Interconnecting Smart Grids and Clouds to save Energy

Anne-C

ecile Orgerie

CNRS, IRISA, Rennes, France

Keywords:

Energy Efﬁciency, Cloud Computing, Smart Grids.

Abstract:

Cloud computing is becoming an essential component for Internet services. However, its energy consump-

tion has become a key environmental and economic concern. The distributed nature of Cloud infrastructures

involves that their components are spread across wide areas, interconnected through different networks, and

lenging. In this paper, we present the opportunity brought by the Smart Grids to exploit renewable energy

availability and to optimize energy management in distributed Clouds. The presence of smart sensors which

are both integrated into the electricity Grid and connected to the Internet, indeed offers for the ﬁrst time the

possibility of exploiting the availability of various energy sources, and of making complete energy measure-

ments of all the Cloud resources – computing, storage and especially networking resources – problems which

have previously been intractable.

1 INTRODUCTION

Global warming, oil reserve limits and ever-growing

electricity consumption are all powerful motivations

for designing more energy-efﬁcient systems. The en-

ergy consumed to power Information and Communi-

cation Technologies (ICT) has become a major con-

cern. Data centers and network infrastructures repre-

sent an ever-growing part of this consumption (Mills,

2013). As an example, for 2010, Google used

900,000 servers and consumed 1.9 billion kWh of

electricity (Koomey, 2011). Electricity becomes the

new limiting factor for deploying data center equip-

ment. Indeed, the energy consumption of state-of-the-

art servers grows inexorably as they embed more and

more powerful cores and advanced features and tech-

nologies. So, electricity consumption is hampering

technological advances. It also raises major environ-

mental, economic and societal concerns. Curbing this

consumption is thus crucial and urgent.

In this paper, we mainly aim at investigating how

to exploit the capabilities of Smart Grids technologies

in the context of Cloud computing in order to save en-

ergy. This has the potential for reducing global energy

consumption and thus, inducing real economic, envi-

ronmental, and societal beneﬁts. On the Cloud’s side,

we also aim at increasing the user’s energy-awareness

and the Cloud’s energy-efﬁciency which are the main

levers needed to trigger substantial involvement from

Cloud providers and designers in order to reduce the

electricity demand, and contribute to a more sustain-

able ICT industry.

On the generation side, it is estimated that 10%

of electric energy produced by power plants is cur-

rently lost during transmission and distribution to

consumers, with 40% of these losses occurring on the

distribution network (Feng et al., 2009). As an ex-

ample, in 2006 in the United-States, the total energy

losses and distribution losses were about 1,638 billion

and 655 billion kWh, respectively (Feng et al., 2009).

Smart Grids provide the means to control the energy

supply more efﬁciently and to dynamically manage

peak load. We argue that their utilization will greatly

improve understanding of the energy consumption of

Clouds and will help to reduce it signiﬁcantly.

The remainder of the paper is organized as fol-

lows. Section 2 presents the context and motivation,

which is further discussed over a canonical example

on Section 3. Section 4 illustrates the problem com-

plexity. The related work is presented in Section 5.

Section 6 concludes and presents our future work.

2 CONTEXT AND MOTIVATION

Recently, the maturity of virtualization techniques has

led to the emergence of Cloud Computing, which

is a new paradigm becoming increasingly essential

376

Orgerie A..

Interconnecting Smart Grids and Clouds to save Energy.

DOI: 10.5220/0005484903760381

In Proceedings of the 4th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS-2015), pages 376-381

ISBN: 978-989-758-105-2

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

to Information Technology (IT) services. It aims

to provide dynamic, reliable, customized and QoS-

guaranteed environments to end-users. By putting to-

gether individual requirements and by gathering the

needed equipment, Cloud providers can efﬁciently

manage and offer virtually unlimited resources, mini-

mizing the costs incurred by organizations when pro-

viding Internet services.

