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Journal of Advanced & Applied Sciences (JAAS)
Volume 03, Issue 03, Pages 107-112, 2015
ISSN: 2289-6260
An Energy-aware resource management scheme of Data Centres for eco-
friendly cloud computing
Ashis Kumar Mandal*, Mohd Nizam Bin Mohmad Kahar
Faculty of Computer System & Software Engineering(FSKKP), University Malaysia Pahang, , Kuantan, Malaysia
* Corresponding author. Tel.: 0102575325;
E-mail address:[email protected]
A b s t r a c t
Keywords:
Cloud computing,
NP-Hard problem,
Simulated Annealing,
Data centres,
Virtual Machine.
Cloud computing is one of the most popular technologies at recent times that delivers on-
demand applications and resources over the Internet. The main hub of the cloud computing
is numerous data centres, from where all of the services are disseminated towards the end
users. Although cloud computing is being appeared as a big business in IT industry,
consumption of too much energy in cloud data centres has created new concern, especially
in terms of increasing energy-related costs and carbon dioxide emission rate. Therefore,
cloud resources, such as CPU, memory, networks, need to be managed energy-efficiently
to reduce operational costs as well as the negative consequences of cloud computing on
our natural environment. In this paper, we explore a VM (Virtual Machine) consolidation
model that focuses on minimizing power consumption and resource wastage. As VM
consolidation is NP-Hard problem, we also suggest efficient resource allocation scheme
using meta-heuristic algorithm, such as simulated annealing (SA), which try to optimize
VM consolidation and ensure proper utilization of resources.
Accepted: 30 April2014 © Academic Research Online Publisher. All rights reserved.
1. Introduction
With the expansion of Internet, Cloud computing
has emerged as a new model providing numerous
services, such as storage, data access, software and
computation, to the customer through online.
Usually these services are classified as Platform as
a Service (PaaS), Software as a Service (SaaS) and
Infrastructure as a Service (IaaS)[1]. As cloud
computing is highly scalable, cost-effective, and
on-demand service provider, it is lucrative to many
business organizations, academic institutions and
consumers. Recently, online companies, like
Google, Yahoo, Microsoft, Amazon , have adapted
cloud technology and have established new data
centers all over the world. The more the consumers
incline to cloud technology, the more the data
centers have been constructed. Although these data
centers ensure smooth service to consumers,
consumption of large amount of power creates new
concern to providers. These eventually lead to
increase more carbon dioxide emission in the
environment and energy related cost of data
centers.
A recent survey indicates that the amount of energy
consumed by average data centre is equal to that of
25,000 households [2]. Gartner stated that in 2007
information and communication industry produced
2% of total carbon dioxide emission[3], and data
centres are major contributor of this emission. It is
apparent that larger data centres consume more
energy which results in more carbon emission in
the environment .Therefore, energy-aware design
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of data centres is indispensible that can reduce
carbon emission. To tackle the problem, recently,
eco-friendly, or more precisely, Green Cloud
computing is envisioned that manage the data
centres resources energy-efficient manner [4].
Among various techniques, virtualization
technologies allow data centres to address such
resource and energy inefficiency by placing
multiple Virtual Machines (VM) in Physical
Machines (PM) efficiently, such as through live
VM migration, online-monitoring and VM
placement optimization technique. This energy
efficiency is achieved through switching idle
physical servers to lower power states.
In this paper, we attribute our main concentration
on placement of VMs optimally in order to
ensuring better resource allocation. As placement
of VMs in large data centres optimally is infeasible
producing a combinatory optimization problem, we
investigate a VM consolidation model and use
meta-heuristic technique like simulated annealing
(SA) to get near optimum solution in case of
placing VMs in PMs. This approach of VMs’
consolidation could ensure better resource
utilizations and result in reducing energy
consumptions of data centres.
2. Related Work
Conservation of cloud data centres’ energy has
gained much research attention in last decade.
Researchers proposed numerous strategies both in
hardware and software levels for green cloud
computing. Development of energy-ware hardware,
for instance, for cloud data centres can be a good
approach. Designing energy-ware processor, using
solid-state discs, and intelligence power down
mechanism also help energy conservation of
cloud[5].
There are several works have been proposed on
energy-efficient scheduling in cloud system. In[6] ,
authors proposed a green scheduling algorithm that
minimized the consumption of server power in
cloud. They used neural network technique to help
the algorithm in terms of predicting which machine
should be turned off/on. Garg et al. in [7]
developed near-optimization scheduling policy
which not only minimized energy consumption of
data centre but also maximized earnings of cloud
provider. Authors in [8], considered hybrid genetic
algorithm to schedule set of tasks in set of
processors so that energy efficiency of data centre
might be ensured as much as possible. Similarly,
[9] adopted genetic algorithm as well as Dynamic
Voltage Scheduling (DVS) to schedule tasks in
energy-ware. DVS allows multiprocessors to
dynamically change their voltage levels aiming to
lessen energy utilization[9] , whereas GA makes
optimum decision.
