Yang Li, Li Guo and Yike Guo
Department of Computing, Imperial College London, London, U.K.
Keywords: Cloud Computing, Cloud Storage, Amazon S3, CACSS.
Abstract: The advent of the cloud era has yielded new ways of storing, accessing and managing data. Cloud storage
services enable the storage of data in an inexpensive, secure, fast, reliable and highly scalable manner over
the internet. Although giant providers such as Amazon and Google have made a great success of their
services, many enterprises and scientists are still unable to make the transition into the cloud environment
due to often insurmountable issues of privacy, data protection and vendor lock-in. These issues demand that
it be possible for anyone to setup or to build their own storage solutions that are independent of
commercially available services. However, the question persists as to how to provide an effective cloud
storage service with regards to system architecture, resource management mechanisms, data reliability and
durability, as well as to provide proper pricing models. The aim of this research is to present an in-depth
understanding and analysis of the key features of generic cloud storage services, and of how such services
should be constructed and provided. This is achieved through the demonstration of design rationales and the
implementation details of a real cloud storage system (CACSS). The method by which different
technologies can be combined to provide a single excellent performance, highly scalable and reliable cloud
storage system is also detailed. This research serves as a knowledge source for inexperienced cloud
providers, giving them the capability of swiftly setting up their own cloud storage services.
For many IT professionals, researchers and
computer owners, finding enough storage space to
hold data is a real challenge. Some people invest in
ever larger external hard drives, storing files locally
in order to deal with their data growth. However,
protecting against the many unexpected things that
can happen to computers or hard drives is not a
trivial task. Similar issues exist for a number of large
and geographically diverse enterprises; they store
and process their rapidly growing data in several
data centres and may adopt multiple storage
systems. Finding and managing files in this
increasingly large sea of data is extremely difficult.
Although modern distributed computing systems
provide ways of accessing large amounts of
computing power and storage, it is not always easy
for non-experts to use. There is also a lack of
support in user defined file metadata in these
systems. This has led most users to store a lot of data
that strongly relates to the file content in relational
databases. This separation of the file data and
semantic metadata can create issues of scalability,
interoperability and furthermore may result in low
Unlike local storage, cloud storage relieves end
users of the task of upgrading their storage devices
constantly. Cloud storage services enable
inexpensive, secure, fast, reliable and highly scalable
data storage solutions over the internet. Leading
cloud storage vendors, such as (Amazon) and
(Google) , provide clients with highly available, low
cost and pay as you go based cloud storage services
with no upfront cost. Recently, Amazon announced
that it currently holds more than 566 billion objects
and that it processes more than 370,000 requests per
second at peak times (Barr, 2011). Even so, many
enterprises and scientists are still unable to shift into
the cloud environment due to privacy, data
protection and vendor lock-in issues. In addition, the
high read/write bandwidths that are demanded by
I/O intensive operations, which occur in many
different scenarios, cannot be satisfied by current
internet connections.
These reasons provide an incentive for
* Please direct your enquiries to the communication autho
Professor Yike Guo
Li Y., Guo L. and Guo Y..
DOI: 10.5220/0003910800270036
In Proceedings of the 2nd International Conference on Cloud Computing and Services Science (CLOSER-2012), pages 27-36
ISBN: 978-989-8565-05-1
2012 SCITEPRESS (Science and Technology Publications, Lda.)
organisations to set up or build their own storage
solutions, which are independent of commercially
available services and meet their individual
requirements. However, knowledge of how to
provide an effective cloud storage service with
regards to system architecture, resource management
mechanisms, data reliability and durability, as well
as proper pricing models, remains untapped.
In order to reveal this secret knowledge behind
cloud storage services and thereby a generic solution,
we present CACSS, a generic computational and
adaptive cloud storage system that adapts existing
storage technologies to provide efficient and
scalable services. Through a demonstration of
CACSS, full details can be given of how a proper
cloud storage service can be constructed, in
consideration of its design rationale, system
architecture and implementation. We also show that
our system not only supports the mainstream cloud
storage features —such as well supported scalability,
sufficient replications and versioning— but that it
also supports large scale metadata operations such as
metadata searching. These features provide a generic
basis that enables potential storage service providers,
both internet wide and intranet wide, to setup their
“private S3”. Furthermore, CACSS is designed at
petabyte scale and can be deployed across multiple
data centres in different regions around the world.
