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Disaster Recovery as a Cloud Service: Economic Benefits & Deployment Challenges


Abstract and Figures

Many businesses rely on Disaster Recovery (DR) services to prevent either manmade or natural disasters from causing expensive service disruptions. Unfortunately, current DR services come either at very high cost, or with only weak guarantees about the amount of data lost or time required to restart operation after a failure. In this work, we argue that cloud computing platforms are well suited for offering DR as a service due to their pay-as-you-go pricing model that can lower costs, and their use of automated virtual platforms that can minimize the recovery time after a failure. To this end, we perform a pricing analysis to estimate the cost of running a public cloud based DR service and show significant cost reductions compared to using privately owned resources. Further, we explore what additional functionality must be exposed by current cloud platforms and describe what challenges remain in order to minimize cost, data loss, and recovery time in cloud based DR services. 1
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Disaster Recovery as a Cloud Service:
Economic Benefits & Deployment Challenges
Timothy Wood, Emmanuel Cecchet, K.K. Ramakrishnan
Prashant Shenoy, Jacobus van der Merwe
, and Arun Venkataramani
University of Massachusetts Amherst
AT&T Labs - Research
{twood,cecchet,shenoy,arun} {kkrama,kobus}
Many businesses rely on Disaster Recovery (DR) services to
prevent either manmade or natural disasters from causing ex-
pensive service disruptions. Unfortunately, current DR services
come either at very high cost, or with only weak guarantees
about the amount of data lost or time required to restart opera-
tion after a failure. In this work, we argue that cloud comput-
ing platforms are well suited for offering DR as a service due
to their pay-as-you-go pricing model that can lower costs, and
their use of automated virtual platforms that can minimize the
recovery time after a failure. To this end, we perform a pricing
analysis to estimate the cost of running a public cloud based DR
service and show significant cost reductions compared to using
privately owned resources. Further, we explore what additional
functionality must be exposed by current cloud platforms and
describe what challenges remain in order to minimize cost, data
loss, and recovery time in cloud based DR services.
1 Introduction
Our society’s growing reliance on crucial computer sys-
tems means that even short periods of downtime can re-
sult in significant financial loss, or in some cases even
put human lives at risk. Many business and government
services utilize Disaster Recovery (DR) systems to mini-
mize the downtime incurred by catastrophic system fail-
ures. Current Disaster Recovery mechanisms range from
periodic tape backups that are trucked offsite, to contin-
uous synchronous replication of data between geograph-
ically separated sites.
A key challenge in providing DR services is to sup-
port Business Continuity
(BC), allowing applications to
rapidly come back online after a failure occurs. By mini-
mizing the recovery time and the data lost due to disaster,
a DR service can also provide BC, but typically at high
cost. In this paper we explore how virtualized cloud plat-
forms can be used to provide low cost DR solutions that
In this work we consider BC to be a stringent form of DR that
requires applications to resume full or partial operation shortly after
a disaster occurs, and we focus on the software and IT infrastructure
needed to support this. In addition, a full BC plan must cover issues
related to physical facilities and personnel management.
are better at enabling Business Continuity.
Virtualized cloud platforms are well matched to pro-
viding DR. The “pay-as-you-go” model of cloud plat-
forms can lower the cost of DR since different amounts
of resources are needed before and after a disaster oc-
curs. Under normal operating conditions, a cloud based
DR service may only need a small share of resources to
synchronize state from the primary site to the cloud; the
full amount of resources required to run the application
only needs to be provisioned (and paid for) if a disas-
ter actually happens. The use of automated virtualization
platforms means that these additional resources can be
rapidly brought online once the disaster is detected. This
can dramatically reduce the recovery time after a failure,
a key component in enabling business continuity.
