Energy Sustainability in Cooperating Clouds

Antonio Celesti, Antonio Puliaﬁto, Francesco Tusa and Massimo Villari

Universit

a degli Studi di Messina, Facolt

a di Ingegneria, Contrada di Dio, S. Agata, 98166 Messina, Italy

Keywords:

Cloud Computing, Federation, Virtual Infrastructure Management, Energy Sustainability, Photovoltaic

Systems, Renewable Energy, Energy Optimization.

Abstract:

Nowadays, cloud federation is paving the way toward new business scenarios in which it is possible to enforce

more ﬂexible energy management strategies than in the past. Considering independent cloud providers, each

one is exclusively bounded to the speciﬁc energy supplier powering its datacenter. The situation radically

change if we consider a federation of cloud providers each one powered by both a conventional energy supplier

and a renewable energy generator. In such a context the opportune relocation of computational workload

among providers can lead to a global energy sustainability policy for the whole federation. In this work, we

investigate the advantages, constrains, and issues for the achievement of such a sustainable environment.

1 INTRODUCTION

Federation is the next frontier of cloud comput-

ing. Throughout the federation, different small and

medium Cloud providers belonging to different orga-

nizations, can join each other to achieve a common

goal, usually represented by the optimization of their

resources.

The basic idea is that a Cloud provider has not

inﬁnite resources. In order to achieve target busi-

ness scenarios a Cloud provider may need a ﬂexi-

ble infrastructure. Federation allows Cloud providers

to achieve such a resilient infrastructure asking ad-

ditional resources to other federation-enabled Cloud

Providers. Cloud federation is much more than the

mere use of resources provided by a mega-provider.

From a political point of view, the term federa-

tion refers to a type of system organization character-

ized by a joining of partially “self-governing” entities

united by a “central government”. In a federation,

each self-governing status of the component entities

is typically independent and may not be altered by a

unilateral decision of the “central government”.

Besides cloud mega-providers, also smaller/

medium providers are becoming popular even though

the virtualization infrastructures they have deployed

in their datacenters cannot directly compete with the

bigger counterparts. A way to overcome these re-

source limitations is represented by the promotion of

federation mechanisms among small/medium cloud

providers. This allows to pick up the advantages of

other form of economic model considering societies,

universities, research centers and organizations that

commonly do not fully use the resources of their own

physical infrastructures.

In this work, we focus on an innovative sustain-

able federated cloud scenario in which resources are

relocated between cloud providers whose datacenters

are partially powered by renewable energy generator

systems. The federation is seen as a way for reduc-

ing energy costs (Energy Cost Saving), but at the

same time a possibility to reduce the CO

emissions

(Energy Sustainability). Here we discuss strategies

and policies it is possible to apply in federated cloud

environments for achieving the just mentioned goals.

Speciﬁcally our assessment is aimed at the design of

an Energy Manager, to be included in whichever Vir-

tual Infrastructure Manager as well OpenStack, Open-

Nebula and Cloud Stack.

The manuscript is organized as follows. Section

2 introduce how an energy sustainability strategy can

be applied to a federated cloud environment. The en-

ergy consumption of a datacenter is affected by dif-

ferent factors including the Power contribution for

the Information Technology (IT) equipment (P

), the

Power contribution for the Electrical (POW) equip-

ments (P

POW

), and the Power contribution for the

Cooling (COOL) equipments (P

COOL

). To this regards

several energy considerations about cloud datacenters

are discussed in Section 3. As said before, a deci-

sion algorithm for sustainable federated clouds is pre-

sented in Section 4. Section 5 discusses related works.

Celesti A., Puliaﬁto A., Tusa F. and Villari M..

Energy Sustainability in Cooperating Clouds.

DOI: 10.5220/0004371200830089

In Proceedings of the 3rd International Conference on Cloud Computing and Services Science (CLOSER-2013), pages 83-89

ISBN: 978-989-8565-52-5

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

Section 6 summarizes conclusions and lights to the

future.

