the machines available is done using machine learning
techniques. The CPU utilization is predicted and the machines are termed
either under-utilized or over-utilized. The machine-learning approach views
resource provisioning as a demand-prediction function, and applies various
machine learning algorithms to estimate how the Workflow changes over
time. Workflow refers to the set of services that an application is composed of
jobs (Nathani et al 2012). Literatures use multiple linear regressions and feed
forward neural networks to predict resource demand for a cloud. In the
context of IaaS, they enable a hosted application to make autonomic scaling
decisions using intelligent resource prediction techniques. All of these
techniques, however, require historical data to learn effectively. To simulate
historical data, they run a standard client-server benchmark application, TPC-
W, on Amazon’s EC2 cloud. This data is then divided into training sets and
validation sets, using a variety of statistical techniques. We have discussed
some papers on reliability in section 2.4.
Linear regression attempts to fit a curve to the given data points,
while minimising the error between the curve and the observed data. It
effectively yields a function that, when successful, approximates the real
process which has given rise to the observed data. Neural Networks are
another machine-learning technique, which approximates a real-world
process. A neural network consists of an input layer, an output layer and one
or more hidden layers. Each layer consists of neurons that have a certain
value, and are connected to the next layer’s neurons by way of synapses.
These synapses initially start with random weights, but get adjusted on the go.
The network is trained by presenting a known input to the input layer, and
observing the output at the output layer. The difference between the output
produced by the network, and the actual output is the error. This error is fed
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backwards into the network, to allow the synapses to change their weights.
This is called Training the network. The Sliding Window technique is a
sampling technique to allow the learning algorithm to view the same dataset
from different sample perspectives.
After training, both Linear Regression and Neural Networks were
evaluated using unseen data and compared on several statistical measures.
Neural Networks were found to be able to generalise well, with the prediction
of resource usage closely matching the actual data. However, the only
parameter used by the authors was the resource load placed on the cloud
provider. In reality, each resource has several attributes that are each decision
parameters in their own right. In such a scenario, it is difficult to train such
machine learning methods. Increasing the number of hidden layers in a neural
network does not necessarily increase its predictive power.
2.1.4 Resource Allocation in the Cloud
Byun et al (2011) proposed a cost-optimization method for task
scheduling. They attempt to find the minimum number of resources, given a
set of tasks with deadlines. Like swapping and backfilling attempt to move
tasks that are not in the critical path of the application Workflow, such that
the total cost of the resources used for the Workflow is minimized. Their
algorithm, called Partitioned Balanced Time Scheduling (PBTS), assumes that
tasks are non-preemptible and executed on a single resource or set of
resources. Based on the minimum time charge unit of the provisioning system
(say 1 hour on Amazon’s EC2), the algorithm divides the application’s
estimated work-time into time-partitions. It then iterates over all the tasks that
are yet to be executed, and estimates the next set of tasks that can be fully
scheduled in the next time-partition and their required resources. Having done
this, it schedules these tasks for execution, and repeats the cycle for the
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remaining tasks, until all tasks are completed. While this results in minimum
cost of resources, it does not take into account other qualities of resources that
might be required by a certain task. For instance, a task might request for 1
hour of processing on a node that has a certain kind of graphics chip or level-
1 cache. Since PBTS assumes that all resource units are homogeneous, these
type of requests cannot be accommodated.
As a result, cluster-level control solutions are needed to allow
shifting of power and workload for optimized system performance. Virtual
power management as discussed in (Nathuji & Schwan 2007) was the first
initial experiments done in this field where they have proposed architecture of
a data center’s resource management system where resource management is
divided into Local and Global policies. At the local level the system leverages
the guest OS’s power management strategies. The global manager gets the
information on the current resource allocation from the local managers and
applies its policy to decide whether the Virtual Machine (VM) placement
needs to be adapted (Beloglazov et al 2011). Dynamic resource management
at the global level was not addressed.
Cluster level performance is very important when there is a large
amount of virtualized servers in play and the servers are vital and a
coordinated energy efficient approach is always viable and should be taken
care of. As many data centers start to adopt virtualization technology for
resource sharing, application performance of each virtual machine (instead of
the entire server) needs to be effectively controlled. Beloglazov & Buyya
(2012) have discussed extensively on the Host oversubscription detection and
VM selection algorithms and competitive analysis of the single VM migration
and dynamic VM consolidation problems have been experimented. Energy
efficient algorithms have been discussed elaborately extending the available
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algorithms and Minimum energy heuristics have been deployed for cyber-
physical systems where energy from a batter is a major constraint (Wu et al
2011). The literature surveys for the work proposed in the objectives are
addressed in the rest of the chapters.
Nathuji & Schwan (2007) investigated the first time the power
management procedures in the connection of virtualized frameworks as
shown in Table 2.1. Many other papers relating to data center literature have
also been tabulated. They researched the issue of power proficient resource
management in expansive scale virtualized data centers. The creators
portrayed numerous closely related approaches pointed at the minimization of
force utilization under QoS demands, and at power capping. The worldwide
arrangements are answerable for managing different physical machines
utilizing the learning of rack or edge level server attributes and necessities.