To offer better QoS and support more users,

Clouds are distributed: they consist in several data

centers geographically distributed. Computer net-

works are the crucial elements that interconnect the

data centers providing the computing and storage ca-

pacities of these distributed Clouds. As the size of

Clouds increases and their trafﬁc demands diversify,

computer network resources, inside and in-between

the data centers, are often stretched to their limits and,

in many cases, become a performance bottleneck. Be-

sides, they represent a non negligible part of the en-

ergy consumption of these distributed systems (Org-

erie et al., 2014).

Advances in distributed systems have historically

been related to improving their performance, scala-

bility and quality of service. Yet, it is now urgent

to drastically increase the efﬁciency of large-scale

distributed systems in order to curb the rising en-

ergy consumption of ICT (Information and Commu-

nications Technologies). Indeed, a recent study fo-

cused on Clouds (Mills, 2013) estimates that the ICT

ecosystem now approaches 10% of world electric-

ity generation. The authors promise a faster growth

in ICT energy use, pointing out the information ap-

petite of Big Data, big networks and big infrastructure

which unavoidably leads to big power.

Smart Grid sensors are the monitoring devices of

Smart power Grids. They provide high ﬁdelity control

through high-speed, two-way communication, sens-

ing, and real-time coordination of all assets down to

the customer meter and the end-user devices (Pratt

et al., 2010). As they are literally located at the in-

tersection between the electrical and communication

networks, smart sensors enable improvement in the

stability, efﬁciency, ﬂexibility, and reliability of elec-

tricity services. Through their communicating sen-

sors, they offer better support of renewable energy

and more comprehensive views of the consumption

of infrastructures. As for the Smart Grid actuators,

the operating counterpart of smart sensors, they can

act directly on the devices to enforce energy-efﬁcient

policies (Hledik, 2009). For instance in smart homes,

they can adjust air conditioning level according to the

parameters detected by the smart sensors: the temper-

ature, the operation of appliances producing heat and

human presence.

While the design of smart actuators is under study,

smart sensors starts to be deployed at the scale of en-

tire cities (like in Lyon, France, with the smart city

project for instance). This deployment comes along

with a Big Data challenge since the collected data rep-

resent huge amounts, coming from distributed places,

and requiring cross-computations. While Clouds

come naturally to the rescue of Smart Grids for deal-

ing with this big data issue, little attention has been

paid to the beneﬁts that Smart Grids could bring to

Clouds.

We propose exploiting Smart Grid technologies to

come to the rescue of energy-hungry Clouds. Unlike

in traditional electrical distribution networks, where

power can only be moved and scheduled in very lim-

ited ways, Smart Grids dynamically and effectively

adapt supply to demand. This recent technology of-

fers the unique chance to monitor the energy con-

sumption in real-time of entire and distributed infras-

tructures through their smart sensors, to reduce en-

ergy use through their smart actuators, and to act as

the bridge enabling hitherto impossible joint energy

production/consumption synergies.

3 A CANONICAL EXAMPLE

When a user submits a Google request (such as a

search for instance), it is routed to a data center that

processes it, computes the answer and sends it back

to the user. Google owns several data centers spread

across the world. For performance reasons, the cen-

ter answering the user’s request is more likely to be

the one closest to the user. However, this data cen-

ter may be supplied by a far away energy source. A

more distant data center may be using a renewable en-

ergy source which happens to be close by and avail-

able at the time of the request. This request may have

consumed less energy, or a different kind of energy

(renewable or not), if it had been sent to this further

data center. In this case, the response time would have

been increased but maybe not noticeably: a different

trade-off between performance and energy-efﬁciency

could have been adopted.

However, two issues make this trade-off impos-

sible to determine: 1) the cloud (hosting Google’s

services here) is not able to estimate the energy con-

sumption of a user’s request because it implies vari-

ous and distributed resources (several servers to pro-

cess the request and communication networks to carry

the request and the answer), and 2) the cloud has no

means of knowing where the energy for its data cen-

ters comes from, and whether it is sufﬁciently avail-

able at a given time. Such data could be provided by

InterconnectingSmartGridsandCloudstosaveEnergy

377

Communication

Network

Electrical

Network

User Router Power stations Data center

Hardware

Resources

Smart Grid

layer

Cloud

layer

Figure 1: Representation of the involved resources and layers on the example of a worldwide Cloud.

the smart sensors included in Smart Grid infrastruc-

tures. Smart sensors and Cloud’s knowledge about its

utilization could be combined to take near real-time

allocation decisions for the Cloud’s resources, and en-

ergy management optimizations through the smart ac-

tuators for the electrical Grid.

