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doi:10.1093/comjnl/bxp080
Energy-Efficient Cloud Computing
Andreas Berl1,∗, Erol Gelenbe2, Marco di Girolamo3, Giovanni Giuliani3,
Hermann de Meer1, Minh Quan Dang4and Kostas Pentikousis5
1Fakultät für Informatik und Mathematik, University of Passau, Innstr. 43, 94032 Passau, Germany
2Electrical and Electronic Engineering Department, Imperial College London, South Kensington Campus,
London SW7 2AZ, UK
3HP-European Innovation Centre, HP IIC (Italy Innovation Centre), Italy
4School of Information Technology, International University in Germany, Bruchsal, Germany
5VTT Technical Research Centre of Finland, Kaitoväylä 1, FI-90571 Oulu, Finland
∗
Corresponding author: berl@uni-passau.de
Energy efficiency is increasingly important for future information and communication technologies
(ICT), because the increased usage of ICT, together with increasing energy costs and the need to
reduce green house gas emissions call for energy-efficient technologies that decrease the overall
energy consumption of computation, storage and communications. Cloud computing has recently
received considerable attention, as a promising approach for delivering ICT services by improving the
utilization of data centre resources. In principle, cloud computing can be an inherentlyenergy-efficient
technology for ICT provided that its potential for significant energy savings that have so far focused
on hardware aspects, can be fully explored with respect to system operation and networking aspects.
Thus this paper, in the context of cloud computing, reviews the usage of methods and technologies
currently used for energy-efficient operation of computer hardware and network infrastructure.After
surveying some of the current best practice and relevant literature in this area, this paper identifies
some of the remaining key research challenges that arise when such energy-saving techniques are
extended for use in cloud computing environments.
Keywords: energy-efficient computing and networking; energy-aware data centres; cloud computing
Received 28 July 2009; revised 28 July 2009
Handling editor:
1. INTRODUCTION
Significant savings in the energy budget of a data centre,
without sacrificing service level agreements, are an excellent
economic incentive for data centre operators, and would also
make a significant contribution to greater environmental sus-
tainability. According to Amazon.com’s estimates [1], at its
data centres (as illustrated in figure 1), expenses related to
the cost and operation of the servers account for 53% of the
total budget (based on a 3-year amortization schedule), while
energy-related costs amount to 42% of the total, and include
both direct power consumption (∼19%) and the cooling
infrastructure (23%) amortized over a 15-year period.
Dennis Pamlin, the Global Policy Advisor of WWF, Sweden
[2] highlighted different IT solutions and their beneficial
impact on green house gases (GHG), which include CO2,
emissions. These opportunities include IT- based solutions:
e.g. smart buildings, smart transportation and communication,
smart commerce and services, and smart industrial production.
The colloquial term ‘smart’ in this case means ‘with low
carbon footprint’, showing that the adoption of such ‘smart’
IT solutions will enable a potentially large GHG reduction,
including information and communication technologies (ICT)
itself which is a large power consumer (and therefore a GHG
emitter), and IT solutions that have a huge potential impact in
reducing GHG emissions in many sectors (Fig. 1).
Based on a recent ‘Data Centre Energy Forecast Report’[3], it
can be expected that savings of the order of 20% can be achieved
in server and network energy consumption with respect to
current levels, and that these savings may induce an additional
30% saving in cooling needs as detailed in a study by HP and the
Uptime Institute [4]. It shows that ‘most of data centre power
is spent on cooling ICT equipment (between 60 and 70%)’.
Thus there are very significant economic and environmental
gains to be obtained from a serious research thrust on energy
efficiency in the general area of IT and computer networks.
In particular, cloud computing is an inherently energy-efficient
virtualization technique [5], in which services run remotely
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FIGURE 1. Energy distribution in the data centre.
in a ubiquitous computing cloud that provides scalable and
virtualized resources. Thus peak loads can be moved to other
parts of the cloud and the aggregation of a cloud’s resources can
provide higher hardware utilization.