Another approach is to use VM consolidation for
reducing energy consumption of data centres. In
[10], authors used heuristic approach for
dynamically allocating VMs by live migration
considering CPU performance. Their simulation
result indicates significant amount of energy
savings. To reduce carbon footprint in cloud data
centres, in [11], Liu et al. proposed a green cloud
architecture and considered optimization for VMs
placements. They claimed that this architecture was
able to save 27% of energy. Likewise, in [12],
authors developed deterministic algorithm
considering adaptive heuristics and optimization
for dynamic VM consolidation in energy-ware.
Besides, artificial intelligence and machine learning
approaches like reinforcement learning, multi-agent
approach have also been used for reducing energy
consumption of cloud [13, 14].
Mandal et al. / Journal of Advanced & Applied Sciences (JAAS), 3 (3): 107-112, 2015
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3. Proposed scheme
3.1. VM consolidation and optimization
Cloud computing environment adopts virtualization
concept to increase the overall efficiency of cloud.
It allows transferring VMs among PMs using live
or off-line migration. In a cloud computing context,
VM instances might be deployed and removed at
any time dynamically, which causes resource
fragmentation in the servers and this leads to server
inefficiency in term of energy consumptions. To
prevent degradation in server resource utilization,
VM consolidation performs as a tool which tries to
minimize the PM by turning off the PM or
retaining it in energy savaging mode according to
the requirement of current resource. This technique
helps data centres to reduce their energy
consumption significantly.
However, energy-aware dynamic consolidation of
VMs is not always easy task, especially when
multi-dimensional resource demands have to be
considered. For example, when two resources, such
as CPU and Memory, are considered, effort to
energy-aware VMs placement in PMs is more
complex compared to considering single resource.
Moreover, the complexity increases with the
number of VMs and resource dimension. To tackle
this, using traditional greedy approach including
First Fit, Next Fit, Random Fit[15], might not
produce optimal or near optimal solution for
energy-aware VMs consolidation. In this respect, it
is one of the research challenges to optimize VM
consolidation over considering numerous
resources. Figure 1 shows a simple example of
optimization over two resources (CPU and
Memory). Initially 8 VMs are placed in 4 PMs.
Without violation of two resource constraints, it is
possible to place 8 VMs optimally in two PMs only
(PM1, PM2), which lead to turn off other two PMs
(PM3, PM4), and, hence, reduce power
consumption of data centre.
3.2. Modeling VM consolidation for
optimization
We can consider VM consolidation as a Multi-
dimensional Vector Packing Problem (mDVPP)
where a number of items have to be packed into the
minimum number of bins provided that bins
capacities are not violated [16-18]. Like mDVPP,
VM consolidation is a NP-Hard problem. To
generate mathematical model for VM
consolidation, we consider PMs as bins and the
VMs as items to pack into the
Fig .1: Optimization of VM consolidation with
considering two resources.
bins. In [18] ,authors have proposed a model for
VM consolidation as Multi-dimensional Vector
Packing Problem. The model worked fine in VM
consolidation. In this paper we adopt it for
optimization problem.
Let P denotes the set of n PMs and V denotes the
set of m VMs in the data center. The set of d types
of resources available in the PMs is represented by
Mandal et al. / Journal of Advanced & Applied Sciences (JAAS), 3 (3): 107-112, 2015
110 | P a g e N C O N - P G R 2 0 1 5
R. Each PM Pi (Pi ∈ P) has a d-dimensional
Resource Capacity Vector (RCV) 𝐶𝑖 =
⟨𝐶𝑖1, … , 𝐶𝑖
𝑘, … . , 𝐶𝑖𝑑⟩where 𝐶𝑖
𝑘 denotes the total
capacity of resource Rk of PM Pi. Similarly, each
VM , Vj (Vj ∈ V) is represented by its d-
dimensional Resource Demand Vector (RDV) 𝐷𝑖 =
⟨𝐷𝑖1 , … , 𝐷𝑖
𝑘 , … . , 𝐷𝑖𝑑⟩ where 𝐷𝑗
𝑘 denotes the demand
of resource Rk of VM Vj. The Resource Utilization
Vector (RUV) 𝑈𝑖 = ⟨𝑈𝑖1, … , 𝑈𝑖
𝑘 , … . , 𝑈𝑖𝑑⟩ of PM Pi
is computed as the sum of the RDVs of the hosted
VMs:
𝑈𝑖𝑘 = ∑ 𝐷𝑗
𝑘 𝑓𝑜𝑟 ∀𝑥𝑖,𝑗 = 1 (1)
where x is the Placement Matrix that models the
VM-to-PM placements and is defined as follows:
𝑥𝑖,𝑗 = {1 𝑖𝑓 𝑉𝑗 𝑖𝑠 𝑝𝑙𝑎𝑐𝑒𝑑 𝑖𝑛 𝑃𝑖
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (2)
Another terms is the PM Allocation Vector y,
where each element yi equals 1 if PM Pi is hosting
at least one VM, or 0 otherwise:
𝑦𝑖 = {1 𝑖𝑓 ∑ 𝑥𝑖,𝑗 ≥ 1𝑚
𝑗=1
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (3)
The goal of the proposed VM consolidation
algorithm is to place the VMs in the available PMs
in such a way that resource utilization of active
PMs is maximized across all dimensions and power
consumption of active PMs is minimized. We
formulate the objective function or cost function as
a single minimization function on y:
min 𝑓(𝑦) = ∑ 𝑦𝑖𝑛𝑖=0 (4)
Finally, the PM resource capacity constraint (i.e.
