Thesis: Energy Optimization and Management in Cloud Computing Systems

This thesis, submitted for the NIT6042 (Thesis 2) unit, investigates energy optimization and management strategies within cloud computing environments. The research addresses the increasing energy consumption of cloud data centers, driven by the growth of virtual machines and the demand for scalable resources. The study explores the challenges of energy consumption, including high operating costs and the need for green computing. The research examines various approaches and mechanisms for optimizing energy consumption, such as dynamic voltage and frequency scaling and virtual machine consolidation. The thesis includes a literature review, research methodology, experimental results using the CloudSim simulator, and a discussion of the findings. The study aims to identify major challenges, investigate the impact of energy consumption, understand optimization approaches, and propose recommendations for addressing the energy consumption issue in cloud computing. The conclusion summarizes the research, discusses limitations, and suggests further scope for future studies. The thesis emphasizes the importance of energy-efficient solutions to balance performance and sustainability in the cloud environment.

Energy Optimization and Management in
Cloud Computing
Full Name
Student ID
Submitted for unit NIT6042 (Thesis 2)
Date

Abstract
Cloud computing typically supports scalable resource utilization, virtualization and offers
various services including IaaS (Infrastructure as a Service), PaaS (Platform as a Service) and
SaaS (Software as a Service). In spite of the tremendous advantages of cloud computing, it
still has certain shortcomings. For instance, both public and private clouds have relatively
high operating costs, which is a major disadvantage of cloud computing. Apart from that, the
era of green computing essentially mandates the utilization of limited energy resources.
However, the increasing number of the data centers demands more computational power,
which in turn raises the demand for low energy and low cost. The primary benefit of cloud
computing services is that it offers constant access and availability to multiple services
including networking, data storage, and computational power and so on. However, since the
cloud-computing paradigm has grown to be popular across the world, the amount of power
consumption has also increased dramatically.
Keywords: cloud computing, energy efficiency, energy optimization, cloud data center,
energy consumption

Table of Contents
Abstract......................................................................................................................................2
1. Introduction............................................................................................................................5
1.1 Background of the Study..................................................................................................5
1.2 Research Aim...................................................................................................................6
1.3 Research Objectives.........................................................................................................6
1.4 Research Questions..........................................................................................................7
1.5 Problem Statement...........................................................................................................7
1.6 Rationale of the Study......................................................................................................7
1.7 Structure of the Thesis.....................................................................................................8
2. Literature Review.................................................................................................................10
2.1 Energy Consumption Problem in the Cloud..................................................................11
2.2 Cloud Energy Aware Scheduling...................................................................................12
2.2 Data Center Network Topologies...................................................................................13
2.3 Cloud Data Centers........................................................................................................15
2.4 Adoption of energy efficient solutions...........................................................................18
2.5 Cloud Federation............................................................................................................19
3. Research Methodology.........................................................................................................22
3.1 Research Problem...........................................................................................................22
1.4 Research Questions........................................................................................................23
3.3 Time Horizon.................................................................................................................23
4. Experiments and Results......................................................................................................25

4.1 Presentation of the results..............................................................................................25
4.1.1 The CloudSim Simulator.........................................................................................25
4.1.2 The CloudSim architecture.....................................................................................26
4.1.3 The SoS (System of Systems) functional architecture............................................28
4.2 System Model.................................................................................................................29
4.3 Simulation tool...............................................................................................................29
4.4 Discussion......................................................................................................................32
5. Conclusion............................................................................................................................35
5.1 Limitation of the Study..................................................................................................35
5.5 Further Scope of the Study.............................................................................................36
References................................................................................................................................36