The rest of this paper is organized as follows.After reviewing
the current state of energy efficiency in ICT in Section 2, we
discuss the salient aspects of energy-efficient cloud computing
in Section 3. In Section 4, we detail the main research challenges
that lie ahead, and provide concluding remarks in Section 5
2. CURRENT STATE OF ENERGY EFFICIENCY IN
ICT INFRASTRUCTURES
ICT consumes an increasing amount of energy, but is also
instrumental in increasing productivity and economic prosperity
and in reducing energy expenditure from other sources through
e-work, e-commerce and e-learning. Traditional network design
has sought to minimize infrastructure costs and maximize
quality of service (QoS). However, ICT also plays a complex
role in energy consumption via the ‘communicate more and
travel less’paradigm, as well as through the use of smart devices
in homes and offices to optimize energy management. Thus, ICT
can reduce energy consumption and carbon emissions, but this
potential reduction is partially offset by the power used by data
centres and computer networks [6] which runs into billions of
dollars or euros. Thus, a fraction of energy savings in ICT and
networks could lead to significant financial and carbon savings.
In this section, we review recent research in energy efficiency
for standalone hardware, and then review work that considers
energy consumption as part of the cost functions to be used
for scheduling in multiprocessor and grid architectures. Finally,
we briefly review energy consumption in cluster servers and
wired/wireless networks.
2.1. Energy-efficient hardware
One of the approaches to increase the energy efficiency is to
develop more energy-efficient hardware. This effort is fostered
by labels such as the US Energy Star [7] or the European
TCO Certification [8] which rate IT products (mostly monitors)
according to their environmental impact. Novel emerging
technologies such as solid-state discs are content with much
less energy than the currently used hard disc drives. Computer
power can be saved by means of various well-known techniques.
First, the processor can be powered down by mechanisms like
SpeedStep [9], PowerNow [10], Cool’nQuiet [11] or Demand-
Based Switching [12]. These measures enable slowing down
CPU clock speeds (clock gating), or powering off parts of the
chips (power gating), if they are idle [13,14]. By sensing the
lack of user–machine interaction, different redundant hardware
parts can incrementally be turned off or put in hibernating mode
(display, disc etc.).
The advanced configuration and power interface (ACPI)
specification [15] defines four different power states that an
ACPI-compliant computer system can be in. These states range
from G0-working to G3-mechanical-off. The states G1 and G2
are subdivided into further substates that describe which com-
ponents are switched off in the particular state. For devices and
the CPU, separate power states (D0–D3 for devices and C0–C3
for CPUs) are defined, which are similar to the global power
states. Some of the mentioned techniques are usually applied
to mobile devices but can be used for desktop PCs as well.
2.2. Energy-aware scheduling in multiprocessor
and grid systems
Energy-aware scheduling in multiprocessor and grid systems
is a widely discussed problem in the literature as the following
overview shows. In [16] the authors present an energy-aware
method to schedule multiple real-time tasks in multiprocessor
systems that support dynamic voltage scaling (DVS). The key
in their approach is to consider the probabilistic distribution
of the tasks’ execution time in order to partition the workload
and reduce energy consumption. Memory energy consumption
[17] can also be reduced by scheduling techniques that impact
the effectiveness of frequency scaling by combining the effect
of tasks running on a multicore system, including memory
contention and the technical constraint of chip-wide frequency
and voltage settings. The DVS capability [18] addresses energy
minimization for periodic pre-emptive hard real-time tasks
that are scheduled on an identical multiprocessor platform.
AlEnawy and Aydin [18] suggest partitioned scheduling and
assume that the tasks are assigned rate-monotonic priorities.
To solve this problem, they proposed an integrated approach,
including rate monotonic scheduling, an admission control
test, a partitioning heuristic and a speed assignment algorithm.
All the above work proposes to control the energy consumption
of hardware by adjusting voltage levels.
Cong et al. [19] consider energy-efficient scheduling for data
grids supporting real-time and data-intensive applications. They
use both, the location of data and application properties to
design a novel distributed energy-efficient scheduler that aims
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Energy-Efficient Cloud Computing 3
to seamlessly integrate scheduling tasks with data placement
strategies to provide energy savings. The main energy savings
are obtained by reducing the amount of data replication and task
transfers. In [20] an energy-constrained scheduling scheme for a
grid environment is investigated both for energy minimization in
mobile devices and for grid utility optimization, by formalizing
energy-aware scheduling using nonlinear optimization theory
under the constraints of energy budget and the job deadline. In
addition, [20] proposes a distributed pricing scheme that makes
trade-offs between energy and deadlines to achieve a system-
wide optimization based on the preference of the grid user.