for each resource type, demands Dk of hosted VMs
not to exceed host PM’s resource capacity Ck) is
expressed as follows:
∑ 𝐷𝑗𝑘𝑚
𝑗=1 𝑥𝑖,𝑗 ≤ 𝐶𝑖𝑘 , ∀𝑖 ∈ {1, … . , 𝑛}, ∀𝑘 ∈ {1, … , 𝑑}
(5)
and the following ensures that each VM is assigned
to at most one PM:
∑ 𝑥𝑖,𝑗𝑛𝑖=1 ≤ 1, ∀𝑗 ∈ {1, … . . , 𝑚} (6)
The equation 5 and 6 indicate our hard constraints.
3.4. Proposed Algorithm
Simulated annealing (SA) is one of the most
popular local search based meta-heuristic
techniques to find optimal or near optimal solution
of NP-Hard problems. It is a stochastic algorithm
that probabilistically accepts some worst solutions
to escape from the local optimum[19]. Algorithm 1
illustrates how we use SA to achieve near optimal
solution.
First, for generating feasible or initial solution, we
use greedy approach such as Next Fit (NF). In a
certain time, if a VM request appears, PM scanned
firstly with sufficient resources is selected for
allocating this VM and then it is placed in the PM.
Next VM is placed in the PM providing sufficient
resources are available. If that PM does not able to
allocate VM, new PM will be selected for placing
the VM. In this way all VMs are placed in PMs and
generate initial solution. In this NF approach,
although local optimization can be acquired, for
global optimum solution, we can use the simulated
annealing (SA).
Phase second starts with initial temperature –a high
temperature, which indicates diversification in
early search. For each iteration, the temperature is
gradually reduced to restrict the acceptance of low
quality solution (intensification) and end when
terminal condition meet (temperature is zero).
In each iteration, we calculate the cost function of
present solution using equation 4, and tweak the
present solution randomly to generate neighbor
solution. In other words, swap the random VM
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111 | P a g e N C O N - P G R 2 0 1 5
(virtual machine) from its current physical machine
(PM) to new physical machine allowing violation
of the soft constraint (equation 4) with very low
probability but maintaining the hard constraint
(equation 5, 6). If the cost of new solution is
minimum than the previous solution, new solution
will be accepted as present solution. Otherwise,
decision to acceptance will be confirmed by
probabilistic function. Finally, we take the best
solution as placement of VMs in PMs.
Algorithm 1: Simulated Annealing in VM
consolidation problems
1 S← Create initial solutions using Next Fit
approach
2 bestSolution← S
3 T← Set initial temperature; initially high
temperature
4 C← Set cooling rate
5 while T>1 do //termination criterion not
satisfied(until system has cooled)
6 Create new neighbor S* from S by
applying an random move of VM to PM
7 if Cost(S*)< Cost(S) or a random
number chosen
from 0 to 1<𝑒𝐶𝑜𝑠𝑡(𝑠)−𝐶𝑜𝑠𝑡(𝑠∗)
𝑡 then
8 S←S*
9 end if
10 T=T*(1-C) //decrease T(cooling )
11 if Cost(S)< Cost(BestSolution) then
12 bestSolution ←S
13 end if
14 end while
15 return bestSolution as placement of VMs to
PMs
4. Conclusions
Ensuring energy-efficiency of a cloud data centre is
really a challenging task requiring managing
numerous computing resources efficiently. In this
paper, we have explored a model to address
resource wastage minimization in cloud data
centers ensuring green cloud computing. When
data center is large and multiple resources have to
be considered, it is quiet important to use the
optimization technique for better energy efficiency
of the data centers. Hence, focus is given on VM
consolidation optimally based on simulated
annealing meta-heuristic. It is expected that the
proposed work could help cloud providers in
improving carbon and energy foot print of their
clouds, which eventually likely to promote a green
cloud solution to consumers.
