A survey on energy efficient with task consolidation in the virtualized cloud computing environment

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 03 | Mar-2014, Available @ http://www.ijret.org 70
A SURVEY ON ENERGY EFFICIENT WITH TASK CONSOLIDATION IN
THE VIRTUALIZED CLOUD COMPUTING ENVIRONMENT
Elizabeth Sylvester Mkoba1
, Mokhtar Abdullah Abdo Saif2
1, 2
Student, School of Computer Science and Technology, Dalian University of Technology, Liaoning, China
Abstract
Cloud computing is a new model of computing that is widely used in today’s industry, organizations and society in information
technology service delivery as a utility. It enables organizations to reduce operational expenditure and capital expenditure. However,
cloud computing with underutilized resources still consumes an unacceptable amount of energy than fully utilized resource. Many
techniques for optimizing energy consumption in virtualized cloud have been proposed. This paper surveys different energy efficient
models with task consolidation in the virtualized cloud computing environment.
Keywords: Cloud computing, Virtualization, Task consolidation, Energy consumption, Virtual machine
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1. INTRODUCTION
Cloud computing is a new service model for Information
Technology provision, often based on virtualization and
distributed computing technologies. Resource provisioning
based on virtual machine (VM) has been widely accepted and
adopted in cloud computing environments. Virtualized
resources with a low utilization rate still consume an
unacceptable amount of energy compared to the energy
consumption of a fully utilized cloud computing. According to
[13], energy consumption of an idle resource can be as much
as 60% or peak power. In cloud computing, there is a
relationship between energy consumption and resource
utilization. Task consolidation is an effective technique greatly
enabled by virtualization technologies, which facilitate the
concurrent execution of several tasks, maximize resource
utilization and in turn reduce the energy consumption [1].
The cloud resource utilization can be realized by dynamically
allocating resources based on utilization analysis and
prediction [5]. A good resource management scheme is
fundamental for on-demand resource allocation, performance
optimization, load balancing and energy saving in the cloud
system. Although some existing task consolidation algorithms
have been developed to address the energy efficiency problem,
they normally result in high migration costs, ignoring other
virtual resources which influence energy consumption as well
as low resource utilization due to ignoring the multi-
dimensional characteristics of virtual machines.
In this survey paper, energy optimization techniques are
discussed. The rest of the paper is organized as follows:
Section 2 presents task consolidation overview, Section 3
presents the literature review of the existing methods of
energy efficient with task consolidation, and Section 4
presents a conclusion.
2. TASK CONSOLIDATION
2.1 Task Consolidation Problem
The task consolidation problem is the process of assigning a
set of t tasks (service requests) to a set of r cloud computing
resources in order to maximize resource utilization and
ultimately to minimize energy consumption. Time constraints
are directly related to the resource usage associated with the
tasks. In the task consolidation problem, the resource allocated
to a particular task must sufficiently provide the resource
usage of the given task. Task consolidation aims at effective
usage of cloud resources by consolidating a set of tasks into a
small number of virtual machines. The advantage of effective
resource usage is to reduce monetary cost by reducing: (a)
amount of virtual machines; (b) labors required to maintain
virtual machines; (c) floor space; and (d) energy consumption.
2.2 Task Parameters
There is a relationship between energy consumption, system
configurations, performance and task workload in the virtual
machines. When tasks are running in the virtual machines
resources are utilized such as CPU, memory, disk, network
bandwidth which lead to energy consumption. The workload
of a task comprises with: (a) data size for the task; (b) number
of processes for the task; (c) size of data to be transmitted (d)
task processing time. The energy consumption is highly
influenced by the workloads for a task because they consume
resources, as shown in figure 1. The design of the cloud
computing system needs to take into account how the
following tasks categories are executed in the virtual
machines: (a) communication-intensive tasks; (b)
computation-intensive tasks; (c) data-intensive tasks.
Furthermore, during the cloud system design these questions
need to answered such as what are the task characteristics?,