To explore the potential for using cloud computing as
a DR solution, we perform a basic pricing analysis to
understand the cost of running cloud-based DR for dif-
ferent application types and backup mechanisms. Our
results indicate that some applications can see substan-
tial economic benefits due to the on demand nature of
cloud computing platforms. We discuss under what sce-
narios clouds provide the greatest benefits for DR, and
present the limitations of current cloud platform features
and pricing schemes. Our end goal is to show how cloud
platforms can provide low cost DR services and can be
optimized to minimize data loss and recovery time in or-
der to provide both efficient disaster recovery and busi-
ness continuity.
2 How is DR Done Today?
A typical DR service works by replicating application
state between two data centers; if the primary data cen-
ter becomes unavailable, then the backup site can take
over and will activate a new copy of the application us-
ing the most recently replicated data. In this work we
focus on DR systems with the goal of providing business
continuity–allowing applications to fail over to a backup
site while minimizing service disruptions.
2.1 DR Requirements
This section discusses the key requirements for an ef-
fective DR service. Some of these requirements may be
based on business decisions such as the monetary cost of
system downtime or data loss, while others are directly
tied to application performance and correctness.
Recovery Point Objective (RPO): The RPO of a DR
system represents the point in time of the most recent
backup prior to any failure. The necessary RPO is gen-
erally a business decision—for some applications abso-
lutely no data can be lost (RPO=0), requiring continuous
synchronous replication to be used, while for other appli-
cations, the acceptable data loss could range from a few
seconds to hours or even days.
Recovery Time Objective (RTO): The RTO is an or-
thogonal business decision that specifies a bound on how
long it can take for an application to come back online
after a failure occurs. This includes the time to detect the
failure, prepare any required servers in the backup site
(virtual or physical), initialize the failed application, and
perform the network reconfiguration required to reroute
requests from the original site to the backup site so the
application can be used. Depending on the application
type and backup technique, this may involve additional
manual steps such as verifying the integrity of state or
performing application specific data restore operations,
and can require careful scheduling of recovery tasks to
be done efficiently [7]. Having a very low RTO can en-
able business continuity, allowing an application to seam-
lessly continue operating despite a site wide disaster.
Performance: For a DR service to be useful it must
have a minimal impact on the performance of each appli-
cation being protected under failure-free operation. DR
can impact performance either directly such as in the syn-
chronous replication case where an application write will
not return until it is committed remotely, or indirectly by
simply consuming disk and network bandwidth resources
which otherwise the application could use.
Consistency: The DR service must ensure that after a
failure occurs the application can be restored to a con-
sistent state. This may require the DR mechanism to
be application specific to ensure that all relevant state is
properly replicated to the backup site. In other cases, the
DR system may assume that the application will keep a
consistent copy of its important state on disk, and use a
disk replication scheme to create consistent copies at the
backup site.
Geographic Separation: It is important that the pri-
mary and backup sites are geographically separated in or-
der to ensure that a single disaster will not impact both
sites. This geographic separation adds its own challenges
since increased distance leads to higher WAN bandwidth
costs and will incur greater network latency. Increased
round trip latency directly impacts application response
time when using synchronous replication. As round trip
delays are limited by the speed of light, synchronous
replication is feasible only when the backup site is within
10s of kilometers of the primary. Asynchronous tech-
niques can improve performance over longer distances,
but can lead to greater data loss during a disaster. Dis-
tance can especially be a challenge in cloud based DR
services as a business might have only coarse control over
where resources will be physically located.
2.2 DR Mechanisms
Disaster Recovery is primarily a form of long distance
state replication combined with the ability to start up ap-
plications at the backup site after a failure is detected.
The amount and type of state that is sent to the backup
site can vary depending on the application’s needs. State
replication can be done at one of these layers: (i) within
an application, (ii) per disk or within a file system, or
(iii) for the full system context. Replication at the appli-
cation layer can be the most optimized, only transferring
the crucial state of a specific application. For example,
some high-end database systems replicate state by trans-
ferring only the database transaction logs, which can be
more efficient than sending the full state modified by each
query [8]. Backup mechanisms operating at the file sys-
tem or disk layer replicate all or a portion of the file sys-
tem tree to the remote site without requiring specific ap-
plication knowledge [6]. The use of virtualization makes
it possible to not only transparently replicate the com-
plete disk, but also the memory context of a virtual ma-
chine, allowing it to seamlessly resume operation after a
failure; however, such techniques are typically designed
only for LAN environments due to significant bandwidth
and latency requirements [4, 9].