2 CLOUD FEDERATION AND

ENERGY SUSTAINABILITY

Federation brings new business opportunities for

clouds. In fact, besides the traditional market where

cloud providers offer cloud-based services to their

clients, federation triggers a new market where cloud

providers can buy and/or sell computing/storage ca-

pabilities and services from/to other clouds. A cloud

provider can decide to lend resources to other clouds

when it realizes that its datacenter is under-utilized at

given times. Typically, datacenters are under-utilized

during the night and over-utilized during the morning.

Therefore, as the datacenter cannot be turned off, the

cloud provider may decide to turn the problem into a

business opportunity.

As federation enables cloud providers to relocate

their services on other peers belonging to the system,

in our opinion, it is possible to carry out more ﬂexible

energy-aware scenarios than the past, when we con-

sidered independent non-federated clouds. Two pos-

sible alternative energy-aware scenarios are: Energy

Cost Saving and Energy Sustainability.

Figure 1: Example of Sustainable Federated Cloud Envi-

ronment.

The main contribution of this work is to propose

a possible approach for the achievement of such an

environment. Our approach is based on the following

idea: “moving the computation toward the more sus-

tainable available cloud datacenter”. This statement

is motivated by the following assumptions:

1. Often, the renewable energy generator systems

produce more energy than necessary.

2. It is very hard to store the exceeded produced re-

newable energy (e.g., in batteries).

3. Alternatively, it is becoming very difﬁcult to put

the exceeded produced renewable energy in pub-

lic electric grids. This practice is becoming a

problem for Energy suppliers as it implies uncon-

trolled power surges which are hard to be man-

aged. As this problem is becoming more and more

sensitive, the energy suppliers are becoming to be

reluctant to absorb energy produced by private re-

newable energy generator systems.

4. As consequence of 1), 2), and 3) often the ex-

ceeded produced renewable energy is wasted.

5. Consequently, it is easier to move the computa-

tion toward a datacenter powered by a renewable

energy generator system with a high large avail-

ability of energy than moving the “green energy”

toward the datacenter where the computing has to

take place.

If we consider a set of datacenters with these features,

a sustainable federated cloud environment can allow

to save money, maximizing the use of “green energy”

and reducing the level of carbon dioxide.

An example of such a scenario is depicted in Fig-

ure 1. The sustainable federated cloud ecosystem in-

cludes four cloud providers: Messina, Sidnay, S

Paulo, and Stuttgard. The electricity suppliers A, B,

C, and D are independent companies that provide en-

ergy at different costs. In our scenario, the datacenter

of each cloud providers is meanly powered by a pri-

vate renewable energy generator system as primary

source of energy. When the primary source of energy

is not enough to power the datacenter, the cloud uses

the energy of its Electricity Supplier. In addition, each

cloud is federated with each other in order to take the

advantages of service relocation and resource consol-

idation. Further details regarding how federate cloud

architecture are out of scope of this work. Further de-

tails can be found in Section 5.

Each cloud provider joins the federation in or-

der to move its computation in other federated clouds

where the production of “green energy” is maximum.

In simple terms, we move the computation load to-

wards the more efﬁcient renewable energy generators

(in term of produced electricity) maximizing the uti-

lization of the federated clouds in which the workload

has been transferred. Considering Figure 1, the four

clouds have different latitudes. According to a given

period (due to time zone and month), each renew-

able energy generator system that primarily powers

each cloud datacenter can have different energy efﬁ-

ciency compared to each other. These different con-

ditions can depend by different factors according to

the adopted source of renewable energy. For exam-

ple the amount of energy produced by a photovoltaic

system depends on the solar radiance which is differ-

CLOSER2013-3rdInternationalConferenceonCloudComputingandServicesScience

ent hour by hour, day by day, and month by month.