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when to migrate the tasks?, which task to migrate? how to
reduce the tasks migration overheads?
3. EXISTING ENERGY EFFICIENT MODELS
WITH TASK ONSOLIDATION IN THE CLOUDS
3.1 Energy Conscious Task Consolidation
The model proposed by [13] for task consolidation is called
energy-conscious task consolidation (ECTC). ECTC computes
the energy consumption of a given task on a selected resource
and ECTC cost function is designed to encourage resource
sharing.
However, this model does not take into consideration other
resources which have influence in energy consumption such as
disk utilization, memory utilization, network bandwidth usage,
application software. It tends to increase number of virtual
machines within the cluster. It does not consider the migration
of task from one virtual cluster to another.
3.2 Maximum Rate Utilization of Resources
In [13], authors studied energy efficient utilization of resource
in the cloud computing systems. The problem addressed is a
resource allocation strategy that takes into account resource
utilization that leads to better energy efficiency. The cloud
computing system utilizes virtualization technologies where
tasks can be easily consolidated which is an effective method
to increase resource utilization and in turn reduces energy
consumption. They proposed task consolidation Maximum
rate Utilization (MaxUtil). MaxUtil looks after the more
energy efficient resources in terms of resource utilization. The
energy model is devised on the basis that processor utilization
has a linear relationship with energy consumption.
The task consolidation problem is modeled as a bin packing
problem where virtual machines represent bins, tasks are
objects to be packed in the bins and CPU resource utilization
represents bin dimension. They proposed 100% resource
utilization rule for virtual machines and makes task
consolidation decision based on the resource utilization which
is a key indicator for energy efficiency. The advantage of this
method is to reduce number of virtual machines in the cluster
which resulted in minimizing energy consumption in the cloud
computing system.
However, the deficiencies of the proposed MaxUtil model are:
(a) it assumes energy consumption is linear to resource
utilization focused on CPU usage without considering other
resources utilization such as disk, network bandwidth,
memory etc.; (b) the incorporation of task migration increased
energy consumption because migrated tasks tend to be with
short remaining processing time and these tasks are most
likely to hinder the consolidation of a new arriving task (c) the
migration of task from one virtual cluster to another is not
modeled.
3.3 Energy Task Consolidation
The authors [1] proposed energy-aware task consolidation
(ETC) technique to optimize energy consumption of virtual
clusters. The CPU and network utilization are taken into
account when dealing with task migration in the virtual
clusters. They proposed 70% CPU utilization principle to
manage task consolidation among virtual clusters in the cloud
computing system. When the CPU utilization in a virtual
cluster is above 70%, the task is migrated to another cluster. If
there are multiple virtual machines in the clusters available to
receive the tasks, the one with minimal energy cost is chosen
to perform task consolidation.
Their experiment results show that energy-aware task
consolidation technique can reduce power consumption in
managing task consolidation for cloud systems. However, by
using 70% principle for CPU utilization, the virtual machine
still consumes energy because it is not fully utilized. ETC
increases number of virtual machines during the task
consolidation as compared to MaxUtil technique.
3.4 Task-Based Energy Consumption
The authors in [11] studied task-based energy consumption in
the virtualized cloud computing systems. High energy
consumption directly contributes to operational costs and
environmental damage such as high carbon emission. In order
to address the problem, they proposed an energy consumption
model to calculate the energy consumption of specific types of
tasks in cloud systems and use the model to dynamically
organize cloud application load. They divided tasks into three
types: computation-intensive, data-intensive, and
communication-intensive and measure the performance with
its corresponding energy consumption.
They investigated the relationship between energy
consumption, system configurations and cloud system
performance. The total energy consumption of a cloud
workload is divided into a fixed part and dynamic part. The
proposed technique determines the energy consumption for
computation-intensive, data-intensive and communication-
intensive and compares it with system performance. The task