The level of data protection and speed of recovery de-
pends on the type of backup mechanism used and the na-
ture of resources available at the backup site. In general,
DR services fall under one of the following categories:
Hot Backup Site: A hot backup site typically provides
a set of mirrored stand-by servers that are always avail-
able to run the application once a disaster occurs, provid-
ing minimal RTO and RPO. Hot standbys typically use
synchronous replication to prevent any data loss due to a
disaster. This form of backup is the most expensive since
fully powered servers must be available at all times to
run the application, plus extra licensing fees may apply
for some applications. It can also have the largest impact
on normal application performance since network latency
between the two sites increases response times.
Warm Backup Site: A warm backup site may keep
state up to date with either synchronous or asynchronous
replication schemes depending on the necessary RPO.
Standby servers to run the application after failure are
available, but are only kept in a “warm” state where it
may take minutes to bring them online. This slows re-
covery, but also reduces cost; the server resources to run
the application need to be available at all times, but ac-
tive costs such as electricity and network bandwidth are
lower during normal operation.
Cold Backup Site: In a cold backup site, data is of-
ten only replicated on a periodic basis, leading to an RPO
of hours or days. In addition, servers to run the applica-
tion after failure are not readily available, and there may
be a delay of hours or days as hardware is brought out
of storage or repurposed from test and development sys-
tems, resulting in a high RTO. It can be difficult to sup-
port business continuity with cold backup sites, but they
are a very low cost option for applications that do not
require strong protection or availability guarantees.
The on-demand nature of cloud computing means that
it provides the greatest cost benefit when peak resource
demands are much higher than average case demands.
This suggests that cloud platforms can provide the great-
est benefit to DR services that require warm stand-by
replicas. In this case, the cloud can be used to cheaply
maintain the state of an application using low cost re-
sources under ordinary operating conditions. Only after
a disaster occurs must a cloud based DR service pay for
the more powerful–and expensive–resources required to
run the full application, and it can add these resources
in a matter of seconds or minutes. In contrast, an enter-
prise using its own private resources for DR must always
have servers available to meet the resource needs of the
full disaster case, resulting in a much higher cost during
normal operation.
2.3 Failover and Failback
In addition to managing state replication, a DR solution
must be able to detect when a disaster has occurred, per-
form a failover procedure to activate the backup site, as
well as run the failback steps necessary to revert con-
trol back to the primary data center once the disaster
has been dealt with. Detecting when a disaster has oc-
curred is a challenging problem since transient failures
or network segmentation can trigger false alarms. In
practice, most DR techniques rely on manual detection
and failover mechanisms. Cloud based systems can sim-
plify this problem by monitoring the primary data center
from cloud nodes distributed across different geographic
regions, making it simpler to determine the extent of a
network failure and react accordingly.
In most cases, a disaster will eventually pass, and a
business will want to revert control of its applications
back to the original site. To do this, the DR software
must support bidirectional state replication so that any
new data that was created at the backup site during the
disaster can be transferred back to the primary. Doing
Primary Data Center
DR Cloud
Failover Mode
Replication Mode
Redirect after
Figure 1: RUBiS is configured with 3 web servers and 1
database at the primary site. In ordinary operation the cloud
only requires a single DR server to maintain database state, and
only initializes the full application resources once a disaster oc-
curs. After the failure, client traffic must be redirected to the
cloud site.
this efficiently can be a major challenge: the primary site
may have lost an arbitrary amount of data due to the dis-
aster, so the replication software must be able to deter-
mine what new and old state must be resynchronized to
the original site. In addition, the failback procedure must
be scheduled and implemented in order to minimize the
level of application downtime.