4 ILLUSTRATION OF THE

PROBLEM COMPLEXITY

Figure 1 shows an example of a large-scale Cloud in-

frastructure. Its hardware layer is constituted of two

physical components: data centers to process the user

requests and communication network resources (i.e.

routers) to transport the requests and to link the data

centers together. Electricity, which is also a physical

resource, is distributed through the electrical network

from power stations to data centers and network re-

sources.

But, as shown in Figure 1, the electrical network

and the communications network of a Cloud infras-

tructure are not directly interconnected. So, the Cloud

manager has no access to the electrical information.

This implies two inherent problems for the Cloud

manager which aims to optimize the energy consump-

tion of the entire Cloud infrastructure: 1) it has no

way to know the consumption of the networking re-

sources as they belong to other (numerous) compa-

nies; 2) it has no access to the electricity availability

in the power stations used by its resources.

As illustrated in Figure 1, Smart Grids constitute

the autonomic management layer that enables cooper-

ation among the electrical network and the communi-

cation network. Our goal is to make the Cloud layer

and the Smart Grid layer cooperate in order to save

energy in the Cloud infrastructures (with its underly-

ing physical resources) and in the electrical network.

As pointed out in the ﬁgure, the communication net-

work will play a key role in this cooperation as it

physically stands in between.

Smart Grids have been designed to be deployed at

large scale in order to improve the efﬁciency of the

production and distribution of electricity in a compre-

hensive context. Although some smart sensors (home

smart meters for instance) are already available, smart

actuators are still under development. This currently

leaves Smart Grids with a measurement role only (ex-

ploited for a smarter accounting for instance), and

considerably limits the power of Smart Grids, which

are here simply able to collect more detailed monitor-

ing information than regular power grids. Thus, the

current status of deployed Smart Grid technologies

is not allowing them yet to dynamically adapt sup-

ply to demand. Nonetheless, we believe that they will

strengthen in the forthcoming years in order to answer

to the urgent need of smarter infrastructures for sav-

ing energy.

In this paper, we limit the investigation of the po-

tential impact of Smart Grids to distributed Cloud in-

frastructures, impact which has not been estimated yet

to the best of our knowledge. Interconnecting Cloud

computing infrastructures and Smart Grid technolo-

gies is not straightforward due to the complexity of

these two systems. It requires to face multi-parameter

modeling and multi-objective optimization challenges

with distributed algorithmic and real-time constraints.

A ﬁne-grained modelization of the considered Cloud

applications is also needed in order to be able to ac-

comodate users’ expectations in terms of QoS and in

order to anticipate the impact of infrastructure recon-

ﬁgurations and optimization algorithms.

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5 RELATED WORK

Large-scale distributed systems – such as computing

data centers, Grids, Clouds and dedicated networks –

consist of vast collections of computing and storage

resources interconnected through a network. These

systems, with different levels of scalability, interoper-

ability and respect to user constraints, have become

the building blocks for numerous applications and

services ranging from weather forecast to web search.

Cloud infrastructures consist in geographically

distributed data centers which are linked through

communication networks (Wang et al., 2008a). With

the emergence of the Future Internet and the dawning

of new IT models such as cloud computing, the usage

of data centers (DC), and consequently their power

consumption, increase dramatically. As an example,

for 2010, Google used 900,000 servers and consumed

1.9 billion kWh of electricity (Koomey, 2011). Other

big Cloud companies present similar ﬁgures and sim-

ilar issues (Katz, 2009).

Electricity becomes the new limiting factor for de-

ploying data center equipment. Indeed, the energy

consumption of state-of-the-art servers grows inex-

orably as they embed more and more powerful cores

and advanced features and technologies. So, elec-

tricity consumption is hampering technological ad-

vances. It also raises major environmental, economic

and social concerns. Curbing this consumption is thus

crucial and urgent.