We describe in detail how we manage the separation
of file content and file metadata, including user
defined metadata. We also demonstrate the
implementation of enabling multi-region support for
this cloud storage system. This paper demonstrates
how different technologies can be combined in order
to provide a single and highly superior generic
2.1 Generic Cloud Storage Features
A cloud storage system should be designed and
implemented with consideration of several generic
High scalability and performance: storage
demand has increased exponentially in recent
years and as such, it is necessary for cloud
storage systems to be able to seamlessly and
rapidly scale their storage capability for both file
content and file metadata. Traditionally,
metadata and file data are managed and stored
by the same file system and most of the time on
the same device. In some modern distributed file
systems, to improve scalability and performance,
metadata is stored and managed separately by
one or more metadata servers (Gibson and Van
Meter, 2000). However, many of these still
suffer from bottlenecks at high concurrent access
rates (Carns et al., 2009). The metadata of a
petabyte scale file system could contain in
excess of billions of records, consuming
terabytes of space or more. Therefore, the
efficient metadata management of cloud storage
systems is crucial for their overall storage
system performance.
Data durability: a far more common event than
hardware failure or a disaster scenario is end
user error, by which data is unintentionally
deleted or overwritten. This demands that cloud
storage systems have sophisticated replication,
versioning and recovery mechanisms to restore
Support of various pricing models: the
traditional pricing model for software has been a
one-time payment for unlimited use. Cloud
pricing models, such as pay as you go and pay
monthly are very similar to the usage charges of
utility companies. In order to accommodate this,
it is necessary for cloud storage systems to have
an efficient monitoring framework to track all
kinds of resource usage, including network data
transfer, I/O requests, amounts of data stored
(file content and file metadata) and resources
consumed for various computations.
Security models: various security models need to
be implemented to ensure that documents and
files are accessible at the correct time, location
and by the right person, providing adequate and
accurate security control over the data without
compromising performance.
Interoperability: no specific standards that
enable interoperability between cloud storage
vendors currently exist, and this has created
problems in the moving of data from one cloud
to another. Similar issues arise in converting
existing applications that are built on traditional
file systems to the cloud. The ideal cloud storage
system must offer a level of abstraction,
portability and ease of use that enables the
consuming of storage services with minimal
support and development overhead.
2.2 Cloud Storage for Different
Enterprises and scientists use cloud storage services
for various purposes, and files are in different sizes
and formats. Some use cloud storage for large video
and audio files, and some use it for storing relatively
small files that are large in quantity; the variety and
range is vast. The different purposes of using cloud
storage services give rise to a significant diversity of
patterns of access to stored files. The nature of these
stored files, in terms of features such as size and
format, and the way in which these files are accessed
are the main factors that influence the quality of
cloud storage services that are eventually delivered
to the end users. Some common domains for future
cloud storage are as follows:
Computational storage: many applications in
science and enterprise are becoming
increasingly demanding in terms of their
computational and data requirements. Some
applications store terabytes of data and carry out
intensive I/O operations. Examples include
bioinformatics analysis and log processing
applications. Improving the overall performance
of such applications often demands a cloud
storage system that can bring computational
power closer to the data. Amazon Elastic
MapReduce service (Amazon) adapts a hosted
Hadoop framework running on the
infrastructure of Amazon EC2, in order to
provide an on-demand service to setup
computational tasks and process data that are
stored on Amazon S3.
Small file storage: some large online
ecommerce companies and social networking
websites store enormous numbers of small files;
these are mostly image files, and their numbers
are constantly growing. Every second, some of
these files are requested at a high rate by public
users. As the metadata of a small file may well
occupy more space than the file content itself,
large concurrent accessing of a small file may
cause excessive and redundant I/O operations
due to metadata lookups (Beaver et al., 2010).
This scenario can sometimes create a bottleneck.
Metadata operation intensive storage: metadata
is the information that describes data files.
Common metadata attributes include the time of
an event, author’s name, GPS location and
captions. Many scientists record information
about their experimental configuration, such as
temperature, humidity and other data attributes;
for many it has become an integral part of
storage. Accurate identification and adequate
support for metadata queries of these metadata
would bring additional values to the stored files
and ensure that analyses and computations are
carried out correctly and efficiently.