3 DR as a Cloud Service
While there are many types of DR that can be provided
using cloud resources, we focus on a warm standby sys-
tem where important application state is continuously
replicated into the cloud. Figure 1 illustrates this setup
for a web application that requires four servers (one
database and three web servers) in the primary site.
Within the cloud providing DR, the level of resources
required depends on whether it is in Replication Mode
or Failover Mode. During normal operation, the system
stays in Replication Mode, and requires only a single low
cost VM to act as the DR Server that handles the state
synchronization. When a disaster occurs, the system en-
ters Failover Mode, which requires resources to support
the full application. In this section we analyze the costs
of this form of DR and discuss both the benefits and chal-
lenges remaining for DR in the cloud.
3.1 Are Clouds Cheaper for DR?
We first study the costs associated with disaster recovery
services to understand if clouds can actually make DR
cheaper. We compare the cost of running a DR service
Public Cloud Colocation
RUBiS Replication Failover Replication Failover
Servers $2.04 $32.64 $26.88 $26.88
Network $0.54 $18.00 $1.16 $39.14
Storage $1.22 $1.39
Total per day $3.80 $52.03 $28.04 $66.01
Total per year $1,386 $18,992 $10,234 $24,095
99% uptime cost $1,562 per year $10,373 per year
Resource Consumption
Replication Failover
Servers 1 cloud / 4 colo 4
Network 5.4 GB/day 180 GB/day
Storage 30 GB 30 GB
IO 130 req/sec 150 req/sec
Figure 2: (a) Cost per day and year for providing DR services for RUBiS. Under normal operation, only the Replication Mode cost
must be paid, leading to substantial savings when using a cloud platform. (b) Resources required during Replication and Failover
Modes are the same for the cloud and colocation center except that the colo center must always have 4 servers available.
using public cloud resources against a “build your own”
DR service using an enterprise’s own private resources.
To estimate the cost of the latter approach, we use the
price of renting resources from a colocation facility. This
is a reasonable estimate for small to medium size busi-
nesses which may own a single data center but cannot
afford the additional expense of a second full data center
as a DR site.
Our cost study is meant to be illustrative rather than
definitive—we found a wide range of prices for both
cloud and colo providers, and we do not include fac-
tors such as management costs which may not be equiv-
alent in each case. While large enterprises that own
multiple data centers may be able to obtain cheaper re-
sources by running DR between their sites, they will
still face the same cost model as the colocation facil-
ity. Past cost studies indicate that the primary costs of
running a private data center are for purchasing servers
and infrastructure—costs that do not change regardless of
whether servers are actively used or not [5]. In contrast,
the cloud’s pay-as-you-go model benefits users who can
turn resources on and off as needed, which is exactly the
case in disaster recovery services that acquire resources
on demand only after a failure occurs.
3.1.1 Case Study: Multi-tier Web Application
To understand the cost of providing DR in the cloud, we
first consider a common multi-tier web application archi-
tecture composed of several web front ends connected to
a database server containing the persistent state for the
application. This scenario illustrates how some compo-
nents of an application may have different DR require-
ments. The web servers in this example contain only
transient state (e.g., session cookies that can be lost with-
out significantly disrupting the application) and only re-
quire a weak backup policy; we assume that all the front
ends can be recreated from a template image stored in the
backup site and do not require any other form of synchro-
nization. The database node, however, requires stronger
consistency and uses a disk based replication scheme to
send all writes to a VM in the backup site. Applications
such as this are a natural fit for a cloud based DR ser-
vice because fewer resources are required to replicate the
important state than to run the full application.
To analyze the cost of providing DR for such an appli-
cation, we calculate the Replication Mode and Failover
Mode costs of running DR for the RUBiS web bench-
mark. RUBiS is an e-commerce web application that
can be run using multiple Tomcat servers and a MySQL
database [3]. Figure 1 shows RUBiS’s structure and how
it replicates state to the cloud. We calculate costs based
on resource usage traces recorded from running RUBiS
with 300 clients, and prices gathered from Amazon’s
Cost Comparison Calculator [1]; we have validated that
the colocation pricing information is competitive with of-
ferings from other providers.