These arrangements solidify VMs utilizing movement within request to free
softly stacked server and put them into power recovering states. The authors
proposed part into neighbor-hood and worldwide approaches. At the nearby
level, the framework facilitates and influences force management approaches
of visitor VMs at every physical machine. An illustration of such an
arrangement is the on-interest senator combined into the Linux bit. At this
level, the requisition level QoS is supported as choices about progressions
in power states are issued by the visitor OS. The investigations led by the
creators indicated that the utilization of the proposed approach accelerates
productive coordination of VM and provision particular power management
approaches, and decreases power utilization up to 34% with next to zero
execution errors.
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Table 2.1 Literature consolidated for IaaS
Author Idea Technique Harnessing Component
Virtualization Implementation
(Nathuji &Schwan 2007)
To reach Minimum Energy Consumption with
Performance Constraints
DVFS, Power Switching, Soft
ScalingCPU, VM Yes
(Garg et al 2011)
To Achieve Minimum Energy
and CO2,Maximum Profit
DVFS CPU No
(Pinheiroet al 2001)
To Achieve Minimum Power,
Performance Constraints
SwitchingServer Power
CPU, Drive, Network No
(Kumar et al 2009)
Performance and Power Budget Constraints, To
Achieve Minimum Energy
DVFS, VM Consolidation
CPU, RAM, Network Yes
(Buyya et al 2010)
Minimum Energy under
Performance Constraints
DVFS CPU Yes
(Chase et al 2001)
Minimum Power under
PerformanceConstraints
Workload Consolidation CPU No
(Beloglazov& Buyya
2012)
Energy and VM Consolidation LRMMT CPU and VM
Consolidation Yes
Keeping servers under-utilized is the principle reason behind the
energy utilization around different issues in a data center like the cooling,
individual server leveraging, provisioning and bandwidth accessibility
stipulations. Graph theory has been studied in wide area of distributed
systems and cloud computing is picking up pace in the Distributed
frameworks enclosure (Kwok & Ahmad 1999; Henricksen & Indulska 2006;
Ghazale Hosseinabadi 2009; Fakhar et al 2012). Lundqvistet al (2012) have
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discussed on the service program mobility which helps CDN to efficiently
provide QoS to the mobile applications. Chen et al (2012) have discussed
over machine to machine transactions on the system correspondence where
different system dominions were highlighted and proficient outcomes were
distributed however server side designs in the correspondence issues were not
talked over, where virtualization is the way to handle variable burdens
regarding the matter of machine to machine transactions and the space issues
concerning virtual had cases which have been discussed about in this paper.
Simultaneous power and scheduling control faces several major challenges.
We have concentrated on the creation of a workflow model to
better understand the cost function and the expense of power of hosts in a
datacenter. The datacenter consists of hosts containing VMs and all
datacenters are distributed globally to address the Infrastructure as a Service.
The IaaS consists of the machines and support modules organising an
infrastructure. The literature has been partitioned into three parts where we
include three works proposed and their consequence to the proposal. The
Cloud Graphical Workflow model has been concentrated in our first part of
the literature survey. In the second work we concentrate on the Energy Curve
model and address the VM migration and SLA violation metrics to harness
the power performance trade-off. Next we concentrate on the machine
learning techniques and their effect on predicting the behaviour of the hosts
available in the datacenter. The over-loading or under-loading of servers were
proposed by a different machine learning technique to efficiently study the
behaviour of reliable nodes based on their alive status which is a binary
property. Finally we extend the literature to analyze the reliability as a
statistical property.
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Based on literature survey, we understand that Energy consumption
on cluster level performance and trust based QoS analysis has not been
considered. We have taken sincere attempts in our thesis to address the
aforementioned issues. Learning environments for cloud computing and
related applications are available. The applications involved and the
environment needed to create a cloud stack is very important. But the real
analysis and the environment can be studied by simulation tools and learning
by simulation is vital when research is considered. Emotional aspects of
involving cloud computing into learning are discussed (Rizzardini & Amado
2012). Cloud based activities were discussed but research based activities in
cloud can be realised when simulated learning is brought in to learn the cloud
itself.
Mikroyannidis (2011) has concentrated on Personal Learning
Environment and the role it plays. ROLE (Responsive Open Learning
Environment) was established and survey from 19 students was conducted
and results were discussed (Rizzardiniet al 2012). Our paper on the other hand
concentrates on the Learning experience of a data center in simulation basis.
Emphasis on the three important layers viz., Software, Platform and
Infrastructure and the learning concepts regarding these layers and how they
make an effect in the cloud computing stack are worth research areas.
Cooperative Energy aware techniques were discussed in our previous paper
(Park & Pai 2006).