1. Introduction
The tremendous growth in the technological front, starting from server virtualization
to multi-tenant cloud models, the increasing number of cloud data centers is consuming an
enormous amount of power. Cloud computing essentially solves some serious problem such
as elimination of chronic costs, management of inefficiencies and dismal CPU utilization
levels. Large farms all across the world are rapidly deploying the cloud strategies to get rid of
the burden of maintaining the physical servers (Helleret al., 2015). However, the cramming
number of data centers and virtual machines (VMs) are creating major business data traffic
on the network. As a result, the dramatic increase in power consumption is leading to several
environmental as well as operational problems (higher power costs). In addition to that, there
are huge concerns in terms of the ability of the power grids to support the escalating energy
consumption requirements along with the elastic demand requirements of the cloud
environment.
1.1 Background of the Study
The energy consumption by the cloud data centers increasing at an exponential rate is
becoming a huge challenge for the cloud service providers. The cloud users are no more tied
to the physical infrastructure as their applications, software and data everything is access
through virtual services. Thus, the rapid migration of application platforms onto the cloud
environments is resulting into and increased demand for resources by the data centers. The
severe heat dissipated by the integrated servers and the associated cloud computing
equipment is automatically boosting the emission of harmful gases causing greenhouse effect
and increasing the amount of carbon footprints (Jalaliet al., 2016). In addition to that, the
high degree of power consumption is also responsible of entailing phenomena such as floods,
droughts and abnormal rise in the temperature. However, it is obvious that energy savings

would compromise the performance of the computing servers. According to Boruet al.
(2015), performance loss due to energy savings is a major and recurrent issue, which needs to
be addressed. In this context, an energy efficient model is required for optimizing and
managing the energy consumption in the cloud environment. There are several existing
studies in this area and the present study aims at unveiling the primary aspects related to the
optimization, conservation and management of could data center energy.
1.2 Research Aim
The study aims to investigate the energy efficient approaches and techniques
deployed in the cloud in order to address the exponential increase in power consumption by
the data centers to manage the enormous amount of data traffic on the Internet. a significant
number of research works have been undertaken in the same area that try to examine the
ways to reduce the amount of power consumption of cloud data center servers. Several works
have addressed the placement of virtual machines (VMP) that majorly concerns with the
performance and energy aspects. Thus, the primary focus of this study is to examine and
study the approaches towards energy optimization and management in cloud computing.
1.3 Research Objectives
The objectives for the present study are formulated below:
 To identify the major challenges of cloud energy consumption
 To investigate the impact of energy consumption in the cloud
 To understand the various approaches and mechanism employed for
optimizing and managing energy consumption in cloud computing
 To propose recommendations from secondary sources to address the energy
consumption issue

1.4 Research Questions
The research questions for the present study are formulated from the research
objectives as identified in the above section:
 What are the major challenges of cloud energy consumption?
 What is the impact of energy consumption in the cloud?
 What are the various approaches and mechanisms employed for optimizing,
and managing energy consumption in cloud computing?
 What solutions are there to address the energy consumption issue?
1.5 Problem Statement
With the growing demand of cloud services across the globe, the growth of data
centers is accelerated, which in turn led to this problem of massive energy consumption. The
virtualized data centers and lowering the utilization of physical servers is the prime reason of
energy inefficiency. According to Dayarathna, Wen and Fan (2016), the issue related to the
high level of power consumption can be manifested into two broad aspects such as the data
center and the processing area. Due to the technological evolution, the wide and more
frequent use of computers have largely expanded the scope of data centers. As a result, the
number of servers is increasing exponentially day by day, resulting in excessively
overwhelming power consumption. The low level of server utilization also leads to the
wastage of energy.
1.6 Rationale of the Study
The cloud service providers need to deal with a major challenge of managing and
optimizing the energy consumption of virtual computers and data centers in the cloud
environment. The various challenges in this aspect encompass the operational challenges,
energy consumption measurement and management challenges. In this context, several

mechanisms and approaches have been discussed in previous research works to deal with the
real issue of massive power consumption by cloud services (Hintemann and Clausen, 2016).
Today, the world is changing at a fast pace and people are solely reliant on the Internet and
advanced technologies associated with it. In this situation, the use of virtualized cloud
platforms is becoming quite necessary for everyday business. Thus, it is the high time to
carry out an in depth analysis of the energy consumption management and optimization in
order to enable effective and efficient industrial development and thereby, lead the
humankind towards the ideal age of information technology.
1.7 Structure of the Thesis
The paper is divided into different individual sections or chapters, each of which
properly focuses on the different aspects and factors of the study in a detailed manner.
Therefore, the content of the thesis will be segregated based on these chapters.
Introduction: The introduction section describes the basic aims, objectives and
purpose of the undertaking the study on the effectiveness of the energy optimization and
management of cloud computing data centers.
Literature review: The literature review section will focus on the basic concepts and
theories on the particular area of study. In order to serve this purpose, various literature
sources will be collected in order to analyze and gather useful and relevant data and
information regarding the need and mechanisms of energy management and optimization in
the cloud data centers.
Methodology: The methodology section will describe the basic requirement of
selecting a method and the chosen research approach to be able to conduct this study in an
appropriate manner so that a successful outcome of the research can be achieved.