Cost Breakdown: Figure 2(a) shows the yearly cost
for running the DR service with a public cloud or a pri-
vate colocation facility. The server cost only requires one
“small” VM to run the DR server in Replication mode in
the cloud whereas the colocation DR approach must al-
ways be provisioned with the four “large” servers needed
to run the application during failover. Figure 2(b) shows
the resource requirements for both modes. The network
and IO consumption during failover mode includes the
web traffic of the live application with clients whereas the
replication mode only includes the replicated state per-
sisted to the database. The storage cost for EC2 is based
on EBS volumes (Amazon’s persistent storage product)
and IO costs, whereas the colocation center storage cost
is included as part of the server hardware costs.
99% Uptime Cost: Since disasters are rare, most of
the time only the Replication Mode cost must be paid.
The best way to compare total costs is thus to calcu-
late the yearly cost of each approach based on a cer-
tain level of downtime caused by disasters. Assuming a
99% uptime model where a total of 3.6 days of downtime
is handled by transitioning from Replication to Failover
Mode, the yearly cost of the cloud DR service comes to
only $1,562, compared to $10,373 with the colocation
provider—an 85% reduction (Figure 2a). This illustrates
the benefit of the cloud’s pay-as-you-go pricing model—
Public Cloud Colocation
Data Warehouse Replication Failover Replication Failover
Servers $4.08 $12.00 $8.51 $8.51
Network $0.10 $0.12 $0.22 $0.26
Storage $3.50 $3.92
Total per day $7.68 $16.04 $8.73 $8.77
Total per year $2,802 $5,853 $3,186 $3,202
99% uptime cost $2,832 per year $3,186 per year
1 2 6 12
Yearly 99% Uptime Cost
Cloud Colo
Backups per Day
Figure 3: (a) Cost for providing DR services for the data warehouse application. The cloud provides only moderate savings due to
high storage costs. (b) Using periodic backups can significantly lower the price of DR in the cloud by reducing the cost of VMs.
substantial savings can be achieved if the cost to synchro-
nize state to a backup site is lower than the cost of run-
ning the full application.
Cost of Adding DR: Our analysis so far considered
the primary site to run on the user’s own private re-
sources, but they could also be run in the cloud. How-
ever, simply using cloud resources does not eliminate the
need for DR—it is still critical to run a DR service to
ensure continued operation if the primary cloud provider
is disrupted. Running the whole application in the cloud
costs $18,992 per year and using cloud DR in addition
only adds 8%. Running the application in a colo center
costs more in the first place ($24,095 per year) but adding
DR in a second colo facility increases the total cost by al-
most 42%. Finally, if a colocation center is used for the
primary site but a cloud is used for DR, then the incre-
mental cost of having DR is only 6.5%.
3.1.2 Case Study: Data Warehouse
Our second case study analyzes the cost of providing DR
for a Data Warehouse application. A data warehouse
records data such as a stream of website clicks or sales
information produced by other applications. Data is typi-
cally appended to the warehouse at regular intervals, and
reports are generated based on the incoming and exist-
ing data sets. We consider a small sized Data Warehouse
with a 1TB capacity that adds 1 GB of new data per day.
To run the full application, a powerful server is required–
we estimate costs based on a “High-Memory Extra Large
Instance” from EC2.
Cost Breakdown: Figure 3(a) shows the cost for run-
ning the data warehouse application. We assume that the
cloud based DR system requires a “medium” size VM
as a backup server due to its IO intensive nature, result-
ing in a relatively high server cost even under normal op-
eration. Additionally, the cloud must pay a large stor-
age cost to support the 1TB capacity of the data ware-
house. As a result, the cloud based DR service provides a
smaller benefit because its Replication Mode cost is only
slightly lower than the cost in a colocation facility, and
its Failover Mode cost is significantly higher.