Another approach that increases energy efficiency in data
centres is based on server consolidation by service virtualization
[21–25]. Virtualization partitions computational resources and
allows the sharing of hardware. Many services often need
only a small fraction of the available computational resources
[26] of a data centre server. However, even when run at a
low utilization, servers typically need up to 70% of their
maximum power consumption [27]. Such services can be
virtualized and run within a virtual machine (VM) resulting
in significant increases in overall energy efficiency. Depending
on their utilization, many VMs can run on a single hardware
unit (server consolidation). Therefore, less hardware is needed
overall, thus reducing energy wasted for cooling, while the
deployed hardware utilization increases. This consolidation of
shared hardware fosters energy efficiency, measured as work
accomplished per unit of consumed energy [28].
Resources can be virtualized on different layers and imple-
ment different forms of virtualization: full virtualization, hosted
virtualization or operating system (OS) layer virtualization [29].
When system virtualization [30] (e.g. full virtualization) is sup-
ported, the virtualization software emulates full-featured hard-
ware and runs on top of the local OS. In the paravirtualization
approach, guest VMs are modified in order to perform so-called
‘hyper calls’ instead of system calls, leading to higher perfor-
mance of VMs [31], as used, for instance, in the XEN systems.
In XEN 3.0 [32] guests can be virtualized without being mod-
ified by using virtualization support of X86 CPUs. OS layer
virtualization has been proposed in the Linux-VServer [33,34],
a kernel-based virtualization.
It is important to point out that virtualization comes at a
cost which needs to be managed efficiently. When resources are
virtualized, additional management of VMs is needed to create,
terminate, clone or move VMs from host to host. Migration of
VMs can be done off-line (the guest in the VM is powered off)
or on-line (live migration of a running VM to another host).
The management solution infrastructure 3 [35] of VMWare, for
instance, supports live migration.
2.3. Power minimization in clusters of servers
Recent research has considered power minimization in server
clusters, with guaranteed throughput and response time [36].
Energy consumption depends primarily on CPU utilization, but
components, such as discs, memory and network devices, also
use energy so that a server that seems to remain idle may still use
up to 60% of its peak power. In [37] policies are developed that
use economic criteria and energy as criteria to despatch jobs to a
small set of active servers, while other servers are down to a low-
power state. Similar dynamic provisioning algorithms [38] are
studied for long-lived TCP connections as in instant messaging
and gaming. A queuing approach to [39] dynamic provisioning
technique has also been studied to obtain the minimum number
of servers required to respect the required QoS and reactive
provisioning can be used to compensate for sudden surges
in load.
2.4. Power minimization in wireless and wired networks
According to some estimates, ‘the Internet’ may by consuming
more than 860 TWh annually [40], but such figures can only
be considered as educated guesses due to the number of
assumptions one has to make. Traditionally fixed network
operators have not considered energy consumption as a major
cost factor. Lately, however, as sustainability is becoming a
key business objective, fixed network operators are looking
for ways to decrease their energy footprint. On the contrary,
wireless network operators due to regulatory requirements and
operational considerations regarding base station deployments
have been trying to minimize energy consumption for over a
decade. In fact, it is reported that [41] the radio access network
(rather than the core network) is the most energy consuming part
of the infrastructure, and in many cases the associated energy
bills are comparable to the total costs for the personnel who
work on network operations and maintenance. The ICT energy
estimates in [41] report that the Vodafone Group radio access
network alone consumed nearly 3 TWh in 2006.
Surprisingly enough, energy savings for infrastructure
networks have not received much attention until very recently,
while energy-saving routing protocols in wireless sensor
networks have already been studied in detail [42–44] because of
the specific needs of battery powered networks and the related
research has included the use of topology control [45,46]
that modify the network graph to optimize properties such as
network capacity and QoS. Since processing and transmission
power in nodes are the essential consumers of energy, it is
also necessary to optimize the number of hops traversed by
packets. An interesting trade-off then arises between high
transmission power which can reduce the number of hops,
low transmission power which can lead to more hops being
necessary due to shorter ranges and transmission interference
which can be affected by power in a complex manner. Related
work can be found in [42,43] where the idea of turning
nodes on and off is also considered. In a wired node, power
consumption depends and influences other factors, such as the
node’s throughput; furthermore, up to 60% of a node’s energy
consumption can originate with peripheral devices such as link
drivers. Turning wired network nodes on and off may be very
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difficult in a wired context because of the high volumes, rate of
traffic and the stringent QoS constraints. Routing for wireless
ad hoc networks with battery-powered nodes [47] have also
been considered in the selection paths so as to satisfy QoS
constraints and minimize power. Generally speaking, we feel
that the research community has begun to seriously consider
energy consumption in infrastructure networks, and the IEEE
has now focused on developing a standard for energy-efficient
Ethernet (IEEE 802.3 az).
