Big Data in Cloud Computing: Features and Issues
Pedro Caldeira Neves
, Bradley Schmerl
, Javier Cámara
and Jorge Bernardino
Carnegie Mellon University, Institute for Software Research, Pittsburgh, PA 15213, U.S.A
ISEC – Superior Institute of Engineering of Coimbra, Polytechnic of Coimbra, 3030-190 Coimbra, Portugal
CISUC – Centre of Informatics and Systems of the University of Coimbra, FCTUC, University of Coimbra,
3030-290 Coimbra, Portugal
Keywords: Big Data, Cloud Computing, Big Data Issues.
Abstract: The term big data arose under the explosive increase of global data as a technology that is able to store and
process big and varied volumes of data, providing both enterprises and science with deep insights over its
clients/experiments. Cloud computing provides a reliable, fault-tolerant, available and scalable environment
to harbour big data distributed management systems. Within the context of this paper we present an overview
of both technologies and cases of success when integrating big data and cloud frameworks. Although big data
solves much of our current problems it still presents some gaps and issues that raise concern and need
improvement. Security, privacy, scalability, data governance policies, data heterogeneity, disaster recovery
mechanisms, and other challenges are yet to be addressed. Other concerns are related to cloud computing and
its ability to deal with exabytes of information or address exaflop computing efficiently. This paper presents
an overview of both cloud and big data technologies describing the current issues with these technologies.
In recent years, there has been an increasing demand
to store and process more and more data, in domains
such as finance, science, and government. Systems
that support big data, and host them using cloud
computing, have been developed and used
successfully (Hashem et al., 2014) .
Whereas big data is responsible for storing and
processing data, cloud provides a reliable, fault-
tolerant, available and scalable environment so that
big data systems can perform (Hashem et al., 2014).
Big data, and in particular big data analytics, are
viewed by both business and scientific areas as a way
to correlate data, find patterns and predict new trends.
Therefore there is a huge interest in leveraging these
two technologies, as they can provide businesses with
a competitive advantage, and science with ways to
aggregate and summarize data from experiments such
as those performed at the Large Hadron Collider
To be able to fulfil the current requirements, big
data systems must be available, fault tolerant, scalable
and elastic.
In this paper we describe both cloud computing
and big data systems, focusing on the issues yet to be
addressed. We particularly discuss security concerns
when hiring a big data vendor: data privacy, data
governance, and data heterogeneity; disaster recovery
techniques; cloud data uploading methods; and how
cloud computing speed and scalability poses a
problem regarding exaflop computing.
Despite some issues yet to be improved, we
present two examples that show how cloud
computing and big data can work well together.
Our contributions to the current state-of-the-art is
done by providing an overview over the issues to
improve or have yet to be addressed in both
The remainder of this paper is organized as
follows: Section 2 provides a general overview of big
data and cloud computing; Section 3 discusses and
presents two examples that show how big data and
cloud computing work well together and especially
how hiring a big data vendor may be a good choice so
that organizations can avoid IT worries; Section 4
discusses the several issues to address in cloud
computing and big data systems; and Section 5
presents the discussion, conclusions and future work.
Neves, P., Schmerl, B., Cámara, J. and Bernardino, J.
Big Data in Cloud Computing: Features and Issues.
DOI: 10.5220/0005846303070314
In Proceedings of the International Conference on Internet of Things and Big Data (IoTBD 2016), pages 307-314
ISBN: 978-989-758-183-0
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
The concept of big data became a major force of
innovation across both academics and corporations.
The paradigm is viewed as an effort to understand and
get proper insights from big datasets (big data
analytics), providing summarized information over
huge data loads. As such, this paradigm is regarded
by corporations as a tool to understand their clients,
to get closer to them, find patterns and predict trends.
Furthermore, big data is viewed by scientists as a
mean to store and process huge scientific datasets.
This concept is a hot topic and is expected to continue
to grow in popularity in the coming years.
Although big data is mostly associated with the
storage of huge loads of data it also concerns ways to
process and extract knowledge from it (Hashem et al.,
2014). The five different aspects used to describe big
data (commonly referred to as the five “V”s) are
Volume, Variety, Velocity, Value and Veracity (Sakr
and Gaber, 2014):
Volume describes the size of datasets that a big
data system deals with. Processing and storing big
volumes of data is rather difficult, since it
concerns: scalability so that the system can grow;
availability, which guarantees access to data and
ways to perform operations over it; and bandwidth
and performance.