Unfortunately most storage systems are not
capable of efficiently searching file metadata,
especially those which are user defined on a
large scale. Leading cloud storage providers
such as Amazon S3, Cloud Files (Rackspace)
and Google Cloud Storage (Google) offer cloud
storage services for object data combined with a
key-value style of user defined metadata. These
metadata can be retrieved and used by user
applications. However, none of these cloud
storage providers yet support any object search
or query based on the object metadata. Some
research has already been done on the topic of
metadata indexing and searching services
(Singh et al., 2003, Leung et al., 2009)
The present study addresses only some of the
common domains to which a cloud storage system
can be applied; indeed there are more domains that
are yet to be studied. In general, it is not easy to
make a system that can work for absolutely any
domain. However, we can learn through generic
features and the challenges that exist in those
domains already studied, and eventually we can
build towards a more adaptive and generic cloud
storage system that can serve different purposes
without too much effort.
Amazon Simple Storage Service (Amazon S3) is an
online storage service that aims to provide reliable
and excellent performance at a low cost. However,
neither its architecture nor its implementation has
yet been made public. As such, it is not available for
extension in order to develop the capability of
creating private clouds of any size.
Amazon S3 is the leading de facto standard of
bucket-object oriented storage services. Successive
cloud storage vendors, such as (Rackspace) and
(Google) all adopt s3’s style of bucket-object
oriented interface. This style hides all the
complexities of using distributed file systems, and it
has proven to be a success (Barr, 2011). It simply
allows users to use the storage service from a higher
level: an object contains file content and file
metadata, and it is associated with a client assigned
key; a bucket, a basic container for holding objects,
plus a key together uniquely identify an object.
Following earlier discussion on key characteristics
of the generic private cloud storage system, we now
present in detail the design rationale of a CACSS
From a conceptive level, the architecture of
CACSS has five main components: the access
interface, which provides a unique entry point to the
whole storage system; the metadata management
service, which manages the object metadata; the
object operation management service, which handles
a wide range of object operation requests; the
metadata storage space, which stores all of the object
metadata; and the object data storage space, which
stores all of the object content data (Figure 1).
Figure 1: CACSS Architecture.
4.1 Access Interface
CACSS offers a web-based interface for managing
storage space and searching for objects. The current
implementation supports Amazon’s S3 REST API,
the prevailing standard commercial storage cloud
4.2 Identity and Access Management
IAM is a separated service that provides
authorization and access control of various resources.
It offers sub user, group management and precise
permission control of which operations a user can
perform and under what conditions such operations
can be carried out.
4.3 Metadata Management
To achieve high performance in metadata access and
operation, CACSS’s object metadata and content are
completely separated. Each object’s metadata—
including its system metadata such as size, last date
modified and object format, together with user
defined metadata—are all stored as a collection of
blocks addressed by an index in CACSS’s Metadata
Storage Space (MSS). MSS keeps all of the
collections’ data sorted lexicographically by index.
Each block is akin to a matrix which has exactly two
columns and unlimited rows. The values of the
elements in the first and second columns are block
quantifiers and block targets, respectively. All of the
block quantifiers have unique values in each block:
E.g. an index of W maps to a collection:
4.3.1 Metadata Management Service
MMS manages the way in which an object’s
metadata is stored. In such a system a client will
consult the CACSS MMS, which is responsible for
maintaining the storage system namespace, and they
will then receive the information specifying the
location of the file contents. This allows multiple
versions of an object to exist.
MMS handles requests as follows. First, it
checks if a request contains an access key and a
signed secret key. CACSS consults AIM and MSS to
verify whether the user has the permission to
perform the operation. If they do have permission,
the request is authorized to continue. If they don’t,
error information is returned. If a request does not
contain an access key or a signed secret key, MMS
is looked up to verify if the request to the bucket or
object is set as publicly available to everyone. If it is
set as public, then the request continues to the next
step. All the requests are logged, both successful and
failed. The logging data can be used by both the
service provider and storage users for billing,
analysis and diagnostic purposes.
Differing from traditional storage systems that
limit the file metadata which can be stored and
accessed, MMS makes metadata more adaptive and
comprehensive. Additional data regarding file and
user-defined metadata can be added to the metadata
storage, and these data can be accessed and adopted
on demand by users or computational works at any
time. Searching via metadata is another key feature
Buckets. To reduce interoperability issues, CACSS
adopts the de facto industry standard of buckets as
basic containers for holding objects.