99% Uptime Cost: By comparing the Failover Mode
costs, it is clear that it is cheapest to use a colocation
center as the primary site of the data warehouse ($5,853
per year in the cloud versus $3,202 per year in a coloca-
tion center). However, since the replication cost for the
cloud is lower and is incurred for 99% of the time, the
total costs is still lower for the cloud. Despite having a
higher Failover Mode price, the cloud based DR system
still lowers the total DR cost from $3,186 to $2,832 over
a one year period assuming 99% uptime.
Periodic Backups: The data warehouse application
obtains a smaller economic benefit from the cloud than
seen in the multi-tier web application case study due to
its increased server and storage requirements during or-
dinary operation. However, the flexibility of cloud re-
sources can help reduce this cost if the application can
tolerate a weaker RPO. For example, it may be sufficient
to only send periodic backups to the cloud site once ev-
ery few hours or after each bulk load, rather than running
the DR service continuously. Assuming that one hour of
VM time is charged per backup, Figure 3(b) shows how
the cost of DR can be substantially lowered by reducing
the backup frequency. While a similar approach could be
used in a private data center to reduce energy consump-
tion, it would have a much smaller effect on overall cost
since power usage of individual servers is a minor frac-
tion compared to the cost of hardware and space that must
be paid regardless of whether a machine is in use or not.
3.2 Benefits of the Cloud
Under current pricing schemes, cloud based DR services
will not see much benefit when used for applications that
require true “hot” standby servers since this can signifi-
cantly raise the cost during normal operation. However,
for applications that can tolerate recovery times on the
order of 200 seconds (a typical VM startup time in the
EC2 cloud), substantial savings can be found by utilizing
low cost servers while replicating state in ordinary con-
ditions and powerful ones only after a disaster occurs.
Cloud DR services may be able to obtain additional eco-
nomic benefits by multiplexing a single replication server
for multiple applications, further lowering the cost of re-
sources under normal operation. For applications with a
loose RPO, the cloud can provide even greater benefits by
only initiating the replication service a few times a day to
create periodic backups.
Cloud computing can facilitate disaster recovery by
significantly lowering costs:
The cloud’s pay-as-you go pricing model signifi-
cantly lowers costs due to the different level of re-
sources required before and during a disaster.
Cloud resources can quickly be added with fine
granularity and have costs that scale smoothly with-
out requiring large upfront investments.
The cloud platform manages and maintains the DR
servers and storage devices, lowering IT costs and
reducing the impact of failures at the disaster site.
The benefits of virtualization, while not necessarily
specific to cloud platforms, still provide important fea-
tures for disaster recovery:
VM startup can be easily automated, lowering re-
covery times after a disaster.
Virtualization eliminates hardware dependencies,
potentially lowering hardware requirements at the
backup site.
Application agnostic state replication software can
be run outside of the VM, treating it as a black box.
These characteristics can simplify the replication and de-
ployment of resources in a cloud DR site, and enable
business continuity by reducing recovery times.
4 Challenges for the Cloud Provider
Although cloud-based DR can provide economic benefits
for a customer, such a service raises numerous challenges
for a cloud provider, as discussed next.
4.1 Handling Correlated Failures
Typically a cloud provider will attempt to statistically
multiplex its DR customers onto its server pool. Such
statistical multiplexing assumes that not all of its cus-
tomers will experience simultaneous failures, and hence
the number of free servers that the cloud providers must
have available is smaller than the peak needs of all its cus-
tomers. However, correlated failures across customers is
not uncommon—for instance, an electric grid failure or
a natural disaster such as a flood can cause a large num-
ber of customer from a geographic area to simultaneously
failover to their DR sites. To prevent such correlated
failures from stressing any one data center, the cloud
provider should attempt to distribute its DR customers
across multiple data centers in a way that minimizes po-
tential conflicts—e.g. multiple customers from the same
geographic region should be backed up to different cloud
data centers. This placement problem is further compli-
cated by constraints such as limits on latency between
the customer and cloud site. To intelligently address this
issue, the cloud provider must employ risk models—not
unlike ones used by insurance companies—to (i) estimate
how many servers should be available in a data center for
a certain group of customers and (ii) how to distribute
customers from a region across different data center sites
to “spread the risk”. In the event of stress on any single
data center due to correlated failures, dynamic migration
of a group of customers to another site can be employed.