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parameters such as data size to be processed and transmitted
as well as number of processes have impact on the task energy
consumption model. They conducted experiments with
varying system configurations and workloads with
corresponding energy consumption and performance data.
Their results revealed that, there is correlation coefficient of
energy consumption, system configuration, and workload as
well as system performance in the cloud systems.
4. COMPARISON OF THE EXISTING METHODS
In this survey, we compare the existing methods of energy
optimization with task consolidation as shown in table 1.
Task
consoli-
dation
method
System
resource
used
Power
saving
technique
Deficiencies
of the method
ECTC CPU
Task
consolidati
on
(a) increase number of
virtual machines
(b) has not considered
migration of task from
one cluster to another.
MaxUtil CPU
Maximize
resource
utilization
to reduce
number of
virtual
machine
(a)has not considered
migration of task from
one cluster to another.
(b)has not considered
other resources which
have influence in
energy consumption.
ETC
CPU,
net-work
Decrease
resource
utilization
(a) 70% principle for
CPU utilization, the
virtual machine still
consumes energy
because it is not fully
utilized.
(b)ETC increases
number of virtual
machines during the
task consolidation as
compared to MaxUtil
technique.
Task-
based
energy
consum
p-tion
CPU,
disk,
memory
System
configurati
ons
(a)has not considered
the energy
consumption when
task migrate to other
virtual cluster.
(b) has not compared
task resource
utilization against
energy consumption as
in ETC and MaxUtil
method.
5. CONCLUSIONS
Task consolidation is an effective technique greatly enabled
by virtualization technologies, which facilitate the concurrent
execution of several tasks in virtual machines and in turn
reduce the energy consumption. Task consolidation in cloud
computing presents a significant opportunity for energy
optimization. In this paper we surveyed various existing task
consolidation methods to minimize energy consumption in the
virtualized cloud computing environments. The main objective
of cloud providers is to develop resource provisioning and
management solutions that minimize energy consumption
while guaranteeing Service level Agreements. Thus, this
survey can be used to improve the energy consumption models
by designing energy optimization models, energy consumption
monitors and energy prediction models for the cloud systems.
REFERENCES
[1] C. Hsu, S.Chen, C.Lee, H. Chang, K. Lai, K. Li and
C.Rong, “ Energy – Aware Task consolidation
Technique for cloud computing”, IEEE , CloudCom
2011, pp.115-121
[2] B.Guenter, N.Jain and C. Williams,“ Managing cost,
Performance and Reliability Tradeoffs for Energy-
Aware Server Provisioning”, IEEE INFOCOM 2011,
pp.1332-1340.
[3] C. Yang, K.Wang, H. Cheng, C. Kuo and W. Chu,
“Green Power Management with Dynamic Resource
Allocation for cloud virtual machines”, IEEE
International Conference on High Performance
Computing and Communications ,2011, pp.726-733
[4] R. Buyya, S.Garg and R. Calheiros, “ SLA-Oriented
Resource Provisioning of cloud computing:
Challenges, Architecture and Solutions”, IEEE
International Conference on Cloud and Service
Computing, 2011, pp1-10
[5] Y.Shi, X.Jiang and K. Ye, “An Energy-Efficient
scheme for cloud resource provisioning based on
CloudSim”, IEEE International Conference on Cluster
Computing, 2011, pp.595-599
[6] S.Garg, C.Yeo and R. Buyya, “ Green cloud framework
for improving carbon efficiency of clouds”, ACM 17th
International Conference, Euro-Par Parallel Processing
2011, pp.491-502
[7] F. Wuhib, R. Stadler and H. Lindgren, “Dynamic
Allocation with Management – Implementation for an
OpenStack Cloud”, 8th International Conference on
Network and Service Management 2012, pp.309-315.
[8] Z. Zhou, F. Liu, H.Jin, B.Li, B.Li and H. Jiang “, On
Arbitrating the Power-Performance and Tradeoff in
Saas Clouds”, INFOCOM 2013, pp.872-880
[9] A. Beloglazov and R. Buyya , “Managing Overloaded
Hosts for Dynamic Consolidation of Virtual Machines
in Cloud Data Centers under Quality of Service
Constraints”. IEEE Transactions on Parallel and
Distributed Systems, vol. 24, N0.7, July 2013.

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[10] P.Leelipushpam and J. Sharmila, “Live Migration
Techniques in cloud environment –Survey, IEEE
Conference on Information and Communication
Technologies, 2013, pp.408-413.
[11] F.Chen, J. Grundy, Y.Yang, J. Schneider and Q.He,
“Experimental Analysis of Task-based Energy
consumption in cloud computing systems”, ACM ICPE
2013, pp.295-306.
[12] L. He, D. Zou, Z. Zhang, K. Yang, H. Jin, and S. A.
Jarvis, "Optimizing Resource Consumptions in
Clouds",12th IEEE/ACM International Conference on
Grid Computing (GRID), 2011, pp. 42-49.
[13] Lee and A.Zomaya, “Energy efficient utilization of
resources in cloud computing systems”, Journal of
Supercomputing 2012, pp. 268-280.
BIOGRAPHIES
Elizabeth Sylvester Mkoba is pursuing PhD
in Computer Science and Technology from
Dalian University of Technology, China. Her
research area is Cloud Computing.
Mokhtar Abdullah Abdo Saif is pursuing
PhD in Computer Science and Technology
from Dalian University of Technology,
China. His research area is Cloud
Computing.

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