Unlike some traditional file systems, in which a
limited number of files can be stored in a directory,
there is no limit to the number of objects that can be
stored in a CACSS bucket. CACSS has a global
namespace—bucket names are unique and each
individual bucket’s name is used as the index in
MSS. We use various block quantifiers and block
targets to store a variety of information, such as
properties of a bucket or an object, permissions and
access lists for a particular user, and other user
defined metadata.
For example, for a bucket named “bucket1”, an
index “bucket1” should exist, which maps to a
collection of data such as:
: bucket1
:2 ;
: ℎ
Objects. The index of each object is comprised of a
string, which has the format of the bucket name
together with the assigned object key. As a result of
this nomenclature, objects of the same bucket are
naturally very close together in MSS; this improves
the performance of concurrent metadata access to
objects of the same bucket.
For example, considering an object with the user-
assigned key “object/key.pdf” in bucket “bucket1”,
an index of “bucket1- object/key.pdf” should exist,
which maps to the following collection of data:
: /.
:1 _;
:ℎ ℎ
: 2011
4.3.2 Object Versioning
When versioning setting is enabled for a bucket,
each object key is mapped to a core object record.
core object record holds a list of version IDs
that map to individual versions of that object.
For example, for an object with a predefined key
“object/paper.pdf” in bucket “versionbucket”, an
index of "versionbucketobject/paper.pdf"
should exist, which maps to the collection data:
: 1
Similarly, the object’s version record with row
key “versionbucket-object/paper.pdf-uuid1” maps to
the collection data:
4.4 Object Data Management
CACCS stores all the unstructured data, such as file
content, in the Object Data Storage Space (ODSS).
ODSS is intentionally designed to provide an
adaptive storage infrastructure that can store
unlimited amounts of data and that does not depend
on underlying storage devices or file systems.
Storage service vendors are able to compose one or
multiple types of storage devices or systems together
to create their own featured cloud storage system
based on their expertise and requirements in terms of
level of availability, performance, complexity,
durability and reliability. Such implementation could
be as simple as NFS (Sandberg et al., 1985), or as
sophisticated as HDFS (Borthakur, 2007), PVFS
(Carns et al., 2000) and Lustre (Schwan, 2003).
CACSS’s File Operation Management Service
(FOMS) implements all ODSS’s underlying file
systems’ API, so that it can handle a wide range of
file operation requests to the ODSS. FOMS works
like an adapter that handles the architectural
differences between different storage devices and
file systems. It works closely with MMS to maintain
the whole namespace of CACSS. FOMS also
possesses the capability of utilising all the available
resources by performing various object allocation
strategies, which are based on factors such as
objects’ types, sizes and even their previous usage
After considerable research and experimentation, we
chose HBase as the foundational MSS storage for all
object metadata. HBase is highly scalable and
delivers fast random data retrieval. Its column-
orientation design confers exceptional flexibility in
the storing of data.
We chose Hadoop DFS (HDFS) as the
foundational storage technology for storing object
data in ODSS. Hadoop also supports MapReduce
framework (Apache) that can be used for executing
computation tasks within the storage infrastructure.
Although there is a single point of failure at the
NameNode in HDFS’s original design, many
research studies have been carried out in order to
build a highly available version of HDFS
NameNode, such as AvatarNode (Borthakur, 2010).
Every file and block in HDFS is represented as an
object in the NameNode’s memory, each of which
occupies about 150 bytes. Therefore the total
memory available on NameNode dictates the
limitation of the number of files that can be stored in
the HDFS cluster. By separating object metadata and
object data, CACSS is able to construct an adaptive
storage infrastructure that can store unlimited
amounts of data using multiple HDFS clusters, whilst
still exposing a single logical data store to the users
(Figure 3).
5.1 Multi-region Support
The design and architecture of CACSS are based on
the principles of scalability, performance, data
durability and reliability. Scalability is considered in
various aspects including the overall capacity of
multi-region file metadata and file storage, as well as
throughput of the system. Taking another
perspective, the implementation of CACSS consists
of a region controller and multiple regions (Figure 2).
Figure 2: Implementation of CACSS.
A Tomcat cluster is used as the application server
layer in each region. It is easy to achieve high
scalability, load balancing and high availability by
using a Tomcat cluster and configuring with other
technologies such as HAProxy and Nginx (Doclo,
2011, Mulesoft).
The region controller has a MySQL cluster for
storing various data such as user account information
and billing and invoice details.
