an energy -aware resource management scheme of data ...€¦ · an energy -aware resource...

107 | P a g e

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

http://www.aropub.org/journals/journal-of-advanced-applied-sciences-jaas/

Mandal et al. / Journal of Advanced & Applied Sciences (JAAS), 3 (3): 107-112, 2015

108 | P a g e N C O N - P G R 2 0 1 5

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].


109 | P a g e N C O N - P G R 2 0 1 5

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


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


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


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.

an energy -aware resource management scheme of data ...€¦ · an energy -aware resource...

Documents