Variety concerns the different types of data from
various sources that big data frameworks have to
deal with.
Velocity concerns the different rates at which data
streams may get in or out the system and provides
an abstraction layer so that big data systems can
store data independently of the incoming or
outgoing rate.
Value concerns the true value of data (i.e., the
potential value of the data regarding the
information they contain). Huge amounts of data
are worthless unless they provide value.
Veracity refers to the trustworthiness of the data,
addressing data confidentiality, integrity, and
availability. Organizations need to ensure that
data as well as the analyses performed on the data
are correct.
Cloud computing is another paradigm which
promises theoretically unlimited on-demand services
to its users. Cloud’s ability to virtualize resources
allows abstracting hardware, requiring little
interaction with cloud service providers and enabling
users to access terabytes of storage, high processing
power, and high availability in a pay-as-you-go
model (González-Martínez et al., 2015). Moreover, it
transfers cost and responsibilities from the user to the
cloud provider, boosting small enterprises to which
getting started in the IT business represents a large
endeavour, since the initial IT setup takes a big effort
as the company has to consider the total cost of
ownership (TCO), including hardware expenses,
software licenses, IT personnel and infrastructure
maintenance. Cloud computing provides an easy way
to get resources on a pay-as-you-go model, offering
scalability and availability, meaning that companies
can easily negotiate resources with the cloud provider
as required. Cloud providers usually offer three
different basic services: Infrastructure as a Service
(IaaS); Platform as a Service (PaaS); and Software as
a Service (SaaS):
IaaS delivers infrastructure, which means storage,
processing power, and virtual machines. The
cloud provider satisfies the needs of the client by
virtualizing resources according to the service
level agreements (SLAs);
PaaS is built atop of IaaS and allows users to
deploy cloud applications created using the
programming and run-time environments
supported by the provider. It is at this level that
big data DBMS are implemented;
SaaS is one of the most known cloud models and
consists of applications running directly in the
cloud provider;
These three basic services are closely related: SaaS is
developed over PaaS and ultimately PaaS is built atop
of IaaS.
From the general cloud services other services
such as Database as a Service (DBaaS) (Oracle,
2012), BigData as a Service (BDaaS) and Analytics
as a Service (AaaS) arose.
Since the cloud virtualizes resources in an on-
demand fashion, it is the most suitable and compliant
framework for big data processing, which through
hardware virtualization creates a high processing
power environment for big data.
Storing and processing big volumes of data requires
scalability, fault tolerance and availability. Cloud
computing delivers all these through hardware
virtualization. Thus, big data and cloud computing are
two compatible concepts as cloud enables big data to
be available, scalable and fault tolerant.
IoTBD 2016 - International Conference on Internet of Things and Big Data
Business regard big data as a valuable business
opportunity. As such, several new companies such as
Cloudera, Hortonworks, Teradata and many others,
have started to focus on delivering Big Data as a
Service (BDaaS) or DataBase as a Service (DBaaS).
Companies such as Google, IBM, Amazon and
Microsoft also provide ways for consumers to
consume big data on demand. Next, we present two
examples, Nokia and RedBus, which discuss the
successful use of big data within cloud environments.
3.1 Nokia
Nokia was one of the first companies to understand
the advantage of big data in cloud environments
(Cloudera, 2012). Several years ago, the company
used individual DBMSs to accommodate each
application requirement. However, realizing the
advantages of integrating data into one application,
the company decided to migrate to Hadoop-based
systems, integrating data within the same domain,
leveraging the use of analytics algorithms to get
proper insights over its clients. As Hadoop uses
commodity hardware, the cost per terabyte of storage
was cheaper than a traditional RDBMS (Cloudera,
Since Cloudera Distributed Hadoop (CDH)
bundles the most popular open source projects in the
Apache Hadoop stack into a single, integrated
package, with stable and reliable releases, it embodies
a great opportunity for implementing Hadoop
infrastructures and transferring IT and technical
concerns onto the vendors’ specialized teams. Nokia
regarded Big Data as a Service (BDaaS) as an
advantage and trusted Cloudera to deploy a Hadoop
environment that copes with its requirements in a
short time frame. Hadoop, and in particular CDH,
strongly helped Nokia to fulfil their needs (Cloudera,
3.2 RedBus
RedBus is the largest company in India specialized in
online bus ticket and hotel booking. This company
wanted to implement a powerful data analysis tool to
gain insights over its bus booking service (Kumar,
2006). Its datasets could easily stretch up to 2
terabytes in size. The application would have to be
able to analyse booking and inventory data across
hundreds of bus operators serving more than 10.000
routes. Furthermore, the company needed to avoid
setting up and maintaining a complex in-house
At first, RedBus considered implementing in-
house clusters of Hadoop servers to process data.