In a future Internet where cloud computing may become
a mainstay for economic growth, businesses and individuals
will require energy-efficient operation that involves not only
computation and storage facilities but networking as well. It is
further anticipated that the majority of users will access cloud
computing resources from mobile, battery-powered devices,
which impose stringent limitations on power consumption.
Clearly, one therefore needs to address not just the issues arising
from individual components (such as storage and processing
elements), resource utilization algorithms (job scheduling,
virtualization, migration) and topology considerations, but the
entire chain of services and infrastructure enablers.
3. TOWARDS ENERGY-EFFICIENT CLOUD
COMPUTING
The previous discussion highlights the need to develop a
comprehensive approach for energy efficiency that involves all
system layers and aspects, including physical nodes, cooling
of nodes, networking hardware, communication protocols and
finally the servers and services themselves. The conceptual
framework of cloud computing may therefore be a way forward
to analyze, identify and implement overall energy savings in a
system to attain truly ‘green’ computing services.
In contrast to hardware-oriented optimization, software
systems can potentially be optimized at development time by
specifying their energy characteristics and by adapting the
implementation. However, this requires individual adaptation
of each component and it also requires understanding the
interaction between individual components when they operate
as a system. A major challenge is therefore to explore the
relations among system components and the trade-offs that can
result in an optimal balance between performance,QoS and
energy consumption and include self-aware runtime adaptation
[40,48–50]. Thus in this section we briefly discuss some
areas of energy-efficiency research based on a cloud computing
perspective.
Significant energy savings can result from using energy-
aware scheduling mechanisms pervasively throughout a system.
Our survey in the previous sections shows that progress has
been made in this area, but that much more needs to be done
in holistically examining where and how such mechanisms are
needed, in applying the available mechanisms, and inventing
and evaluating new ones as needed. In the case of any one
service provider site, algorithms to multiplex and de-multiplex
workload in order to save energy are needed, and they
should incorporate the trade-off between performance and the
reduction in service cost due to energy savings. In addition to
scheduling and the mapping of workflows, the improvement
of energy-aware cloud applications themselves can also benefit
from software optimization.
In a business environment based on cloud computing,
workflows that run over many sites will tend to be popular.
Thus, developing methods that map the workflowonto resources
under the constraint of energy optimization becomes a central
problem of great value and novelty. Furthermore, in order
to comprehensively raise the energy efficiency of a system,
all of its layers have to be considered, including application
layer services. Services have different needs concerning the
environment they are running on or have special properties that
support the energy efficiency of the underlying system (e.g.
certain usage patterns). A service, for instance, might only be
used weekdays, say, from 8 to 18h or have peak usage at a
certain time of the day. A user also may also consider a trade-
off between a more energy-efficient service and a more reliable
or faster service, and compose the service in a way that fits its
needs. Thus it should also be possible to develop accounting
mechanisms that track and depend on the energy that has been
used by a service.
3.1. Energy-aware data centres
The key current technology for energy-efficient operation of
servers in data centres is virtualization. VMs that encapsulate
virtualized services can be moved, copied, created and deleted
depending on management decisions. Consolidating hardware
and reducing redundancy can achieve energy efficiency. Unused
servers can be turned off (or hibernated) to save energy.
Some hardware gets higher load, which reduces the number
of physical servers needed. However, the degree of energy-
efficient self-management in data centres is still limited today.