So far in this study we have formulated the
methodologies of proposed scheme only. In the
future, we will implement and test it with cloud
simulator such as CloudSim[20] to analyze the
feasibility and the practicability of the proposed
approach.
References
[1] Bera S, Misra S, Rodrigues JJ. Cloud
Computing Applications for Smart Grid: A Survey.
2014.
[2] Kaplan JM, Forrest W, Kindler N.
Revolutionizing data center energy efficiency.
Technical report, McKinsey & Company; 2008.
[3] Rivoire S, Shah MA, Ranganathan P, Kozyrakis
C. JouleSort: a balanced energy-efficiency
benchmark. Proceedings of the ACM SIGMOD
international conference on Management of data:
ACM; 2007; 365-76.
[4] Garg SK, Yeo CS, Buyya R. Green cloud
framework for improving carbon efficiency of
clouds. Euro-Par Parallel Processing: Springer;
2011; 491-502.
[5] Berl A, Gelenbe E, Di Girolamo M, Giuliani G,
De Meer H, Dang MQ, et al. Energy-efficient cloud
computing. The computer journal. 2010;53:1045-
51.
[6] Duy TVT, Sato Y, Inoguchi Y. Performance
evaluation of a green scheduling algorithm for
Mandal et al. / Journal of Advanced & Applied Sciences (JAAS), 3 (3): 107-112, 2015
112 | P a g e N C O N - P G R 2 0 1 5
energy savings in cloud computing. Parallel &
Distributed Processing, Workshops and Phd Forum
(IPDPSW), IEEE International Symposium on:
IEEE; 2010; 1-8.
[7] Garg SK, Yeo CS, Anandasivam A, Buyya R.
Environment-conscious scheduling of HPC
applications on distributed cloud-oriented data
centers. Journal of Parallel and Distributed
Computing. 2011;71:732-49.
[8] Mezmaz M, Melab N, Kessaci Y, Lee YC,
Talbi E-G, Zomaya AY, et al. A parallel bi-
objective hybrid metaheuristic for energy-aware
scheduling for cloud computing systems. Journal of
Parallel and Distributed Computing. 2011;71:1497-
508.
[9] Chang-tian Y, Jiong Y. Energy-aware genetic
algorithms for task scheduling in cloud computing.
ChinaGrid Annual Conference (ChinaGrid),
Seventh: IEEE; 2012; 43-8.
[10] Beloglazov A, Buyya R. Energy efficient
resource management in virtualized cloud data
centers. Proceedings of the 10th IEEE/ACM
International Conference on Cluster, Cloud and
Grid Computing: IEEE Computer Society; 2010;
826-31.
[11] Liu L, Wang H, Liu X, Jin X, He WB, Wang
QB, et al. GreenCloud: a new architecture for green
data center. Proceedings of the 6th international
conference industry session on Autonomic
computing and communications industry session:
ACM; 2009; 29-38.
[12] Beloglazov A, Buyya R. Optimal online
deterministic algorithms and adaptive heuristics for
energy and performance efficient dynamic
consolidation of virtual machines in cloud data
centers. Concurrency and Computation: Practice
and Experience. 2012;24:1397-420.
[13] Tesauro G, Das R, Chan H, Kephart J, Levine
D, Rawson F, et al. Managing power consumption
and performance of computing systems using
reinforcement learning. Advances in Neural
Information Processing Systems 2007; 1497-504.
[14] Das R, Kephart JO, Lefurgy C, Tesauro G,
Levine DW, Chan H. Autonomic multi-agent
management of power and performance in data
centers. Proceedings of the 7th international joint
conference on Autonomous agents and multiagent
systems: industrial track: International Foundation
for Autonomous Agents and Multiagent Systems;
2008; 107-14.
[15] Gahlawat M, Sharma P. Survey of virtual
machine placement in federated clouds. Advance
Computing Conference (IACC), IEEE
International: IEEE; 2014; 735-8.
[16] Caprara A, Toth P. Lower bounds and
algorithms for the 2-dimensional vector packing
problem. Discrete Applied Mathematics.
2001;111:231-62.
[17] Monaci M, Toth P. A set-covering-based
heuristic approach for bin-packing problems.
INFORMS Journal on Computing. 2006;18:71-85.
[18] Ferdaus MH, Murshed M, Calheiros RN,
Buyya R. Virtual Machine Consolidation in Cloud
Data Centers Using ACO Metaheuristic. Euro-Par
Parallel Processing: Springer; 2014; 306-17.
[19] Van Laarhoven PJ, Aarts EH. Simulated
annealing: Springer; 1987.
[20] Calheiros RN, Ranjan R, Beloglazov A, De
Rose CA, Buyya R. CloudSim: a toolkit for
modeling and simulation of cloud computing
environments and evaluation of resource
provisioning algorithms. Software: Practice and
Experience. 2011;41:23-50.