Lundqvistet al (2012) have discussed on the service program
mobility which helps CDN (Content Delivery Networks) to efficiently
provide QoS to the mobile applications. Chen et al (2012) have discussed
machine to machine transactions on the network communication where
various network domains were highlighted and efficient results were
published but server side configurations in the communication issues were not
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discussed. In our thesis, we discuss virtualization as the key to handle variable
workloads when it comes to server to server transactions and the storage
issues with respect to virtual hosted instances. In the paper by Chu & Chen
(2011) for a Green cloud, they have addressed the communication and
computation as a model but the Energy consumption in a data center is mainly
due to the under-used servers where the addressing of the VM dynamic
consolidation in a host is of utmost importance. Hence we have come up with
the Minimum Energy Heuristics which caters to the dynamic consolidation of
VM by linear approximation between the two objectives i.e., VM migration
and SLA violation. Mobile virtualization and Green mobile networks have
been discussed in many other technical papers as well as (Mijat & Nightingale
2011, Wang et al 2012, Seo 2010, Weinberg & Pundit 2009) the authors have
discussed coarsely on the mobile virtualization and the processor handling in
a mobile perspective which is vital but the server VM consolidation is to be
addressed when it comes to the data center energy perspective.
VM Consolidation technique involves VM selection before VM
migration. Energy Consumption due to VM migration involves two hosts
which are of importance since twice the energy of a single host is depleted for
a single VM migration. Our Energy model leverages this Energy-Performance
trade-off perspective. For the keywords searched in IEEE transactions, VM
consolidation and QoS we selected 14 papers from a set of 200 papers from
2008 to 2013 which almost resembles our goal of work. VM selection related
literature are concentrated in this section and as seen most of the literature
have not considered VM migration for energy related parameter efficiency.
2.2 DATA CENTER IN DISTRIBUTED SYSTEMS
The distributed systems where we concentrate on the cloud
computing paradigm we focus on the IaaS. Infrastructure as a Service (IaaS)
involves computing machines and handles VMs for the execution of the HPC
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or web workloads also known as Applications. We especially considered VM
consolidation techniques to handle energy consumption and reduce
performance degradation to efficiently harness the available machines in a
data center. Resource sharing is an important aspect for distributed system
processing and communication protocols are concentrated here. The machines
for computation have to be concentrated for the resources. Since the resources
are a stack of processor, memory and operating system which are created as
virtual instances and these instances help in execution of the cloud
applications. The resources to be scheduled have to be addressed when it
comes to Infrastructure as a Service. Carbon footprint due to IaaS has been
taken into account for our literature. The cloud workload depends on various
dependencies due to its virtualized execution. The virtualized instances handle
the workload efficiently and care has to be taken to understand the mechanics
behind an application. Application either needs computation or
communication aspects to be concentrated to efficiently harness the energy
related results to be achieved. The virtual machines contribute to the
provisioning of resources for cloud environment to handle jobs with mixed
workloads. The computing and communication efficiency can be achieved if
better system can be evolved to address the power and performance trade-off
in a data center. The data center handles the workloads and this involves time
for execution of jobs and this in turn involves a depletion of energy due to
operating frequency of a running processor. Many researches on cloud
recently deal with virtual machine monitor and global level aspects of data
center. But local level managers which handle VMs are to be carefully noted
for they incur an expense of power due to VM migrations and VM migration
involves performance degradation due to migration this in turn has got an
expense of power for the time an application gets executed. The Hosts or the
computing machines are either over-loaded or under-loaded and this involves
expense of power running either idle machine where idle machines consume
70% of the energy at that state. The over-loaded machines handle VMs which
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is above their capacity and this in turn leads to an infinite processing thereby
reducing the response and throughput time. The power and performance
trade-off prevails and this has been the focus of research in cloud computing.
The cloud arena and particularly the IaaS involving the resources have a stack
of resources providing computing and communication capabilities which is of
major importance to solve the power performance trade-off. The host status
changes dynamically and forecasting of the host status, before VMs are
provisioned and executed, has to be addressed. The cloud nodes are always
assumed to be reliable and this is assumed based on the host being alive.
Li & Huang (2010) has handled current virtualized cloud platforms;
resource provisioning strategy remains to be a major challenge. Provisioning
will probably gain lower resource utilization determined by peak workload,
and provisioning. The job loads will probably sacrifice the potential profit of
cloud customers because of bad user experiences. VM-based overall
performance isolation in addition restrains source flowing on demand.
Regarding memory, this eventually ends in under-loaded storage and over-
loaded memory inside the same data center. Their paper has proposed a VM-
oblivious vibrant memory optimisation scheme; their case study of server
relief also shows TMemCanal may promote the performance involving
memory-intensive services up to 400%. Server relief is exhaustively
conducted and better results are framed. Memory optimisation was
concentrated in their paper. The resource sharing should be useful but also
look into the proper utility or efficient selection of VMs to provide server
relief. The memory related graph theory was concentrated. But VM and the
Host were also in a graph model so there was a necessity to model the job and
VM complexity.
Wang et al (2011a) focuses on increasing Web business and
processing footprint stimulates server consolidation in data centers. Through
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virtualization technological know-how, server consolidation can lessen
burden on physical hosts and supply scalable providers. However, the
ineffective memory usage among multiple Virtual Machines (VMs) becomes
the bottleneck throughout server relief environment. Because of inaccurate
RAM usage estimate and the possible lack of memory reference
managements, there exists much assistance performance destruction in data
centers, even though they have occupied a substantial amount memory. To be
able to improve that scenario, they first bring in VM's memory division view
and VM's totally free memory section view. Memory usage of the VM has
been considered in this particular paper although VM consolidation
depending on cloud repute was necessary for a much better energy viewpoint.
