A SURVEY: TO HARNESS AN EFFICIENT ENERGY IN CLOUD COMPUTING

International Journal of UbiComp (IJU), Vol.6, No.3, July 2015
DOI:10.5121/iju.2015.6301 01
A SURVEY: TO HARNESS AN EFFICIENT
ENERGY IN CLOUD COMPUTING
Malathi.P1
, Arumugam.S2
1
M.E.Scholar, Department of Computer Science & Engineering, Nandha Engineering
College, Erode-638052, Tamil Nadu, India
2
Professor, Department of Computer Science & Engineering, Nandha Engineering
College, Erode-638052, Tamil Nadu, India
ABSTRACT
Cloud computing affords huge potential for dynamism, flexibility and cost-effective IT operations. Cloud
computing requires many tasks to be executed by the provided resources to achieve good performance,
shortest response time and high utilization of resources. To achieve these challenges there is a need to
develop a new energy aware scheduling algorithm that outperform appropriate allocation map of task to
optimize energy consumption. This study accomplished with all the existing techniques mainly focus on
reducing energy consumption.
KEYWORDS
Cloud computing, Energy consumption, Virtualization, renewable energy, Virtual machine
1. INTRODUCTION
With the development of high speed networks, there is an alarming rise in its usage comprised of
Web queries a day and thousands of e-commerce transactions. A large scale data centers handle
this ever increasing demand by consolidating hundreds and thousands of servers with other
infrastructure such as cooling, network systems and storage. The development of this
commercialization is named as cloud computing. Clouds are sky rocketing virtualized data
centers and applications offered as services on a subscription basis. The characteristics exhibited
by Clouds are shown in Figure 1
Figure 1.characteristics of cloud computing.
In recent years, a great amount of research on cloud computing to offer low power systems, since
serious issue on the sustainability of current technologies and practices. To address the various
issues such as resource management in both software and hardware levels to reduce energy
consumption. Overload and under load of system is also one of the phenomenon for energy loss

2
[5]. According to [15], the recent studies reveal that the ideal system consumes 70% of energy.
The virtualization technology is to resolve those problems [4]. The performance optimization,
energy saving, load balancing and on demand resource allocation can be achieved by using good
resource management scheme [21]. There is a relationship between energy consumption and
resource utilization in cloud computing. If the energy consumption increases tremendously which
directly affect the performance of the cloud in a while increases the cost.
In this survey paper, energy harnessing techniques are discussed. The remaining paper is
organized as follows: Section 2 presents overview, Section 3 presents the literature review of the
existing methods of energy efficient techniques, and Section 4 presents conclusions.
2. OVERVIEW
2.1 Energy Consumption Problem
The major problem in cloud is energy sprawl. This is due to two reasons. One is manual faults
like improper scheduling and work overload [5, 10] and other is due to hike in monetary cost of
electricity [13]. The manual faults can be resolved by using monitoring and virtualization [12].
Second problem can be resolved by harnessing the renewable energy [9]. The two levels in
energy consumption problem are shown in Figure 2
Figure2. Levels in energy consumption
2.1.1 Monitoring
It revealed that you cannot improve what you do not measure. Power metering is essential to
construct power models that knowledge the system to know the energy consumed by a particular
device, and how it can be reduced [12]. The virtual machine power metering follows the three
steps such as: information collection, modelling and estimation in a while the unified efficiency
of a data centers are measured and improve its performance per-watt.
2.1.2 Virtualization
Virtualization technology provides the flexible resource provisioning and migration of machine
state [5]. Due to hot spot, excess space capacity and load imbalance which migrates the machine.
Virtualization enables the consolidation, load balancing and hot spot mitigation [4]. It allocate
data center resources dynamically based on application demands and support green computing by
optimizing number of servers in use.