Experiments & results: Following the methodology as chosen in the previous
chapter, this chapter will explain the results and findings from executing the research. To be
more precise, the method will be followed and the derived outcomes will be analyzed and
discussed in this particular section.
Conclusion: The conclusion section will briefly summarize the entire research as and
how it has been carried out. In other words, this section will represent an overall view of the
thorough study, which includes the discussion of the results and findings as well. Apart from
that, it will look into the limitation of the present study as well as the future scope of
extending the research work.

2. Literature Review
The literature review section focuses on the different concepts and ideas related to
cloud computing and its energy consumption behaviors. In addition to that, it also looks into
the different energy optimization and management techniques with an aim to identify the
effective and most appropriate solutions. Several past research works have been executed in
this particular area of study, which has uncovered some truly interesting mechanisms to solve
the problem of massive energy consumption by the data centers in cloud computing. Thus,
these studies have been focused in this section as follows:
According to Ronget al. (2016), the primary benefit of cloud computing services is
that it offers constant access and availability to multiple services including networking, data
storage, and computational power and so on. However, since the cloud-computing paradigm
has grown to be popular across the world, the amount of power consumption has also
increased dramatically. In this context, Silvaet al. (2017) suggests that the cloud companies
have implemented some fine mechanisms to deal with energy costs and reduce the carbon
footprint. However, this approaches towards energy saving may lead to the loss of overall
cloud performance. Therefore, it is necessary to talk about solutions that do not affect the
performance metrics and generates equal or more profits. Mastelicet al. (2015) argues that it
may be possible by scheduling based on dynamic speed scaling, which in turn may use
machine learning and load prediction algorithms.
On the other hand, Jalaliet al. (2016) identified the virtual machine consolidation as a
process that comprises four individual decision making activities such as virtual machine for
migrating selection, physical node overload and under-load and target node for the migrated
virtual machine selection. Similarly, Hintemann and Clausen (2016) has proposed separate
adaptive heuristic algorithms that apply statistical analysis for estimating the threshold of

overload of CPU utilization based on the historical data of virtual machines. Ronget al.
(2016) has proposed the CloudSim framework for virtual machine consolidation.
2.1 Energy Consumption Problem in the Cloud
There are two techniques for saving the energy consumed by the data center hardware
and servers. DVFS (dynamic voltage or frequency scaling) is one method and the other one is
by shutting down the idle servers. To be more precise, in the method of dynamic voltage and
frequency scaling, energy is typically saved by means of adjusting the operating clock for the
purpose of scaling down the supply voltages that is based in adaptive algorithm. The DVFS
approach effectively reduces potential power consumption even if it is dependent on the
hardware components for executing the scaling job. On the other hand, shutting down the
servers that are not in use effectively conserve more amount energy as in a highly dynamic
environment, turning off the system can potentially create high overhead that in turn leads to
a strong degradation performance. Hence, the application of the energy optimization
techniques is highly useful in the calculation of the exact amount of power or energy
consumption by the different components associated with the cloud data centers.
According to Dayarathna, Wen and Fan (2016), it is high time for the industrial and
the governmental institutions to address the serious problems of this massive and explosive
increase in the amount of energy consumption by the cloud data centers. Hence, the
development of highly energy efficient methods and techniques in cloud computing is a
potentially serious issue in this area of cloud computing. However, according to Benkhelifa et
al. (2015), the advancement in the development of the energy efficient cloud based solutions
are highly dependent on multiple key technologies. In this context, Hintemann and Clausen
(2016) has suggested that the platform level of application highly demands the energy
efficient mediums whereas; the hypervisor level requires energy efficient scheduling
algorithms along with storage and memory systems and resource management policies.