However they soon realized it would take too much
time to set up such a solution and that it would require
specialized IT teams to maintain such infrastructure.
The company then regarded Google bigQuery as the
perfect match for their needs, allowing them to:
Know how many times consumers tried to find an
available seat but were unable to do it due bus
Examine decreases in bookings;
Quickly identify server problems by analysing
data related to server activity;
Moving towards big data brought RedBus business
advantages. Google bigQuery armed RedBus with
real-time data analysis capabilities at 20% of the cost
of maintaining a complex Hadoop infrastructure
(Kumar, 2006).
As supported by Nokia and RedBus examples,
switching towards big data enables organizations to
gain competitive advantage. Additionally, BDaaS
provided by big data vendors allows companies to
leave the technical details for big data vendors and
focus on their core business needs.
Although big data solves many current problems
regarding high volumes of data, it is a constantly
changing area that is always in development and that
still poses some issues. In this section we present
some of the issues not yet addressed by big data and
cloud computing.
As the amount of data grows at a rapid rate,
keeping all data is physically cost-ineffective.
Therefore, corporations must be able to create
policies to define the life cycle and the expiration date
of data (data governance). Moreover, they should
define who accesses and with what purpose clients’
data is accessed. As data moves to the cloud, security
and privacy become a concern that is the subject of
broad research.
Big data DBMSs typically deal with lots of data
from several sources (variety), and as such
heterogeneity is also a problem that is currently under
study. Other issues currently being investigated are
disaster recovery, how to easily upload data onto the
cloud, and Exaflop computing.
Within this section we provide an overview over
these problems.
Big Data in Cloud Computing: Features and Issues
4.1 Security
Cloud computing and big data security is a current
and critical research topic (Popović & Hocenski,
2015). This problem becomes an issue to corporations
when considering uploading data onto the cloud.
Questions such as who is the real owner of the data,
where is the data, who has access to it and what kind
of permissions they have are hard to describe.
Corporations that are planning to do business with a
cloud provider should be aware and ask the following
a) Who is the Real Owner of the Data and Who has
Access to it?
The cloud provider’s clients pay for a service and
upload their data onto the cloud. However, to
which one of the two stakeholders does data really
belong? Moreover, can the provider use the
client’s data? What level of access has to it and
with what purposes can use it? Can the cloud
provider benefit from that data?
In fact, IT teams responsible for maintaining the
client’s data must have access to data clusters.
Therefore, it is in the client’s best interest to grant
restricted access to data to minimize data access
and guarantee that only authorized personal
access its data for a valid reason.
These questions seem easy to respond to, although
they should be well clarified before hiring a
service. Most security issues usually come from
inside of the organizations, so it is reasonable that
companies analyse all data access policies before
closing a contract with a cloud provider.
b) Where is the Data?
Sensitive data that is considered legal in one
country may be illegal in another country,
therefore, for the sake of the client, there should
be an agreement upon the location of data, as its
data may be considered illegal in some countries
and lead to prosecution.
The problems to these questions are based upon
agreements (Service Level Agreements – SLAs),
however, these must be carefully checked in order to
fully understand the roles of each stakeholder and
what policies do the SLAs cover and not cover
concerning the organization’s data. This is typically
something that must be well negotiated.
Concerning limiting data accesses, (Tu et al.,
2013) and (Popa et al., 2011) came up with an
effective way to encrypt data and run analytical
queries over encrypted data. This way, data access is
no longer a problem since both data and queries are
encrypted. Nevertheless, encryption comes with a
cost, which often means higher query processing
4.2 Privacy
The harvesting of data and the use of analytical tools
to mine information raises several privacy concerns.
Ensuring data security and protecting privacy has
become extremely difficult as information is spread
and replicated around the globe. Analytics often mine
users’ sensitive information such as their medical
records, energy consumption, online activity,
supermarket records etc. This information is exposed
to scrutiny, raising concerns about profiling,
discrimination, exclusion and loss of control (Tene
and Polonetsky, 2012). Traditionally, organizations
used various methods of de-identification
(anonymization or encryption of data) to distance data
from real identities. Although, in recent years it was
proved that even when data is anonymized, it can still
be re-identified and attributed to specific individuals
(Tene and Polonetsky, 2012). A way to solve this
problem was to treat all data as personally identifiable
and subject to a regulatory framework. Although,
doing so might discourage organizations from using
de-identification methods and, therefore, increase
privacy and security risks of accessing data.