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2.1.3 Renewable energy
Cloud computing uses the brown energy which leads to hike in electricity bill and increase energy
consumption. Harnessing renewable energy is used to address many issues like carbon emission,
greenhouse gas emission and energy consumption [8], [9]. Renewable energy sources have less
carbon emission rate rather than fossil fuels.
3. EXISTING ENERGY EFFICIENT MODELS IN THE CLOUD
3.1. Workload Consolidation
Srikantaiah et al. [22] have analyzed the relationship between resource and energy consumption.
Moreover, the performances of workload consolidation were evaluated. The authors used two
main features including, disk usage and CPU cycles in a bin-packing problem for task
consolidation. Based on Pareto frontier algorithm the authors merged tasks and balance energy
consumption by computing optimal points. The proposed technique was to compute optimal
points from profiling data. An energy aware resource allocation mechanism used profiling step.
The Euclidean distance measure between the current and the ideal point for each server is
computed by profiling step.
3.2. Virtual Machine Power Metering
ChonglinGu et al. [12] have proposed virtual machine power metering which reduces the power
consumption of data centers. There are three steps in virtual power metering such as information
collection, modeling, and estimation. Power measured for the servers by deploying external meter
and internal meter. It applies two methods as white box method and black box method. The black
box method is used widely. The power meter is needed for virtual machine service billing, power
budgeting, power saving scheduling. The unified efficiency of a data centers are measured and
improve its performance per-watt.
3.3. Energy Conservation Techniques
Mehiar Dabbagh et al. [22] have analysed power management techniques that exploit
virtualization technology to save energy. Prediction, consolidation and over commitment
techniques are deployed. Workload prediction is used to turn off the unused physical machines to
save energy. The overloaded machines are consolidated by virtual machine placement and
workload consolidation. Resource over commitment is to reserve the resources in overly manner.
It achieves the green energy.
3.4. Virtual Machine Scheduling
Dong Jiankang et al. [20] leveraged virtual machine scheduling to solve the combination of bin
packing problem and quadratic assignment problem. It employs the two stage heuristic algorithm
with virtual machine placement and migration. Virtual machine placement is to meet the physical
capacity and network bandwidth. Virtual machine migration to minimize the migration costs to
optimize the network maximum link utilization to reduce the energy consumption. c++ is used to
develop the algorithm. Compared to random algorithm the energy consumption is low.
3.5. Renewable energy – aware migration
UttamMandal et al. [8] have developed the renewable energy aware cloud service and virtual
machine migration to relocate energy demand using resource allocation technique. This technique

4
is used to migrate from one data center to another if more renewable energy is available in
destination. A virtual machine is migrated to more green energy powered data center. Renewable
energy aware migration tries to replace brown energy with green energy.
3.6. Tasks Oriented Energy-Aware Scheduling
Xiaomin Zhu et al. [15] have proposed energy aware scheduling and rolling horizon is named as
EARH algorithm. Algorithm employs a rolling horizon optimization policy integrated with
energy aware scheduling. Rolling horizon has a real time controller and virtual machine controller
to hold new task along with the waiting task. Rolling horizon adjusts the tasks. After that the
energy aware scheduling algorithm performs the resource scale up and scale down. It was
implemented by cloudsim toolkit. The experimental results indicate that scheduling quality is
improved and it saves the energy
3.7. Ant Colony System
Fahimeh Farahnakian et al. [19] have proposed ant colony based virtual machine consolidation
which uses artificial ants to consolidate virtual machine into a reduced number of physical
machines based on the current resource requests and ants work in parallel to build virtual machine
migration. It composed with global and local agent. Global agent consolidates virtual machine
into reduced number of physical machine. Local agent detects physical machine status. It was
implemented by cloudsim toolkit. Result shows that this technique reduces the energy
consumption up to 53.4% than the dynamic virtual machine consolidation.
3.8. Dynamic Resource Allocation
Zhen Xiao et al. [4] have proposed virtualization technology. Virtualization enables the
consolidation, load balancing and hot spot mitigation. Virtualization technology provides the
flexible resource provisioning and migration of machine state. Due to hot spot, excess space
capacity and load imbalance which migrates the machine. The SKEWNESSS algorithm is used to
measure the uneven utilization of server. Algorithm is simulated by trace driven simulation. It
achieves green computing.
3.9. Energy-Aware Scheduling
Li Hongyou et al. [6] have marshalled the workload aware consolidation technique. Energy-aware
Scheduling algorithm and the Energy- aware Live Migration algorithms are used. It accords the
users to access the multiple resources concurrently in cloud data center. Algorithms audit the
problem of consolidating heterogeneous workloads. Simulation results show that both algorithms
convince the promising energy saving capability.
3.10. Energy-Aware Task Consolidation
Ching-Hsien Hsu et al. [11] have developed a technique that reduces energy consumption is
energy-aware task consolidation (ETC) technique. ETC restricts CPU use below a specified peak
threshold. Task consolidation is the major work of ETC. When a task migrates to other virtual
clusters considered as network latency by energy cost model. Compared ETC with MAXUTIL
for evaluation. MAXUTIL is a greedy algorithm that aspires to maximize cloud computing
resources. The simulation result shows that 17% improvement over MAXUTIL.