Mei et al (2011) have concentrated in Server relief and software
consolidation through virtualization. In that paper, they argue it's important
with regard to both cloud consumers and also cloud providers to be aware of
the a variety of factors that may have significant impact on the performance
of purposes running inside a virtualized cloud. Their paper presents an
extensive performance study of community I/O workloads inside a virtualized
cloud environment. Then they study a couple of representative workloads
inside cloud-based info centers, which often compete with regard to either
computation or communication I/O means, and present the precise analysis on
different facets that make a difference in the throughput performance and
useful resource sharing utility. Finally, they review the impact of different
CPU useful resource scheduling strategies and various workload rates about
the performance involving applications migrating on different VMs hosted
through the same real machine. Performance and also Energy trade-off on
response-time centered VM selection parameters are not addressed. Useful
resource sharing and throughput were concentrated but the energy
consumption was our important criteria. Hence carbon footprint and energy
consumption were important to be looked into.
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Moghaddam et al (2011) make a calculation for the carbon
footprint and energy usage of a WAN community of data centers is presented.
This formulation is utilized to measure the footprint of any simulation
platform comprising 13 data centers with seven cities at unique geographical
locations around the world. A heuristic formula (a modified GA) is utilized to
optimise performance as well as footprint of the network. Pertaining to tuning
the particular optimisation, different optimisation intervals are actually
proposed in order to extract the very best optimisation interval. The
communication has been tried under unique loads, and each of our results
show an important carbon footprint reduction as a result of VPC data center
consolidation when compared with LAN server relief. Low Carbon and VPC
is their goal along with a GA algorithm that has been implemented as well as
evolutionary formula perspective is analyzed nevertheless we give full
attention to the strength perspective that has not been concentrated from the
authors.
2.3 VM CONSOLIDATION IN VIRTUALIZED RESOURCE
INSTANCES
VM consolidation in terms of SLA violation was an important
criterion. The virtualized instances and migration of these instances depends
on the machines in a data center. The electricity consumption due to hosts
being alive for VM migration to take place is an important aspect to be
considered. Many literatures have concentrated on this area. Gao et al (2013)
in their paper state probably the most important objectives of data center
management would be to maximise their gain minimising electric power
consumption as well as service-level arrangement violations of hosted
software. In their paper, they propose a management answer which takes
advantages of both personal machine resizing as well as server consolidation
to attain energy efficiency and quality of services in virtualized information
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centers. A novelty of the solution would be to integrate linear encoding, ant
colony optimisation, as well as control idea techniques. An improved energy
aspect was dealt with their paper, VM Number over-subscription as well as
Placement was not discussed in detail. We tried to deal with Host Over-
subscription by means of machine learning techniques of the workload that
has been considered. Execution time and energy consumption can only be
reduced if a proper consolidation can be done.
Viswanathan et al (2011) in their paper presented as well as
evaluated a novel application-centric energy-aware technique for VM portion
that aims at maximising the particular resource operation and strength
efficiency as a result of VM relief while rewarding QoS warranties. To try
this, they formulated an empirical model for the average power consumption
as well as execution time according to measurements by extensive execution
of typical HPC workload benchmarks (all possible allocations according to
number and type of VMs), and designed an algorithm to determine the best
VM portion that achieves an optimisation goal for example minimisation of
energy consumption and/or execution time. Their latest research work are
aimed at making use of machine learning processes to extract on-the-fly any
model from the sub-system operation data compiled from not online
experiments making use of benchmarks. Along with from actual applications
managing VMs as well as they compare planned solution against a lot of real
time traces by employing them.
Wang et al (2011a) focuses on increasing online enterprise and
computing footprint really encourage server relief in data centers. By means
of virtualization technological know-how, server relief can decrease physical
hosts and still provide scalable providers. However, the particular ineffective
memory usage amongst multiple personal machines (VMs) becomes the
bottleneck with server relief environment. The standard test final results show
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their implementation could certainly save 30% physical memory along with
1% RAM usage in order to save 5% overall performance degradation.
Determined by Xen virtualization platform, the work carry dramatic gains to
business oriented cloud data center that is providing over 2,000 VMs'.
Feller & Morin (2012) in their paper presented as well as evaluated
the vitality management mechanisms of any unique cutting edge of using
energy-aware VM management framework named Snooze. Snooze features a
direct request: it can certainly be either utilized so as to efficiently deal with
production data centers or maybe server like test-bed for state-of-the-art
energy-aware VM arranging algorithms. Moreover we decide to integrate
each of our previously planned nature-inspired VM relief algorithms as well
as compare its scalability with the existing greedy algorithm along with
alternative relief approaches (e.g. based in linear programming). In this thesis
we decide to apply machine learning techniques so as to predict VM learning
resource utilization highs and bring about pro-active results.
Moses et al (2011) in their paper have showcased exactly why the
issue of contention from the shared cache is often a critical issue in virtualized
foreign computing information centers. Future operate would involve detailed
profiling of VMs to steer scheduling decisions. Once enforcement capability
can be purchased, highly complex techniques which combine the main
advantages of monitoring as well as enforcement for cache, memory
bandwidth and also power can be very efficiently used in future cloud-
computing data centers. The discussed literature has been tabulated in Table
2.2 and compared with respect to key parameters.