In addition, the energy production of a photovoltaic

system is also affected by the weather and climate

condictions. For simplicity, let us consider the time

zone, when the time of Messina is 17:00, the time of

ao Paulo is 13:00. In this situation, in S

ao Paulo the

solar radiance of the sun is stronger then the one in

Messina. On the other hand, considering both cities

in July, the temperature of Messina will be proba-

bly higher than the one in S

ao Paulo. Further similar

consideration can be made considering the latitude of

the two cities. This scenario implies that if a cloud

provider wants to enforce energy sustainability poli-

cies on its own datacenter, it relocate its services into

other federated cloud providers, chosen according the

aforementioned energy consideration. For reasons of

Quality of Service (QoS), let us suppose that each

cloud provider of the federation has replicated in ad-

vance part of its services into other federated clouds.

Considering a federation of Infrastructure as Service

(IaaS) clouds, service replication means copying Vir-

tual Machines (VMs) disk-image into other federated

providers. In this way the cloud providers that wants

to apply energy sustainability policies can turn off the

blade center hosting its VMs and turn on the copies

of these VMs pre-arranged into other federated cloud

datacenters, where the renewable energy production

is maximum according to temperature, latitude, and

time zone.

3 POWER CONSUMPTION

CONSIDERATIONS OF A

DATACENTER

The ﬁrst step for the achievement of a Sustainable

Cloud Federation is to better understand the main fac-

tors affecting the total power consumption of a data-

center. As already introduced these factors are P

POW

, and P

COOL

is related to the total power consumption of the

IT equipment such as: CPUs, Storage (i.e., Hard

Disk, Tapes, Optical Disks, etc.), RAM, Switches and

Router, Monitors.

POW

regards the total power consumption of the

Electrical equipment, for example, including: UPS

(Uninterruptible Power Supply), PSU (Power Supply

Unit), PDU (Power Distribution Unit), Cable (cop-

per wires characterized by an electrical resistance),

Lights, Batteries.

COOL

refers cooling equipment including for exam-

ple: Chiller. responsible for making the GAP among

the external (outdoor) and internal (indoor) tempera-

tures, FANs, regarding the Control Room Air Condi-

tioning (CRAC) or to equipment used to discard the

heat in the external ambient, Pumps, responsible for

moving the refrigerant substance (or water) inside the

distribution pipes, Valves, Unit of Control.

The entire cooling system of a datacenter can be

referred also as HVAC (i.e, Heating, Ventilating,

Air-Conditioning) or HVAC(R) (Heating, Ventilating,

Air-Conditioning, and Refrigerating). Consequently,

the total power consumption of a datacenter can be

deﬁned as:

TOT

= P

+ P

POW

+ P

COOL

(1)

Figure 2 shows the total amount of energy consump-

tion of a datacenter. The percentages of the total

power spent in a datacenter can be roughly distributed

as follows:

= 50%; P

POW

= 20%; P

COOL

= 30%. (2)

Figure 2: Typical Consumption of Energy inside a Datacen-

ter.

and P

POW

are strongly related to the transistors

performances. In fact, currently, they have a physical

limits that it is not possible to be overcame. However,

recent studies are trying to break such limits and the

expectation is that future innovation can bring to more

performance equipment from the point of view of the

energy consumption. In this direction, a recent and in-

teresting dissertation was conducted in The Optimist,

the Pessimist, and the Global Race to Exascale in 20

Megawatt (Tolentino and Cameron, 2012). Consid-

ering the aforementioned assumption, particular con-

sideration deserves P

COOL

. We believe that P

COOL

will

have a big role in the energy consumption studies in

the ICT ﬁeld. In fact, at present P

COOL

is the parame-

ter easier to optimize and how it is described later in

this manuscript, cloud federation can help to achieve

this goal.