A bucket can be created in one of the regions. At
the same time, a DNS A record is also inserted into
the DNS server. This mapping ensures that clients
will send a hosted-style access request of the bucket
and the object to the correct region. Each region is
consistent with a Tomcat cluster, an HBase cluster
and a set of HDFS clusters. The object data is stored
in one of the HDFS clusters in the region. The object
key and metadata are stored in the region’s HBase
cluster. It is always important to consider that any
access to a bucket or object requires access rights to
be checked. In CACSS, each request goes through its
region first; if the requested bucket or object is set to
be public, there is no need to communicate with the
region controller. If it is not set as public, it consults
the region controller to perform the permission check
before making a response. The region controller,
which includes a MySQL cluster, keeps records of all
the requests and maintains user accounts and billing
information. A DNS system (such as Amazon Route
53 (Amazon)) serves to map the bucket name to its
corresponding region’s Tomcat cluster IP. The region
controller can also connect to the existing IAM
service to provide more sophisticated user and group
Figure 3: Implementation multi-region HDFS clusters for
storing buckets and contents of objects.
CACSS also adopts other useful features of
HDFS such as no explicit limitation on a single file
size and no limitation on the number of files in a
directory. In CACSS, most of the objects are stored
in a flat structure in HDFS. Each object’s file name
under HDFS is a generated UUID to ensure
The implementation of CACSS does not need to
rely solely on HDFS. The separation of file metadata
entirely from file content enables CACSS to adapt to
one or even multiple file systems, such as GPFS or
Lustre. It is now deployed as a service under IC-
Cloud platform (Guo and Guo, 2011), and is
expected to work with a variety of distributed file
systems through POSIX or their APIs without much
We performed our experiments on top of Amazon
EC2 instances, to enable the comparison of CACSS
and Amazon S3 under similar hardware and network
environments. We used (JetS3t), an open source
Java S3 library, configuring it with our experiment
code to evaluate the performance of CACSS.
We used one m2.xlarge instance, with 17.1GB of
memory and 6.5 EC2 Compute Units, to run
MySQL, HDFS NameNode, HBase Hmaster and
Tomcat with the CACSS application. Three
m1.large instances, each with 7.5GB memory and 4
EC2 Compute Units ran HDFS DataNodes and
HBase Regionservers. Each of these instances was
attached with 100GB volumes of storage space.
Another two m1.large instances were configured
with the same experiment code but different S3 end
points. We refer to these two instances as “S3 test
node” and “CACSS test node”.
To evaluate the performance of CACSS, we ran
a series of experiments on both Amazon S3 and
CACSS. The evaluation of the performance of
Amazon EC2 and S3 has been carried out previously
by (Garfinkel, 2007). A similar method was adopted
here to evaluate the overall throughput of CACSS.
Figure 4 and Figure 5 illustrate respectively the
write and read throughputs of Amazon EC2 to
Amazon S3, and of EC2 to CACSS, based on our
experiments. Each graph contains traces of observed
bandwidths for transactions of 1Kbyte, 1Mbyte,
100Mbyte and 1Gbyte. Both Amazon S3 and
CACSS perform better with larger transaction sizes,
because smaller size files would experience more
transaction overhead. For files larger than 1Mbyte,
the average speed of transaction of CACSS is higher
than Amazon S3; this is probably due to underlying
hardware differences between Amazon EC2 and
Amazon S3, such as hard drive RPM and RAID
Amazon S3’s List Objects operation only
supports a maximum of 1000 objects to be returned
at a time, so we could not properly evaluate its
object metadata service performance. However, we
were able to run some tests to see how CACSS’s
metadata management performs. We ran a List All
Objects operation after every 1000 Put Object
operations. All of the operations were targeted to the
same bucket. Each Put Object was done using an
empty file, because we were only interested in the
performance of the metadata access in this
experiment. Figure 6 shows a scatter graph of the
response time of each Put Object, with respect to the
total number of objects in the bucket. The result
shows an average response time of 0.007875s and a
variance of 0.000157s for each Put Object operation.
This indicates that the response time is pretty much
constant no matter how many objects are stored in
the bucket. Figure 7 illustrates the response time of
each List All Objects operation with respect to the
total number of objects contained in the bucket.
There are several peaks in the graph which have
been marked with a red circle. These peaks are
caused by sudden network latency between Amazon
EC2 instances during that time. Otherwise, the
overall result shows a linear relation between the
response time and the total number of objects.