The ﬁrst way to save energy at a data center level

consist in locating it close to where the electricity is

generated, hence minimizing transmission losses. For

example, Western North Carolina, USA, attracts data

centers with its low electricity prices due to abundant

capacity of coal and nuclear power following the de-

parture of the region’s textile and furniture manufac-

turing (Greenpeace, 2011). As of writing, this region

has three super-size data centers from Google, Apple

and Facebook with respective power demands of 60 to

100 MW, 100 MW and 40 MW (Greenpeace, 2011).

Other companies opt for greener sources of en-

ergy. For example, Quincy (Washington, USA) sup-

plies electricity to data facilities from Yahoo, Mi-

crosoft, Dell and Amazon with its low-cost hydro-

electrics left behind following the shutting down of

the region’s aluminum industry (Greenpeace, 2011).

Several renewable energy sources like wind power,

solar energy, hydro-power, bio-energy, geothermal

power and marine power can be considered to power

up super-sized facilities.

In spite of these approaches, numerous data facil-

ities have already been built and cannot be moved.

Cloud infrastructures, on the other hand, can still take

advantage of multiple locations to use green sources

of energy with approaches such as follow-the-sun and

follow-the-wind (Figuerola et al., 2009). As sun and

wind provide renewable sources of energy whose ca-

pacity ﬂuctuates over time, the rationale is to place

computing jobs on resources using renewable energy,

and migrate jobs as renewable energy becomes avail-

able on resources in other locations.

Within the data center itself, a range of tech-

nologies can be utilized to make cloud computing

infrastructures more energy efﬁcient, including bet-

ter cooling technologies, temperature-aware schedul-

ing (Fan et al., 2007), Dynamic Voltage and Fre-

quency Scaling (DVFS) (Snowdon et al., 2005), and

resource virtualization (Talaber et al., 2009). The

use of VMs (Barham et al., 2003) brings several

beneﬁts including environment and performance iso-

lation; improved resource utilization by enabling

workload consolidation; and resource provisioning

on demand. Nevertheless, such technologies should

be analyzed and used carefully for really improv-

ing the energy-efﬁciency of computing infrastruc-

tures (Miyoshi et al., 2002).

Concerning wired networking resources, which

constitute an important part of Cloud infrastructures,

the energy-efﬁcient management techniques can be

divided into two primary categories (Orgerie et al.,

2014; Bolla et al., 2011): sleeping techniques mak-

ing use of future foreseen on/off capabilities of net-

work devices, and rate adaptation techniques. The

most emblematic solution of the sleeping techniques

is named Low-Power Idle (Christensen et al., 2010),

standardized under the name 802.03az, and starting

to be implemented on Network Interface Cards and

switches available on the market. The precursor of

rate adaptation techniques is named ALR (adaptive

Link Rate) (Gunaratne et al., 2006), and proposes to

adapt the negotiated link rates (between the two link

end devices) to the actual load.

As detailed in a survey (Orgerie et al., 2014),

numerous works target resource allocation and task

scheduling with energy constraints for both comput-

ing (Lef

evre and Orgerie, 2010; Goiri et al., 2011),

and networking (Baliga et al., 2011; Bolla et al., 2011;

Betti et al., 2013) resources separately. However, for

both of these disjointed propositions, they either fo-

cus on energy-efﬁcient goals (ie. reducing the amount

of consumed energy), or on energy-aware objectives

(ie. maximizing the amount of consumed renewable

energy). Only few works consider both aims at the

same time, like it is the case in this position paper;

and none considered both aims for both kind of re-

sources: computing and networking.

The Cloud workload varies widely on a short time

InterconnectingSmartGridsandCloudstosaveEnergy

379

scale (Elson and Howell, 2008). To quickly and de-

centrally adapt Cloud resource allocation to renew-

able energy availability, efﬁcient real-time autonomic

reconﬁguration mechanisms are required. Centralized

solutions have been proposed to this issue (Orgerie

et al., 2014). Yet, their scalability is limited, and thus,

their applicability to Cloud infrastructures is arguable.