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Table 2.2 Literature review of VM consolidation in IaaS
Author
Was VM selection taken
into consideration?
Response time of VM taken into account
Energy Consumption
due to VM migration taken into
consideration?
Graph Theory based
Distributed Cloud
analysis?(Li & Huang 2010) No Yes No No
(Mei et al 2011) Yes Yes Yes No
(Moghaddam et al 2011)
Yes No Yes No
(Gao et al 2013) Yes Yes Yes No
(Viswanathan et al 2011)
Yes Yes Yes No
(Wang et al 2011a) Yes No No No
(Feller et al 2012) Yes No No No
(Moses et al 2011) Yes No Yes Yes
2.4 RELIABLE DATA CENTER FOR ENERGY EFFICIENCY
Reliability of cloud nodes are important to be considered. But
instead of binary property perspective based on alive hosts, we find a new
dimension of analysis. A statistical property for reliability gave us a new
dimension in a real world scenario. Imada et al (2009) investigates power
along with QoS (Quality of Service) overall performance characteristics of
virtualized hosts with virtual machine technological know-how. Currently,
one of the vital problems at data centers with plenty of servers is the particular
increased power consumption. Virtual machines (VMs) tend to be used for
Internet companies for efficient server operations and provisioning. While we
expect of which virtualized servers where multiple VMs run help you save
power, new issues in virtualized servers arise compared to conventional
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physical servers: migration with the load between servers along with
processor core assignment with the server's workload from the particular
viewpoints of QoS overall performance and energy consumption. Their
experimental results display that server consolidation using VM migration
plays a part in power reduction without or even with slight QoS overall
performance degradation, and assignment of VMs to be able to multiple
processor cores running at the lower frequency can achieve additional power
reduction over a server node. QoS related server consolidation was addressed
but VM consolidation was not considered.
Li & Huang (2010) has handled current virtualized foreign
platforms; resource provisioning strategy is a big difficult task. Provisioning
will achieve low resource utilization based on peak workload, and
provisioning based on average workload will sacrifice plenty of potential
revenue of cloud customers. Customer cloud experience is determined by the
cloud workload handling and how the process scheduling is done. Resource
provisioning for reliable nodes were considered. The server consolidation and
performance optimisation has to be considered for reliable nodes.
Mei et al (2011) have focused on Server consolidation and
application consolidation through virtualization are key performance
optimisations with cloud-based service supply industry. They argue it is
important for both cloud consumers as well as cloud providers to be aware of
the various factors which will have significant effect on the performance
involving applications running in a much virtualized cloud. Lastly, they
analyze the actual impact of various CPU resource scheduling strategies and
various workload rates for the performance of purposes running on various
VMs hosted through the same physical machine. QoS is an essential
parameter to handle if a large customer base is available. Customer workload
and applications ought to be processed for a new valid response period. SLA
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agreements are bound by way of cloud provider possesses to meet by way of
proper system in position. The under-loaded processors were not considered
since under-loaded processors were of importance in a data center when
considering the VM scheduling strategy.
Marzolla et al (2011) concentrates about the novel opportunities for
achieving energy savings in cloud: Cloud systems make use of virtualization
techniques to be able to allocate computing sources on demand, as well as
modern Virtual Machine (VM) monitors let live migration involving running
VMs. Therefore, energy conservation is possible through server
consolidations which are alive, moving VM instances clear of lightly loaded
computing nodes to become empty there by enabling it to be switched to low-
power mode. QoS according to cloud host character was not concentrated
despite consolidation of VMs as well as live migration. The energy
conservation that is achieved helps customer to provide more application as
well as process running with a data center. The data center turns into
environmentally sustainable as well as a better approach for the application
handling with respect to energy was analyzed. The reliable nodes consider
alive hosts which are inherently unreliable as well since they are distributed in
nature.
Gao et al (2013) proposed an integrated management solution
which takes attributes of both virtual machine resizing and also server
consolidation to obtain energy proficiency and good quality of program in
virtualized data centers. Virtual Machine resizing for the hosts being alive is
an important criteria to be looked into. A novelty of the solution would be to
integrate linear coding, ant colony optimisation, and also control idea
techniques. The author proposes data center management techniques and also
how SLAV may be reduced by simply establishing a profit in energy personal
savings. The QoS according to SLAV metrics supplied by the impair provider
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really needs to be analyzed. The reliability constraints were examined in
another paper.
Viswanathan et al (2011) offered and evaluated a fresh application-
centric energy-aware strategy for VM allowance that is aimed at maximising
the actual resource use and power efficiency by way of VM combination
while satisfying reliability guarantees. To make this happen, they designed an
empirical model for the average power consumption and also execution time
based on measurements by extensive execution of normal HPC workload
standards (all possible allocations based on number and sort of VMs), and
created an algorithm to look for the best VM allowance that defines an
optimisation goal such as minimisation of energy consumption and/or
execution. QoS was considered based on application as well as types. Their
future research attempts are designed for i) employing machine learning
techniques to extract on-the-fly any model out from the sub-system use data
gathered from off-line experiments employing benchmarks together from true
applications managing on VMs and also ii) compare our suggested solution
against a few of the real time workloads through implementing these on data
centers. Our planned future exploration efforts include, i) extending the
solution to know and service heterogeneous server hardware, which is
necessary for evaluation on a real test bed and also ii) integrating the
suggested solution with schemes for autonomic arctic management in
instrumented data centers.