In order to model a sustainable federated cloud en-

vironment, we consider the datacenter of each cloud

provider as a black box which acts an ideal refrigera-

tion machine also known as the Carnot Engine. The

performances of this ideal model is only affected by

the temperatures: the black box needs to be cooled

EnergySustainabilityinCooperatingClouds

as much as depending on the environment where it

is placed. Moreover we consider that in such a black

box there are both energy coming in, and heat that has

to be discarded in the external environment.

Considering the First Law of Thermodynamic

about the conservation of energy, we have the fol-

lowing equation (see Eq. 3) that states any compo-

nent/device connected to the Electric Grid transform

the energy from one form to other ones (Conservation

of Energy).

= W + Q (3)

Where P

is the input power over the time (i.e., in

hours), W is the energy spent for mechanical works

and Q is the energy release as Heat. In a datacenter,

is the electric power delivered through copper ca-

bles, W is the energy for producing movements (Com-

pressor of a Chiller, FANs, Hard Disk with rotors, Op-

tical Readers, etc.). Finally Q is the Heat produced by

components, lights, motors, compressors. In a data-

center, if we assume the P

and/or P

POW

, the contri-

bution to W is negligible. The Compressor in a chiller

COOL

) catch a lot of energy, but its work is useful for

expelling Heat from inside the datacenter to the out-

side (environment). Using the theoretical analysis of

the Thermodynamic model, it is possible to demon-

strate that for a Carnot Engine, the Energy Q is linked

to the Temperature T .

The measurement of goodness of a datacenter is

given by a number called Power Usage Effective-

ness (PUE). It is expressed as the ratio from the total

amount of energy consumed as input respect to good

part of energy used for IT computations. Values of

PUE equals to 1 correspond to an energy efﬁciency

of 100%.

PUE =

TOT

(4)

The increasing of the PU E value, corresponds to a

greater weight of either P

POW

or P

COOL

contributions

(or both). Typical PUE values for a datacenter are

greater than 1 and corresponds to 2-2.5.

Looking at Figure 3, it is possible to know how a

datacenter cooling system works. Although the Fig-

ure refers to an actual datacenter existing in Messina,

it may represent the general installation of a working

environment. The Figure shows two graphs (in the top

and bottom part of the DC) highlighting how the tem-

perature ranges with both Free Cooling and HVAC

Plants respect to the real distance from the datacenter

(HeatPath). Looking at the Free Cooling situation in

particular, it is possible to remark that the temperature

of the BladeCenter (T

chassis

) should be guaranteed ac-

cording to the external temperature (T

env

). This can

be accomplished only when the climate conditions of

a site allow this conﬁguration. HVAC and Free Cool-

Figure 3: The representation of a Datacenter: it is the real

installation of the DC in Messina. The APC CUBE uses the

HVAC cooling.

ing performances are both environment Temperature

dependent.

Hence, considering a sustainable federated cloud

environment the PU E of two different cloud datacen-

ters with the same equipment, but placed in two dif-

ferent regions, may assume different values. The en-

vironment temperature of each region affects the re-

sulting PUE depending on the adopted cooling tech-

niques.

4 DECISION ALGORITHM

Considering both the scenario previously described in

Section 2 and the considerations already pointed out

in this work, in the following we are going to intro-

duce a simple algorithm designed to address the prob-

lem of establishing the best site on which a given ser-

vice has to be deployed for minimizing both energy

consumption and costs, on the strength of the actual

environmental data collected on each.

We assume a given workload has to be executed in

our four-sites federated scenario in a given time: we

know how many resources will be needed to accom-

plish that task and how many free computational slots

will be available on each site. For each one, we also

assume to know the availability of the instantaneous

amount of electrical power produced from the photo

voltaic equipment, the amount of electrical power ab-

sorbed from the HVAC(R) (or free cooling system)

and that one used for achieving the computation (there

will be also other contribution that will be explained

in the following).