To achieve all of these tasks seamlessly, the cloud
provider should be able to treat all of its data centers
as a single pool of resources available to its DR cus-
tomers [10, 2]. In practice, current data centers act as iso-
lated entities and it is non-trivial to move or replicate stor-
age and computation resources between data centers. We
believe that future cloud architectures will rely on net-
work virtualization to provide seamless connectivity be-
tween data centers, and wide-area VM and storage migra-
tion to allow for resource management across data center
4.2 Revenue Maximization
The DR strategies we have discussed assume that cus-
tomers only pay for the majority of their DR resources
after some kind of failure actually occurs, and that suf-
ficient resources are always available when needed. The
cloud service provider must maintain these resources and
pay for their upkeep at all times, regardless of whether
a customer has experienced a failure. Since disasters are
typically rare, there will be little or no revenue from the
server farm in the normal case when there are no fail-
ures. Hence, a cloud provider must find ways to generate
revenue from such idling resources in order to make its
capital investments viable.
We assume that a cloud DR provider will also offer tra-
ditional cloud computing services and rent its resources
to customers for non-DR purposes. In this case, the cloud
may be able to “double book” its servers for both regu-
lar and DR customers. Public clouds generally only offer
best effort service when new VM or network resources
are requested. While this is sufficient for general cloud
computing, in disaster recovery it is imperative that ad-
ditional resources be available within the specified RTO.
One existing pricing mechanism that would facilitate this
on demand access to resources is the use of “spot in-
stances”. Spot instances allow the service provider to
rent resources, typically at a lower price, without guar-
antees about how long they will be available. A cloud
service could generate revenue from idling DR servers by
offering them as spot instances to non-DR customers and
reclaim them on-demand when these servers are needed
for high priority DR customers.
Currently, cloud platforms often provide few guaran-
tees about server and bandwidth availability and network
quality of service, which are important for ensuring an
application can fully operate after failover. EC2 currently
supports “reserved” VM instances that are guaranteed to
be available, but they are primarily designed for users
who know that they will be actively running a VM for
a long period of time, and their pricing is designed to re-
flect this with a moderate yearly fee but cheaper hourly
costs. For disaster recovery, it may be desirable to al-
low for “priority resources” which are guaranteed to be
available on demand, although perhaps at a higher hourly
cost than ordinary VM instances or network bandwidth
(which also increases the revenue for the cloud provider
while providing better assurances to a customer).
4.3 Mechanisms for Cloud DR
While cloud computing platforms already contain many
useful features for supporting disaster recovery, there are
additional requirements they must meet before they can
provide DR as a cloud service.
Network Reconfiguration: For a cloud DR service to
provide true business continuity, it must facilitate recon-
figuring the network setup for an application after it is
brought online in the backup site. We have previously
proposed how a cloud infrastructure can be combined
with virtual private networks (VPNs) to support this kind
of rapid reconfiguration for applications that only com-
municate within a private business environment [10].
Public Internet facing applications would require addi-
tional forms of network reconfiguration through either
modifying DNS or updating routes to redirect traffic to
the failover site. To support any of these features, cloud
platforms need greater coordination with network service
Security & Isolation: The public nature of cloud
computing platforms remains a concern for some busi-
nesses. In order for an enterprise to be willing to fail over
from its private data center to a cloud during a disaster it
will require strong guarantees about the privacy of stor-
age, network, and the virtual machine resources it uses.