Shi & Hong (2011) motivated with the limit within the Power
Utilization effectiveness (PUE) with the data centers, the potential benefit of
the combination, and the actual impetus associated with achieving maximum
return on investment (ROI) within the cloud computing market, they looked
into VM placement in the data center, formulate the multi-level generalised
assignment problem (MGAP) intended for maximising the actual profit
48
beneath service levels agreement as well as the power price range constraint
using the model of an virtualized files center, and fix it with a first-fit
heuristic. SLA and power price range was concentrated in this paper while we
in this chapter wished to address the actual SLA depending on reliability
limitations. We evolved with a statistical distributed real world scenario for
studying this depending on the proposed algorithms. Power utilization was
dealt here but VM administration concerning a reliable cloud framework is
necessary which is focused in Feller et al (2012). The service level agreement
for reliable nodes has to be concentrated.
Feller et al (2012) in their paper have evaluated the vitality
management mechanisms of the unique holistic energy-aware VM
administration framework called Snooze. Specially, Snooze ships with
integrated VM monitoring and live migration assistance. Moreover, it utilizes
a resource (RAM, CPU, memory and also network) utilization estimation
engine, detects clog and under-load predicaments and performs event-based
VM relocation and periodic consolidation. Snooze is the rest system
implementing the particular server consolidation algorithm that has been
previously only considered by simulation. Finally, once energy benefits are
enabled, idle servers are generally automatically transitioned right into a
lower power point out (e.g. suspend) and woken standing on demand.
Machine learning techniques are also implemented in chapter 5 in this thesis.
Now in this chapter we plan to develop a cloud character model to evaluate
the reliability of the real time cloud environment. Cloud environment largely
decides the VM consolidation and how it helps to reduce the electricity
consumption.
Hu et al (2012) focussed on traditional Infrastructure-as-a-Service
promotions provide customers with many fixed-size virtual appliance (VM)
instances having resource allocations that hopefully will meet application
49
called for. VM depending on the application workload was analyzed in their
paper. The Host quality was predicted using machine learning techniques
since cloud characteristics was on the provider energy perspective. The idle
nodes to be brought to a sleep mode and the reliable nodes are used for better
VM consolidation.
Feller & Morin (2012) in their paper have introduced a new
scalable, autonomic, and also energy-aware VM managing framework called
Rest. Unlike the active cloud management frameworks, Rest utilizes a self-
organising hierarchical buildings and distributes the actual VM management
tasks across multiple class managers (GMs), with each manager having a
subset of nodes (local controllers (LCs)). Also, fault tolerance is provided in
any respect levels of the actual hierarchy. Consequently, the systems have the
ability to self-heal and continue its operation irrespective of system
component failures. Snooze was highlighted within this paper. But the
important challenge of the ability and QoS analysis depends on the cloud
provider character and its capacity. So it is significant to study the actual
cloud character to help analyze the VM consolidation strategies to be used.
Qi-yi & Ting-lei (2010) based on customer needs, the authors
conducted research for scheduling models. They term cloud computing is
promoted from the business in lieu of academic which usually determines its
focus on user software. Different customers have unique QoS needs. So
according to the given deadline and budget, their article conducts research on
scheduling model from the user's perspective. SLA violation was an important
metric to be handled in a data center. This was addressed in their paper in a
trust perspective and how a reliable cloud environment responded was also
taken into account.
Liu et al (2011) while using prosperity of Cluster Computing, cloud
computing, Grid Computing, and some other distributed high performance
50
computing systems, Internet services requests become an increasing number
of diverse. The large variety of services in addition different Quality of
Assistance (QoA) considerations such as provisioning and monitoring make it
challenging to development effective algorithms to fulfil the entire service
demands, especially for distributed systems. In addition, energy consumption
issue attracts an increasing number of concerns. In this paper, they study a
whole new energy efficient, profit as well as penalty conscious allocation as
well as scheduling tactic for sent out data centers in a multi-electricity-market
atmosphere. Our tactic efficiently manages computing resources to reduce the
running and transferring energy money cost within the electricity value
varying atmosphere. Our extensive experimental final results show the new
approach can certainly significantly reduce the strength consumption money
cost as well as achieve larger system's maintained profit. The discussed
literature has been tabulated in Table 2.3 and compared with respect to key
parameters.
Table 2.3 Literature review of Reliability and Virtualized Cloud Environment in IaaS
Author
IsReliability taken into account?
If QoS taken into account, What was the basic idea on which it
was implemented?
QoS analysis in Datacenter?