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Table 1. Comparison of the existing methods
4. CONCLUSIONS
In this survey, we focused on key parameters of energy consumption problem. The existing
models are explained. From the survey it is found that some of the models are yet to be
implemented. The power metering and virtualization technologies are useful to utilize the energy
in efficient way. Cloud computing much more energy efficient, there are still many technological
solutions and enhanced green cloud framework is needed to achieve energy efficient cloud
computing in reality.
Techniques and
Algorithms
Hardware / Datasets Tools Parameter Analysis
Energy Conservation
Techniques
No specific environmental
set up.
Map reduce CPU utilization
Memory utilization
Virtual Machine Power
Metering
A Power Edge blade
servers with Intel E5620
CPU and 24 GB of RAM.
Oprofile, pfmon,
perf-suite
Error rate
Workload Consolidation Data center data sets maple CPU cycles disk usage
Virtualization technology,
Concept of SKEWNESS.
A group of 30 Dell Power
Edge blade servers with
Intel E5620 CPU and 24
GB of RAM. The server
runs on Xen-3.3 and Linux
2.6.18
Perfmon Watt-meter
Renewable energy –
aware migration
Not mentioned Not mentioned Not mentioned
Energy aware task
consolidation(ETC)
Data center data sets Cloud sim toolkit Number of nodes
Workloads
Consolidation of virtual
machine
Data center data sets Ad-hoc
simulator
Number of active
servers,
Migrations per hour,
power
EARH scheduling
algorithm
set up.
Cloud sim
Toolkit
Task count,
Task arrival rate,
Task deadline
Dynamic consolidation of
virtual machines
Hadoop testbed Cloudsim toolkit Energy consumption ,
Number of virtual
machine migration
Virtual machine
scheduling algorithm
.
set up.
C++ Energy consumption,
Total communication
Traffic,
Maximum link
utilization

6
REFERENCES
[1] Bernadette Addis, DaniloArdagna, Barbara Panicucci, Mark S. Squillante and Li Zhang, ―A
Hierarchical Approach for the Resource Management of Very Large Cloud Platforms,‖ IEEE
Transactions on Dependable and Secure Computing, vol. 10, no. 5, September/October 2013.
[2] Federico Larumbe and Brunilde Sanso,‖ A Tabu Search Algorithm For The Location Of Data
Centers And Software Components In Green Cloud Computing Networks‖, IEEE Transactions On
Cloud Computing, Vol. 1, No. 1, January-June 2013.
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Provisioning In The Cloud‖, IEEE Transactions On Cloud Computing, Vol. 1, No. 2, July-
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[4] Zhen Xiao, Weijia Song, and Qi Chen, ‖ Dynamic Resource Allocation Using Virtual Machines
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24, no. 6, June 2013.
[5] Mayank Mishra, Anwesha Das, Purushottam Kulkarni, and Anirudha Sahoo,‖ Dynamic Resource
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6804/12, September 2012.
[6] Li Hongyou, Wang Jiangyong, Peng Jia, Wang Junfeng, Liu Tang, ‖Energy-Aware Scheduling
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7
[17] Mehiar Dabbagh, Bechir Hamdaoui, Mohsen Guizani, and Ammar Rayes,‖ Toward Energy-
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Authors
P.MALATHI received the B.E degree in computer science and engineering from
Nandha Engineering College in 2014. She is currently doing her M.E Computer science
and Engineering in Nandha engineering college, Erode, India.
DR.S.ARUMUGAM,completed B.E and M.Sc.,(Engg.)., from P.S.G College of
Technology,University of Madras in the year 1971 and 1973 respectively.he completed
his Ph.D from College of Engineering, Guindy,Anna University in the year 1990. He
worked in the Department of Technical Education, Government of Tamilnadu from the
year 1974 to 2007 at various capacities,AssociateLecturer,Lecturer,Assistant
Professor,Professor,Principal and Additional Director Of Technical Education. He is
presently working as professor and CEO in Nandha engineering college. He has
published more than 100 papers in various journals. He had a profession membership in ISTE, CSI, IEEE,
IE (I) and IETE. 22 faculty members have completed Ph.D. under his guidance and currently he is
supervising 20 research scholars.

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