Likewise, clouds must guarantee that the performance of
applications running in the cloud will not be impacted by
disasters affecting other businesses.
VM Migration & Cloning: Current cloud comput-
ing platforms do not support VM migration in or out
of the cloud. VM migration or cloning would simplify
the failback procedure for moving an application back
to its original site after a disaster has been dealt with.
This would also be a useful mechanism for facilitating
planned maintenance downtime. The Remus system [4]
has demonstrated how a continuous form of VM migra-
tion can be used to synchronize both memory and disk
state of a virtual machine to a backup server. This could
potentially allow for full system DR mechanisms that al-
low completely transparent failover during a disaster. To
support this, clouds must expose additional hypervisor
level functionality to their customers, and migration tech-
niques must be optimized for WAN environments.
5 Ongoing Work and Conclusions
We have argued that cloud computing platforms are an
excellent match for providing disaster recovery services
due to their pay-as-you-go pricing model and ability to
rapidly bring resources online after a disaster. The flexi-
bility of cloud resources also allows enterprises to make
a trade off between data protection and price to an ex-
tent not possible when using private resources that must
be statically provisioned. We have compared the costs
of running DR services using public cloud or privately
owned resources, and shown cost reductions of up to 85%
by taking advantage of cloud resources.
In our ongoing work, we are developing Dr. Cloud, a
prototype DR system that we can use to understand the
potential for using existing cloud platforms to provide
DR. This will allow us to better understand what fea-
tures and optimizations must be included within the cloud
platform itself, and to explore the tradeoffs between cost,
RPO, and RTO in a cloud DR service.
Acknowledgements: This work was supported in
part by NSF grants CNS-0720271, CNS-0720616, CNS-
09169172, and CNS-0834243, as well as by AT&T. We
also thank our reviewers for their comments and sugges-
[1] Aws economics center.
[2] Rajkumar Buyya, Rajiv Ranjan, and Rodrigo N. Calheiros. InterCloud:
Utility-Oriented Federation of Cloud Computing Environments for Scaling
of Application Services. In The 10th International Conference on Algo-
rithms and Architectures for Parallel Processing, Busan, Korea, 2010.
[3] Emmanuel Cecchet, Anupam Chanda, Sameh Elnikety, Julie Marguerite,
and Willy Zwaenepoel. Performance Comparison of Middleware Archi-
tectures for Generating Dynamic Web Content. In 4th ACM/IFIP/USENIX
International Middleware Conference, June 2003.
[4] Brendan Cully, Geoffrey Lefebvre, Dutch Meyer, Mike Feeley, Norm
Hutchinson, and Andrew Warfield. Remus: High availability via asyn-
chronous virtual machine replication. In Proceedings of the Usenix Sym-
posium on Networked System Design and Implementation, 2008.
[5] Albert Greenberg, James Hamilton, David A. Maltz, and Parveen Patel.
Cost of a cloud: Research problems in data center networks. In ACM SIG-
COMM Computer Communications Review, Feb 2009.
[6] Kimberley Keeton, Cipriano Santos, Dirk Beyer, Jeffrey Chase, and John
Wilkes. Designing for Disasters. Conference On File And Storage Tech-
nologies, 2004.
[7] Kimberly Keeton, Dirk Beyer, Ernesto Brau, Arif Merchant, Cipriano San-
tos, and Alex Zhang. On the road to recovery:restoring data after disasters.
European Conference on Computer Systems, 40(4), 2006.
[8] Tirthankar Lahiri, Amit Ganesh, Ron Weiss, and Ashok Joshi. Fast-
Start:quick fault recovery in oracle. ACM SIGMOD Record, 30(2), 2001.
[9] Vmware high availability.
[10] T. Wood, A. Gerber, K. Ramakrishnan, J. Van der Merwe, and P. Shenoy.
The case for enterprise ready virtual private clouds. In Proceedings of
the Usenix Workshop on Hot Topicsin Cloud Computing (HotCloud), San
Diego, CA, June 2009.
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