If we want to ﬁnd a method for optimizing compu-

tation respect to costs, we have to identify an analytic

CLOSER2013-3rdInternationalConferenceonCloudComputingandServicesScience

approach able to put together all the parameters char-

acterizing the scenario. Since energy providers apply

their fares on the actual amount of consumed kWh,

our analytic model will take into account the energy

contributions as mean value of the electrical power

over a time interval. In order to have a good snapshot

of the energy production/absorption when the com-

puting element placement have to be performed, in

our case this interval corresponds to one hour.

Looking at the scenario depicted in Figure 1,

and paying attention to the aforementioned consider-

ations, we might assume the energy consumption of

each site is represented from the Eq. 5

GRID

(T, G,s) =

(s) + E

COOL

(T ) + E

POW

− E

(T, G)] (5)

where each energy term represents the pro-

duced/absorbed medium power in a time interval of

one hour and is expressed in kWh. E

GRID

(T, G) is the

amount of power grid energy needed from a given site

and is related to the other energy contribution terms

appearing in the formula. According to this latter, it

will depend on the external temperature T where the

site operates, the G factor describing the availability

of the renewable green energy source (e.g, sun radi-

ation, wind intensity, etc.) and the s parameter asso-

ciated to the number of computational unit allocated

(e.g. virtual machines) on that site.

In this speciﬁc work, we are assuming each dat-

acenter can rely on a green energy source. For the

sake of simplicity, we can restrict our considerations

to a scenario where each site takes advantage of just

renewable photovoltaic energy. As consequence, we

can associate the G factor to the sun radiation factor

of the energy plant of a given site. According to the

above mentioned assumption, The remaining terms of

the expression in order are: E

, a non-constant fac-

tor associated to the energy needed from a data cen-

ter to perform the computation for a given service

that depends on the number s of computational slot

to be allocated; E

COOL

(T ) an energy factor associated

to the HVAC(R) system (or free cooling system) that

depends on the external temperature T characterizing

the area where the site is working (it is related to the

measured PUE for that site); E

POW

is the constant fac-

tor associated to the energy consumption of the power

supply equipment of the data center; ﬁnally the last

term E

(T, G) is a function of both external temper-

ature T and sun radiation factor G, related to the mean

energy amount made available from the photo voltaic

system of that site in the time interval of one hour.

If we consider that each site retrieves its electri-

cal power from a different provider, we might esti-

mate the costs needed in terms of electrical power to

achieve a workload execution according to the applied

fares for kWh. With this assumption and starting from

the Eq. 5, you can obtain a new expression 6 related

to the energy expenses associated to each site func-

tioning:

GRID

(T, G,s, c) = E

GRID

(T, G,s) · c (6)

In order to cut costs, the organization of our sce-

nario relying on four different computational sites,

can select the one for which the cost values associated

to expression 6 is minimum for a given set of parame-

ters (i.e. temperature T , sun radiation G, needed com-

putational slots s and fare f ). To achieve this goal,

we can consider data related to those parameters is

collected from a sensor network on each site period-

ically. Depending on these obtained values a table is

built considering the mean values of the retrieved pa-

rameters.

Taking into account such an approach, this par-

ticular module of the VIM operating on the datacen-

ters, will take care of computing the costs for each

site relying on the physical measurements collected

from the available sensors and stored in the table. An

example is reported in Table 1. A simple algorithm

implemented within the Energy Manager will be able

to choose the most convenient site (in terms of energy

consumption) where allocate the computation associ-

ated to a given service.

If two different sites are characterized from the

same cost values in a given time, the algorithm im-

plemented within the Energy Manager will evaluate

also the amount of photo voltaic energy made avail-

able from each site, ﬁnally preferring the one where

this factor is the greatest. In some situations where en-

ergy sustainability is preponderant on cost optimiza-

tion, the same algorithm could be applied in a differ-

ent way: the site(s) where the photo voltaic energy

production is the greatest is ﬁrst chosen and, in the

case of more sites producing the same photo voltaic

energy amount, from the retrieved set, the site where

the overall costs are the lowest will be ﬁnally selected

for allocating the services.