Privacy and data protection laws are premised on
individual control over information and on principles
such as data and purpose minimization and limitation.
Nevertheless, it is not clear that minimizing
information collection is always a practical approach
to privacy. Nowadays, the privacy approaches when
processing activities seem to be based on user consent
and on the data that individuals deliberately provide.
Privacy is undoubtedly an issue that needs further
improvement as systems store huge quantities of
personal information every day.
4.3 Heterogeneity
Big data concerns big volumes of data but also
different velocities (i.e., data comes at different rates
depending on its source output rate and network
latency) and great variety. The latter comprehends
very large and heterogeneous volumes of data coming
from several autonomous sources. Variety is one of
the “major aspects of big data characterization
(Majhi and Shial, 2015) which is triggered by the
belief that storing all kinds of data may be beneficial
to both science and business.
Data comes to big data DBMS at different
velocities and formats from various sources. This is
because different information collectors prefer their
own schemata or protocols for data recording, and the
nature of different applications also result in diverse
data representations (Wu et al., 2014). Dealing with
IoTBD 2016 - International Conference on Internet of Things and Big Data
such a wide variety of data and different velocity rates
is a hard task that Big Data systems must handle. This
task is aggravated by the fact that new types of file
are constantly being created without any kind of
standardization. Though, providing a consistent and
general way to represent and explore complex and
evolving relationships from this data still poses a
4.4 Data Governance
The belief that storage is cheap, and its cost is likely
to decline further, is true regarding hardware prices.
However, a big data DBMS does also concern other
expenses such as infrastructure maintenance, energy,
and software licenses (Tallon, 2013). All these
expenses combined comprise the total cost of
ownership (TCO), which is estimated to be seven
times higher than the hardware acquisition costs.
Regarding that the TCO increases in direct
proportion to the growth of big data, this growth must
be strictly controlled. Recall that the “Value” (one of
big data Vs) stands to ensure that only valuable data
is stored, since huge amounts of data are useless if
they comprise no value.
Data Governance came to address this problem by
creating policies that define for how long data is
viable. The concept consists of practices and
organizational polices that describe how data should
be managed through its useful economic life cycle.
These practices comprise three different categories:
1. Structural practices identify key IT and non-IT
decision makers and their respective roles and
responsibilities regarding data ownership, value
analysis and cost management (Morgan
Kaufmann, 2013).
2. Operational practices consist of the way data
governance policies are applied. Typically, these
policies span a variety of actions such as data
migration, data retention, access rights, cost
allocation and backup and recovery (Tallon,
3. Relational practices formally describe the links of
the CIO, business managers and data users in
terms of knowledge sharing, value analysis,
education, training and strategic IT planning.
Data Governance is a general term that applies to
organizations with huge datasets, which defines
policies to retain valuable data as well as to manage
data accesses throughout its life cycle. It is an issue to
address carefully. If governance policies are not
enforced, it is most likely that they are not followed.
Although, there are limits to how much value data
governance can bring, as beyond a certain point
stricter data governance can have counterproductive
4.5 Disaster Recovery
Data is a very valuable business and losing data will
certainly result in losing value. In case of emergency
or hazardous accidents such as earthquakes, floods
and fires, data losses need to be minimal. To fulfil this
requirement, in case of any incident, data must be
quickly available with minimal downtime and loss.
However, although this is a very important issue, the
research in this particular area is relatively low
(Subashini and Kavitha, 2011), (Wood et al., 2010),
(Chang, 2015).
For big corporations it is imperative to define a
disaster recovery plan – as part of the data governance
plan – that not only relies on backups to reset data but
also in a set of procedures that allow quick
replacement of the lost servers (Chang, 2015).
From a technical perspective, the work described
in (Chang, 2015) presents a good methodology,
proposing amulti-purpose approach, which allows
data to be restored to multiple sites with multiple
methods”, ensuring a recovery percentage of almost
100%. The study also states that usually, data
recovery methods use what they call a “single-basket
approach”, which means there is only one destination
from which to secure the restored data.
As the loss of data will potentially result in the
loss of money, it is important to be able to respond
efficiently to hazardous incidents. Successfully
deploying big data DBMSs in the cloud and keeping
it always available and fault-tolerant may strongly
depend on disaster recovery mechanisms.