(Imada et al 2009) Yes Statistical Analysis of Host Characteristics
Yes
(Li & Huang 2010) No Cloud Environment No(Mei et al 2011) No VM characteristics Yes(Marzolla et al 2011)
No Host Characteristics No
(Gao et al 2013) Yes VM characteristics Yes(Viswanathan et al 2011)
No Workload Analysis No
(Wang et al 2011a) Yes Host Characteristics Yes
51
Table 2.3 (Continued)
(Shi & Hong 2011) Yes Cloud Environment Yes(Feller et al 2012) No Cloud Environment No(Hu et al 2012) Yes Cloud Environment Yes(Feller & Morin 2012)
No Cloud Environment No
(Qi-yi& Ting-lei2010)
No Cloud Environment Yes
(Liu et al 2011) No Cloud Environment Yes
The host character is studied based on reliability for the VM
component and its consolidation.
We found we needed to address three things,
1. Since Energy consumption was the main consideration, we
analyzed the various Host Oversubscription and VM selection
algorithms based on the legacy algorithms and proposed two
algorithms which were efficient.
2. Since from the above available and proposed algorithms we
found that VM migration and SLA violation were important
characters which Energy Consumption was dealing with. We
have proposed an Energy Curve model by the time relationship
between SLA violation and VM migration. Host Analysis
similar to VM analysis and VM selection algorithms were not
found in various literatures.
3. The Host Characteristics’ analysis in various literatures was not
dealt with so we proposed a Statistical Modeling of Real World
Cloud Environment for Reliability where host characteristics’
were studied based on the proposed Energy model.
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2.5 CLOUDSIM TOOLKIT
IT companies who are willing to offer some services in the Cloud
can use a simulation-based approach to perform some benchmarking
experiments with the services to be offered in dependable, scalable,
repeatable, and controllable environments before real deployment in the
Cloud. Therefore, they can test their services in a controlled environment free
of cost, and through a number of iterations, with less effort and time. Also, by
using simulation, they can carry out different experiments and scenarios to
identify the performance bottlenecks of resources and develop provisioning
techniques before real deployment in commercial Clouds. Therefore,
CloudSim has been developed to fulfil these requirements by simulating and
extensible Clouds.
2.5.1 Architecture of CloudSim
CloudSim can be defined as “a new and extensible simulation
framework that allows seamless modeling, simulation, and experimentation
of emerging Cloud Computing infrastructure and application services”.
Initially, the framework of CloudSim consists of multiple layers starting
from the lowest layer of SimJava up to the top layer of User Code. At the
lowest layer, SimJava provides the base engine of the simulation that
supports the implementation of core functionalities essential for the higher-
level frameworks of the simulation, like queuing and processing of events;
formation of system components (services, hosts, brokers, VMs);
interaction between these components, and administration of the simulation
clock. On top of that layer is the GridSim layer which supports high-level
and fundamental Grid components, such as networks, resources, data sets,
and information services. Then, the CloudSim layer forms the next level of
the architecture that extends the core functionalities of the GridSim layer.
53
This layer supports Cloud-based data center environments, including VMs,
memory, storage and bandwidth. Also, this layer can manage instantiating
and simultaneously implementing a large scale Cloud infrastructure
composed of thousands of system components (VMs, hosts, data centers,
and application). Finally, User Code is the top-most layer of the simulation
toolkit, which reveals the configuration of functionality for the system
components, such as the number and specification of hosts and the
scheduling policies of the broker. At this layer, a developer can model and
perform robust experiments and scenarios of Cloud environments based on
custom policies and configurations already supported by the CloudSim, in
order to evaluate and tackle some Cloud issues like the complexities of
Cloud infrastructure and application.
2.5.2 Usability
In order to use the CloudSim toolkit, users need to have a basic
background in Java programming language because it is written in Java. Also,
it requires users to write some code to use the components from its library in
order to simulate the desired scenarios. Therefore, it is not just about setting
the parameters, running the program, and collecting the results, but it also
requires a deep understanding of how the program works. In addition, a little
knowledge about Integrated Development Environments (IDEs), like
NetBeans or Eclipse, will be useful to ease installing the toolkit and the
development of scenarios. Furthermore, CloudSim provides a library that can
be used to build a ready-to-use solution, such as CloudAnalyst which is built
on top of CloudSim, to offer an easy to use graphical user interface.
54
2.5.3 Capabilities
CloudSim has some compelling features and capabilities that can
be extended to model a custom Cloud Computing environment. According to
(Calheiros et al 2011), CloudSim can offer flexibility and applicability and
with less time and effort to support initial performance testing. It can support
simulating, from small-scale up to large-scale cloud environments containing
data centers, with little or almost no overheads in terms of and consumption
of memory. Also, it has an engine that allows the creation of multiple services
that can be independently managed on a single node of the data center.
Moreover, it supports, in addition to other features, energy-awareness
provisioning techniques at resource, VM, and application level, such as VM
allocation and DVFS. For managing the energy conscious techniques in a data
center, CloudSim architecture contains the key components
CloudCoordinator, Sensor, and VMM. The Sensor component, which is
attached to every host, is used by the CloudCoordinator to monitor particular
performance parameters, like energy consumption and resource. Thus,
through the attached Sensors, CloudCoordinator passes real-time information,
like load conditions and processing share, of the active VMs to the VMM.