The algorithm could be implemented creating a

complementary module for the VIM (Energy Man-

ager) that retrieves needed data from the sensor net-

work available in each computational site, computing

the associated values of energy contributions (con-

sidering the instantaneous electrical power values re-

trieved in that moment) and storing them on the table

until the next data refresh (after one hour). We de-

signed the Energy Manager having in mind the pos-

sibility to include it in whichever Virtual Infrastruc-

ture Manager as well OpenStack, OpenNebula and

Cloud Stack. When one of these VIMs has to al-

locate a new set of s virtual machines for a given

EnergySustainabilityinCooperatingClouds

Table 1: Data retrieved from 4 different sites that will be given as input for the VIM Energy Manager.

Site Temperature

(T [°C])

Sun Radi-

ation (G

[MJ/m

])

Energy Grid

Fare (c [$])

Photo Voltaic

Energy

[kW h]

Slots (s) PUE Costs

[$]

Site 1 35 20 0.08 100 120 3.7 10

Site 2 30 18 0.09 150 90 3.2 13

Site 3 18 14 0.07 80 70 1.2 10

Site 4 23 15 0.08 80 75 2.5 15

service, together with snapshot of physical resource

availability of each site (reported in Table 1), it will

also invoke the Energy Manager to retrieve informa-

tion on the most convenient site to which deploy the

allocation either in terms of cost minimization or en-

ergy sustainability. Since each site can offer a limited

number of computational slots, if the virtual machine

number needed for a given service are greater than the

maximum availability for a site, the load will be split

across more sites still considering the satisfaction of

the same requirements (cost minimization or energy

sustainability).

Looking at Table 1, if we suppose executing a ser-

vice that needs 100 virtual machines, in the ﬁrst case

of cost optimization, the site selected by the Energy

Manager will be one between Site 1 or Site 3 (as they

guarantee the lowest energy costs: respectively 10 $

and 13 $). The ﬁnal choice will lead to Site 1 since his

photo voltaic energy availability (100 kWh) is greater

than the one offered by Site 3 (80 kWh). The E

availability in this situation is preponderant: although

the temperature in Site 3 is 18 °C and allows free cool-

ing as refrigeration methods, the C

GRID

costs coming

from Eq. 6 are still more convenient on Site 1 where

the “free cost” energy is offered by the photo voltaic

equipment. Furthermore, Site 1 has the availability

of s = 120 computational slots and is able to directly

satisfy the requested service demand. Otherwise the

computation would be split among textitSite 1 and

Site 3.

Still looking at the same table in the alternative sit-

uation we have mentioned before (energy sustainabil-

ity optimization), the ﬁrst set of selected sites will be

formed by Site 3 and Site 4 (either offering the same

amount of E

= 80 kWh). This time, the ﬁnal choice

will fall on Site 3 as it is able to offer lower grid en-

ergy costs than Site 4 (10 $ against 15 $). Differently

from the previous case, the available computational

slots of this site is lower than the needed ones. In this

case, 70 of the requested VMs will be deployed on

Site 3 while the remaining ones on Site 4.

As reported in the table, the HVAC(R) system of

each data center is characterized in terms of efﬁciency

through the PUE (the values in the table refers to a

mean value of the coefﬁcient in the time interval of

one hour). High PUE values are associated to bet-

ter refrigerator systems that allows to use less electri-

cal power to push out heat from the data center. The

PUE values are tightly related the E

COOL

(T ) values

contributing in Eq. 5. The best efﬁciency in terms

of energy spent for cooling is achieved on the site 3,

where thanks to the low external environmental tem-

perature, it is possible to use the free cooling tech-

nique thus reaching a PUE = 1.2.

5 RELATED WORKS

In the section hereby, the early part analyzes works

falling into energy saving and green energy topics

aimed at datacenter. While in latter part several works

dealing with cloud and federation are reported.