Services should not only be virtualized and managed within
a data centre site but they should be moved to other sites if
necessary. Not only the aspect of load has to be considered,
also the ‘heat’ generated by a service has to be measured and
accounted for before migrating operations. Every operational
physical node produces heat. When a particular node is
excessively used or is near other high-loaded nodes, hotspots
can appear in a given data centre. To avoid such hotspots,
heat can be distributed across sites. Furthermore, services can
be moved from sites with high load or high temperature to
sites with smaller loads and lower temperatures. Generally,
services should be moved to those locations, where they
can operate in the most energy-efficient way. This kind of
energy-efficient management of resources has to be realized
by an autonomous energy management that is as transparent
as possible to the user of a service. Energy-related problems
have to be solved according to defined policies without needing
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Energy-Efficient Cloud Computing 5
human interaction. Machine-readable descriptions of the needs
and features of services, servers, networks and even whole sites
have to be available to enable energy efficiency in the highly
autonomous and adaptive [48] systems of the future.
3.2. Energy savings in networks and protocols
Research has shown that communications, in particular, is one of
the largest consumers of energy, however, energy optimization
for communications must deal with the trade-offs between
performance, energy savings and QoS [51].
Some hardware already offers features that create an
opportunity for energy-efficient operation such as turning
off network interfaces and throttling of processors. Network
protocols could also be optimized, or even be redeveloped in a
way that enhances the energy-efficient operation of the network
elements. Network devices could be enabled to delegate services
to other devices so as to transfer services from energy inefficient
to more energy-efficient devices or to devices that need to
be always on, while certain other devices are turned off. The
delegating device can then become dormant and be turned
off. Currently, many basic network services have to remain
active to periodically confirm their availability even when no
communication is taking place. These ‘soft states’ make it
impossible to turn off certain system components; therefore,
new protocols need to be designed to work around such soft
states so as to increase the energy efficiency of the network.
Signalling can also be revisited in this context; whereas data
and signalling traffic vary widely, the same technology and
mechanisms are used for both (in so-called in-band signalling).
While signalling needs only low bandwidth but can occur
anytime, data traffic occurs after signalling has taken place,
usually requires high bandwidth and traverses all network layers
up to the application layer, and uses processing power through
multiple layers of the network. Therefore the use of out-of-
band signalling should also be evaluated to design and improve
energy-aware communication protocols.
3.3. The effect of Internet applications
Thus far, we have considered the opportunities offered by cloud
computing as a possible foundation for energy-efficient ICT
infrastructures but have not discussed the nature of the applica-
tions themselves. We note that one large application area for the
Internet is in information dissemination. From digital cameras
embedded in mobile phones to environmental sensors to Web
2.0, end users are generating and interconnecting unprece-
dented amounts of information and this trend is expected to
continue unabated. However, the professional, expedited and
reliable distribution of content requires increasing investments
in infrastructure build-out and maintenance, and a matching
electricity bill to run the underlying ICT. Web, peer-to-peer
and web-based video-on-demand services currently dominate
Internet traffic and, taken together, consistently comprise
85% or more of the Internet traffic mix for several years. In
practice, dissemination networks operate using methods and
paradigms based on remote-access, replicating functionality
in several parts of the protocol stack, and fail to benefit from
recent advances in wired and wireless communications, storage
technologies and Moore’s law. If cloud computing becomes a
significant platform for producing and accessing information,
the amount of data that will be transferred over the Internet will
increase significantly. Content replication and dissemination
algorithms will then need to consider energy as a key param-
eter of optimal operation, and therefore cloud computing calls
for a thorough re-examination of the fundamentals of major
computation/communication/storage and energy/performance
trade-offs.
4. CONCLUSIONS
This paper has reviewed the potential impact of energy-
saving strategies for the management of integrated systems that
include computer systems and networks. We have surveyed the
contributions that are available in this area from recent research.
We propose that cloud computing with virtualization as a way
forward to (i) identify the main sources of energy consumption,
and the significant trade-offs between performance, QoS and
energy efficiency and (ii) offer insight into the manner in which
energy savings can be achieved in large-scale computer services
that integrate communication needs. Based on the approaches
that we have identified, we think that specific plug-ins and
energy-control centres for networked large-scale hardware and
software can be implemented and that they can have significant
impact, including:
(i) reducing the software and hardware related energy cost
of single or federated data centres that execute ‘cloud’
applications;
(ii) improving load balancing and hence QoS and
performance of single and federated data centres;
(iii) reducing energy consumption due to communications;
(iv) saving GHG and CO2emissions resulting from data
centres and networks so as to offer computing power
that is ‘environment protecting/conserving’.
Such improvements can have additional impact by reducing
energy utilization for transportation and work by encouraging
‘green’ ICT-based smart solutions for e-work, e-learning and
smart climate control for homes.
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