Then, VMM uses this information to perform the appropriate application of
DVFS and resizing of VMs. Also, according to VMs’ policy and current of
resources, CloudCoordinator constantly issues VM migration commands and
changes the power state of nodes to adapt the allocation of VMs. Cloud
computing is a term used to describe a style of computing for next generation
service centers where massively scalable service-oriented IT-related
capabilities are dynamically delivered to multiple external customers. A cloud
may host a variety of services, that include Web applications (i.e. Software as
a Service (SaaS)), legacy client-server applications, and platforms (i.e.
Platform as a Service (PaaS) , infrastructure (i.e. Infrastructure as a Service
(IaaS) , and information services.
55
2.5.4 Limitations
CloudSim is a powerful tool for modeling and simulating Cloud
computing, but it has some limitations. Firstly, it is not a ready-to-use tool
that would just require setting parameters only. Actually, it does require
writing some Java code to use its library, as discussed earlier. Also, the
capabilities of CloudSim are sometimes limited and require some extensions.
For instance, CloudAnalyst has been developed as an extension of CloudSim
capabilities to offer a separation of the simulation experimentation exercise
from the technicalities of programming, using the library in order to ease
modeling by simply focusing on the complexity of the simulated scenario,
without spending much effort and time on the language in which the simulator
is interpreted. Cloud Computing has been the focus in the industry and other
research organization. There are various simulation tools that are being
introduced in this paradigm. These Simulation Tools help to learn the Cloud
Computing paradigm in the Distributed Computing technology.
Table 2.4 Comparison of Clouds and other Open Source tools
Properties CloudSim Globus Aneka AlchemiArchitecture Layered Layered
andModular
Utility Model and Layered
Hierarchic andLayered
Platform Unix, .Net, Windows
Unix Unix,Windows, Mac, .Net
Unix, MacWindows, .Net
Language C , C# C , Java C , C# C#, .NetService and Simulation Modeling
VirtualMachineModeling,CloudInfrastructure
LowLevelServices
CloudInfrastructure And Services
CloudInfrastructure and Services
56
A comparison of the available open source toolkits for Cloud
Computing is shown in Table 2.4. It shows that CloudSim has been the only
toolkit to be completely layered and VM (virtual machine) modeling
incorporated, this makes it our choice for simulation.
Table 2.5 CloudSim Compared to the other academic simulators
Label CloudSim GreenCloud MDCSim
Platform SimJava Ns2 CSIM
Language/Script Java C++/OTcl C++/Java
Availability Open source Open source Commercial
Simulation time Seconds Tens of minutes Seconds
Graphical support Limited (CloudAnalyst)
Limited(Networkanimator) None
Application models
Computation, Data transfer
Computation, Data transfer, and Executiondeadline
Computation
Communication models
Limited Full Limited
Energy models Available Precise (servers + network)
Rough (servers only)
Power saving modes
DVFS, power models
DVFS, DNS, and both None
Table 2.5 shows toolkits available for academic purposes which
have been compared with CloudSim. Toolkits help in learning the real world
scenario since data centers are very expensive to provision and maintain. The
research arena has to be on par with the industry standards and hence Cloud
Toolkits help to a greater extent to learn a paradigm in an economical manner.
57
2.6 SUMMARY
The literature helped us to find an unexplored area to work on. Our
research from the literature found the need of workflow model to define the
flow and complexity of the jobs in a cloud data center. The Cloud data center
incurred major energy consumption from the ICT carbon footprint. Many
papers concentrated on the VM consolidation. Handling the over-loaded and
under-loaded servers is an important challenge. The servers and their status
prediction have to be concentrated. The status of hosts is important and the
VM consolidation in these processors is of utmost importance. Most of the
papers we found in the literature concentrated on the Energy aware VM
consolidation which mainly focuses on server resources management in a
virtualized environment. But proper VM selection and Host overloading
prediction were not concentrated. The VM consolidation and its effect on the
Overloaded servers have been concentrated in our research.
The real time traces were not used, instead analytical models took
most of the research power models. The host status prediction was not done
and the VMs were consolidated from the overloaded servers. The underloaded
servers were either used or emptied for our simulations in our research. The
server VM consolidation helped in better analysis of the machines available in
cloud data center. We have developed algorithms for better VM selection.
These algorithms were compared with the proposed machine learning
techniques to predict the host status if it was overloading or not. Many papers
handled machine in a reliable environment where the machines are either
alive or not alive and alive machines were only considered for most of the
research in VM consolidation and energy consumption which can be termed a
binary propertied environment. The data center or a distributed system the
main property is its inherent unreliability which was not considered in most of
the literature. We in our work have specifically introduced performance
58
measures for reliability and the proposed algorithms have been compared for
reliability constraints that are available in a cloud data center. A statistical
model for reliability has been concentrated since a statistical property will
give a wider perspective of the machines to be handled based on its
probability and throughput instead of a binary propertied analysis where
machines which are in a pseudo active state. Hosts can be states where it can
be alive and about to shutdown or some cases it may be shutdown and about
to be active based on the VMs. The VM consolidation helped to analyze the
hosts in a data center. The research was done using real time workload traces
and real world scenarios for the repeatability of experiments in simulations
exhibited here.