The work we highlight just below show as the

problematic we are trying to address is an hot topic

indeed. Many works dealing with datacenters and

sustainability exist in the scientiﬁc literature, however

our contribution tries to give an answer in the are of

cooperating clouds.

The work in (Wang et al., 2011) highlights an in-

novative cooling strategy that leverages thermal stor-

age to cut the electricity bill for cooling. The authors

claimed the system does not cause servers in a dat-

acenter to overheat. They worked on Computational

Fluid Dynamics (CFD) to consider the realistic ther-

mal dynamics in a datacenter with 1120 servers.

A Workload Distribution for Internet datacenters

is proposed in (Abbasi et al., 2010), where the server

provisioning algorithm is aware of the temperature

distribution in a DC. The authors try to ﬁnd a way

where the utilization constraints (in term of capacity

and performance constrains) are satisﬁed and energy

consumption is minimized.

Modeling a thermal behavior of a datacenter is a

challenging work due to the high number of physi-

cal parameters need to be considered. An interest-

ing model along with a close-loop control system is

described in (Zhou and Wang, 2011). The authors

assessed a datacenter with many CRACs. The inlet

temperature of many racks is investigated for accom-

CLOSER2013-3rdInternationalConferenceonCloudComputingandServicesScience

plishing the Partition in Zone of a datacenter for an

efﬁcient decentralized control.

5.1 Cloud and Federation

In this paragraph, we provide an overview of cur-

rently existing solutions in the ﬁeld of Cloud Federa-

tion, taking into account initiatives born in academia

and major research projects. Most of the work in the

ﬁeld concerns the study of architectural models able

to efﬁciently support the collaboration between dif-

ferent cloud providers focusing on various aspects of

the federation.

In our previous work (Celesti et al., 2010)

we describe an architectural solution for federation

by means of a Cross-Cloud Federation Manager

(CCFM), a software component in charge of exe-

cuting the three main functionalities required for a

federation. In particular, the component explicitly

manages: i) the discovery phase in which informa-

tion about other clouds are received and sent, ii) the

match-making phase performing the best choice of

the provider according to some utility measure and

iii) the authentication phase creating a secure channel

between the federated clouds.

In (Buyya et al., 2010), the authors propose a

more articulated model for federation composed of

three main components. A Cloud Coordinator man-

ages a speciﬁc cloud and acts as interface for the ex-

ternal clouds by exposing well-deﬁned cloud opera-

tions. The Cloud Exchange component implements

the functionality of a registry by storing all necessary

information characterizing cloud providers together

with demands and offers for computational resources.

The dissertation in (Kiani et al., 2012) describes

the large-scale context provisioning. The authors re-

marked that the adoption of context-aware applica-

tions and services has proved elusive so far, due to

multi-faceted challenges in cloud computing area. In-

deed existing context aware systems are not ideally

placed to meet the domain objectives, and facilitate

their use in the emerging cloud computing scenarios.

The use of a predominant focus upon designing for

static topologies of the interacting distributed com-

ponents. Presumptions of a single administrative do-

main or authority and context provisioning within a

single administrative, geographic or network domain.

6 CONCLUSIONS

Nowadays, a sensitive problem is ﬁnding the right

combination between high performance datacenter

and energy sustainability. In this work, considering a

scenario of cloud federation, we proposed a method-

ology for enabling sustainable cooperating clouds.

Considering photovoltaic energy generation systems,

our approach is based on an energy and temperature-

driven strategies in which the computation workload

of a cloud is moved toward the most efﬁcient sus-

tainable federated cloud. According to such a strat-

egy and considering a federated CLEVER-based sce-

nario, we deﬁned an algorithm for the management of

VM allocation according to energy and temperature-

driven policies. In future works, we plan to consider

also heterogeneous cooperating clouds.

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EnergySustainabilityinCooperatingClouds