Figure 4: Cumulative Distribution Function (CDF) plots
for writing transactions from EC2 to Amazon S3 and
CACSS of various sizes.
Figure 5: CDF plots for reading transactions from EC2 to
Amazon S3 and CACSS of various sizes.
Figure 6: Put Object requests.
Figure 7: List all objects requests.
Besides Amazon S3, there have been quite a few
efforts in cloud storage services, including the
Walrus (Nurmi et al., 2009) is a storage service
included with Eucalyptus that is interface-
compatible with Amazon S3. The open source
version of Walrus does not support data replication
services. It also does not fully address how file
metadata is managed and stored.
The Openstack (Openstack) project has an object
storage component called Swift, which is an open
source storage system for redundant and scalable
object storage. However, it does not support object
versioning at present. The metadata of each file is
stored in the file’s extended attributes in the
underlying file system. This could potentially create
performance issues with a large number of metadata
pWalrus (Abe and Gibson, 2010) is a storage
service layer that integrates parallel file systems into
cloud storage and enables data to be accessed
through an S3 interface. pWalrus stores most object
metadata information as the file’s attributes. Access
control lists, object content hashes (MD5) and other
object metadata are kept in .walrus files. If a huge
number of objects are stored under the same bucket,
pWalrus may be inefficient in searching files based
on certain metadata criteria; this factor can cause
bottlenecks in metadata access.
Cumulus (Bresnahan et al., 2010) is an open
source cloud storage system that implements the S3
interface. It adapts existing storage implementations
to provide efficient data access interfaces that are
compatible with S3. However, details of metadata
organisation and versioning support are not fully
Hadoop Distributed File System (HDFS)
(Borthakur, 2007) is a distributed, reliable, scalable
and open source file system, written in Java. HDFS
achieves reliability by replicating data blocks and
distributing them across multiple machines.
(HBase) is an open source, non-relational,
versioned, column-oriented distributed database that
runs on top of HDFS. It is designed to provide fast
real time read/write data access. Some research has
already been done to evaluate the performance of
(Carstoiu et al., 2010) (Khetrapal and
Ganesh, 2006).
Table 1 shows a comparison of the support
features of Amazon S3, Google Cloud Storage and
CACSS. Many features are shared, with CACSS
Table 1.
Amazon S3 Google Cloud
Bucket region
Yes Yes Yes
access key
with signature;
OAuth access
access key
with signature
Large file
Multi part
Multi part
Yes Yes Yes
Host static
Yes No Yes
Yes No Yes
Access logs
Yes Yes Yes
Random read
Yes Yes Yes
write support
No No No
Object key
Object key
Object key,
metadata and
Storage space,
network usage
and number of
Storage space,
network usage
and number of
99.9% uptime
% durability
99.9% uptime N/A
having the additional capability of metadata and user
defined data searching.
We have presented the design and implementation of
CACSS, a cloud storage system based on the generic
principles of scalability, performance, data durability
and reliability. CACSS not only enables users to
access their data through the S3 interface, the de
facto industry standard, but also provides support for
additional features that make the storage service
more comprehensive. These features include user
defined metadata and object metadata searching. The
storage model we propose offers service providers
considerable advantage in combining existing
technologies to compose a single customized cloud
storage system. Furthermore, CACSS performance
was found to be comparable to Amazon S3 in formal
tests, with similar read/write capabilities. Although
other features were difficult to compare directly,
CACSS performance was highly adequate in terms
of Put Object and List All Object requests.
There are several directions of potential future
studies. In any science and enterprise environment,
data experiences rapid growth. Some of these data
are very active in that they need to be retrieved very
frequently, and conversely much other data are
hardly accessed. In light of this, a potentially useful
research direction is the extension of current work to
enable a more application-aware cloud storage
service. This would include a data movement
framework that enables users or applications to
automatically and manually archive inactive data in
lower cost storage space, whilst upgrading highly
active data to higher performance storage space.
This should enhance capacity on demand cloud
storage services to a higher level, towards
performance on demand cloud storage services.
Further study could also address the issue that
current cloud storage systems do not offer much
support for “in-house” data-centric services, such as
processing and converting existing stored data on
top of the storage infrastructure. This drives users
into an inefficient workflow pattern of downloading
data from cloud storage, dealing with the data
externally, and then uploading the processed data
back to cloud storage. Although parallel computing
(Kumar, 2002) and MapReduce (Dean and
Ghemawat, 2008) provide ways of effectively
executing computational tasks distributed on a large
scale, these implementations are not always easy for
non-experts to setup or use.