In order to solve this technical barrier, we argue for

the use of Software-Deﬁned Networking techniques.

These techniques allow for virtual networks decou-

pling the control (routing messages for instance) and

data planes(users’ messages) (Jain and Paul, 2013).

OpenFlow is currently the mostly used SDN technol-

ogy (Lara et al., 2014).

Network virtualization is also a promising solu-

tion to enable energy savings (Orgerie et al., 2014).

Virtual ROuters On the Move (VROOM), a network

management primitive, pioneering research work, is

presented in (Wang et al., 2008b). It simpliﬁes the

management of routers by virtualizing them and al-

lowing them to freely move from one physical node to

another, thus avoiding logical topology changes. The

migration of virtual routers can also be used to save

energy (Chen and Phillips, 2013).

On the electricity side, it is estimated that 10%

of electric energy produced by power plants is cur-

rently lost during transmission and distribution to

consumers, with 40% of these losses occurring on the

distribution network (Feng et al., 2009). As an ex-

ample, in 2006 in the United-States, the total energy

losses and distribution losses were about 1,638 billion

and 655 billion kWh, respectively (Feng et al., 2009).

Smart Grids provide the means to control the energy

supply more efﬁciently and to dynamically manage

peak load. Their utilization has the potential to greatly

improve understanding of the energy consumption of

Clouds and to help to reduce it signiﬁcantly.

To take advantage from this new technology, we

need to deal with three major issues: 1) complexity in-

curred by the proliferation of energy sensors, the fact

that different systems are interconnected (communi-

cation networks and distribution networks), these sys-

tems are highly multi-scale by nature, different tech-

nologies are employed (different manufacturers), and

a lot of parameters are involved (energy sources and

availability, location, etc.); 2) interdependability be-

cause our solution relies on the collaboration between

electricity networks and communication networks; 3)

performance of the system: it is mandatory for the

monitoring infrastructure to provide real-time anal-

ysis in spite of the large data volumes to process,

and for the Cloud management system to integrate

distributed management and failure recovery mech-

anisms.

Current works on Smart Grids are dealing with

smoothing energy peak demand (Tang et al., 2013),

improving the effective utilization of intermittent and

ﬂuctuating renewables, and improving the scalability

of the proposed solutions (Fang et al., 2012). Our goal

here is not to contribute to the development and tech-

nical advances of Smart Grids. It only consists in uti-

lizing this emerging and innovative technology.

6 CONCLUSION AND FUTURE

WORK

Most of the work on energy-efﬁcient management

of Clouds does not consider networking resources as

they go beyond the limits of the data centers. Thus,

they are not entirely managed by one provider and

their consumption is not accessible to the cloud man-

ager. Yet, the intrinsic energy consumption of the

Google request includes transporting the request to

a data center, computing the request and transport-

ing the answer back to the user. Taking into account

only the computing costs leads to proposing optimiza-

tion algorithms that do not optimize the overall energy

consumption, but only the consumption of one data

center. Indeed, network resources account for a non-

negligible part in the energy equation (Orgerie et al.,

2014; Orgerie et al., 2011). Making Smart Grids and

Cloud managers collaborate could solve these issues

and lead to important energy savings.

Our future work will focus on exploiting this en-

ergy saving potential resulting from the interconnec-

tion of Smart Grids and Clouds. This challenging goal

will require to propose implementable distributed re-

conﬁguration mechanisms and task scheduling algo-

rithms for the allocation of Cloud resources (both

networking and computing resources) taking into ac-

count energy efﬁciency and utilization of renewable

energies. It will also led us to dsign an efﬁcient man-

agement of electricity distribution and utilization in

the context of distributed energy sources linked by

Smart Grids and used to power a distributed Cloud

infrastructure. Working towards this objective should

both increase the knowledge about these two complex

systems – namely Cloud infrastructures and Smart

Grids – and remove technological barriers towards

their interconnection for coping with the big power

issue.

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