As a possible solution to this lack of in-house
services, a cloud storage system could be developed
with a framework that enables different storage
oriented services and operations to be “plugged in”.
This framework should implement sophisticated data
and computing resource sharing mechanisms in
order to allow stored data to be used by those
plugged in services in a controlled and safe manner.
In such a way, it is possible to create a cloud storage
environment for a user community. For example, a
user can publish an application, describing the
specific computational task in an application
repository; another user is then able to process the
same task in a way that requires little or no
knowledge about the computation, simply by
specifying which published application to execute.
Abe, Y. & Gibson, G. pWalrus: Towards better integration
of parallel file systems into cloud storage. 2010. IEEE,
Amazon. Amazon Elastic MapReduce [Online]. Available:
Amazon. Amazon Simple Storage Service (S3) [Online].
Amazon. Route 53 [Online]. Available: http://
Apache. Hadoop MapReduce [Online]. Available: http://
Barr, J. 2011. Available from:
Beaver, D., Kumar, S., Li, H. C., Sobel, J. & Vajgel, P.
2010. Finding a needle in Haystack: Facebook’s photo
storage. Proc. 9th USENIX OSDI.
Borthakur, D. 2007. The hadoop distributed file system:
Architecture and design. Hadoop Project Website.
Borthakur, D. 2010. Hadoop avatarnode high availability.
Available from:
Bresnahan, J., Keahey, K., Freeman, T. & Labissoniere, D.
2010. Cumulus: an open source storage cloud for
science. SC10 Poster.
Carns, P., Lang, S., Ross, R., Vilayannur, M., Kunkel, J. &
Ludwig, T. Small-file access in parallel file systems.
2009. IEEE, 1-11.
Carns, P. H., Ligon III, W. B., Ross, R. B. & Thakur, R.
PVFS: A parallel file system for Linux clusters. 2000.
USENIX Association, 28-28.
Carstoiu, D., Cernian, A. & Olteanu, A. Hadoop Hbase-
0.20.2 performance evaluation. New Trends in
Information Science and Service Science (NISS), 2010
4th International Conference on, 11-13 May 2010
2010. 84-87.
Dean, J. & Ghemawat, S. 2008. MapReduce: simplified
data processing on large clusters. Commun. ACM, 51,
Doclo, L. 2011. Clustering Tomcat Servers with High
Availability and Disaster Fallback. Available from:
Garfinkel, S. L. An evaluation of amazon’s grid
computing services: EC2, S3, and SQS. 2007.
Gibson, G. A. & van Meter, R. 2000. Network attached
storage architecture. Communications of the ACM, 43,
Google. Google Cloud Storage Service [Online].
Guo, Y.-K. & Guo, L. 2011. IC cloud: Enabling
compositional cloud. International Journal of
Automation and Computing, 8, 269-279.
Hbase, A. Available:
JETS3T. JetS3t [Online]. Available: http://
Khetrapal, A. & Ganesh, V. 2006. HBase and Hypertable
for large scale distributed storage systems. Dept. of
Computer Science, Purdue University.
Kumar, V. 2002. Introduction to parallel computing,
Addison-Wesley Longman Publishing Co., Inc.
Leung, A. W., Shao, M., Bisson, T., Pasupathy, S. &
Miller, E. L. 2009. Spyglass: fast, scalable metadata
search for large-scale storage systems. Proccedings of
the 7th conference on File and storage technologies.
San Francisco, California: USENIX Association.
Mulesoft. Tomcat Clustering - A Step By Step Guide.
Available from:
Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G.,
Soman, S., Youseff, L. & Zagorodnov, D. The
eucalyptus open-source cloud-computing system.
2009. IEEE, 124-131.
Openstack. Available:
Rackspace. Cloud Files [Online]. Available: http://
Sandberg, R., Goldberg, D., Kleiman, S., Walsh, D. &
Lyon, B. Design and implementation of the Sun
network filesystem. 1985. 119-130.
Schwan, P. Lustre: Building a file system for 1000-node
clusters. 2003.
Singh, G., Bharathi, S., Chervenak, A., Deelman, E.,
Kesselman, C., Manohar, M., Patil, S. & Pearlman, L.
A metadata catalog service for data intensive
applications. 2003. IEEE, 33-33.