4.6 Other Problems
The current state of the art of cloud computing, big
data, and big data platforms in particular, prompts
some other concerns. Within this section we discuss
data transference onto the cloud; Exaflop computing,
which presents a major concern nowadays; and
scalability and elasticity issues in cloud computing
and big data:
a) Transferring Data onto a Cloud is a very slow
process and corporations often choose to physically
send hard drives to the data centres so that data can
be uploaded. However, this is neither the most
practical nor the safest solution to upload data onto
the cloud. Through the years there has been an effort
to improve and create efficient data uploading
algorithms to minimize upload times and provide a
secure way to transfer data onto the cloud (Zhang et
Big Data in Cloud Computing: Features and Issues
al., 2013), however, this process still remains a major
b) Exaflop Computing (Geller, 2011), (Schilling,
2014) is one of today’s problems that is subject of
many discussions. Today’s supercomputers and
clouds can deal with petabyte data sets, however,
dealing with exabyte size datasets still raises lots of
concerns, since high performance and high bandwidth
is required to transfer and process such huge volumes
of data over the network. Cloud computing may not
be the answer, as it is believed to be slower than
supercomputers since it is restrained by the existent
bandwidth and latency. High performance computers
(HPC) are the most promising solutions, however the
annual cost of such a computer is tremendous.
Furthermore, there are several problems in designing
exaflop HPCs, especially regarding efficient power
consumption. Here, solutions tend to be more GPU
based instead of CPU based. There are also problems
related to the high degree of parallelism needed
among hundred thousands of CPUs.
Analysing Exabyte datasets requires the
improvement of big data and analytics which poses
another problem yet to resolve.
c) Scalability and Elasticity in cloud computing
and in particular regarding big data management
systems is a theme that needs further research as the
current systems hardly handle data peaks
automatically. Most of the time, scalability is
triggered manually rather than automatically and the
state-of-the-art of automatic scalable systems shows
that most algorithms are reactive or proactive and
frequently explore scalability from the perspective of
better performance. However, a proper scalable
system would allow both manual and automatic
reactive and proactive scalability based on several
dimensions such as security, workload rebalance (i.e.:
the need to rebalance workload) and redundancy
(which would enable fault tolerance and availability).
Moreover, current data rebalance algorithms are
based on histogram building and load equalization
(Mahesh et al., 2014). The latter ensures an even load
distribution to each server. However, building
histograms from each server’s load is time and
resource expensive and further research is being
conducted on this field to improve these algorithms.
4.7 Research Challenges
As discussed in Section 3, cloud and big data
technologies work very well together. Even though
the partnership between these two technologies have
been established, both still pose some challenges.
Table 1 summarizes the issues of big data and
cloud computing nowadays. The first column
specifies the existing issues whereas the second
describes the existing solutions and the remaining
present the advantages and disadvantages of each
Concerning the existing problems, we define
some of the possible advances in the next few years:
Security and Privacy can be resolved using data
encryption. However, a new generation of
systems must ensure that data is accessed quickly
and that encryption does not affect processing
times so badly;
Big Data variety can be addressed by using data
standardization. This, we believe, is the next step
to minimize the impact of heterogeneity;
Data governance and data recovery plans are
difficult to manage and implement, but as Big
Data become a de facto technology, companies
are starting to understand the need of such plans.;
New and secure QoS (quality of service) based
data uploading mechanisms may be the answer to
ease data uploading onto the cloud;
Exaflop computing is a major challenge that
involves governments funding and which is in its
best interest. The best solutions so far use HPCs
and GPUs;
Scalability and elasticity techniques exist and are
broadly used by several Big Data vendors such as
Amazon and Microsoft. The major concern relies
upon developing fully automatic reactive and
proactive systems that are capable of dealing with
load requirements automatically.
With data increasing on a daily base, big data systems
and in particular, analytic tools, have become a major
force of innovation that provides a way to store,
process and get information over petabyte datasets.
Cloud environments strongly leverage big data
solutions by providing fault-tolerant, scalable and
available environments to big data systems.
Although big data systems are powerful systems
that enable both enterprises and science to get insights
over data, there are some concerns that need further
investigation. Additional effort must be employed in
developing security mechanisms and standardizing
data types. Another crucial element of Big Data is
scalability, which in commercial techniques are
mostly manual, instead of automatic.
IoTBD 2016 - International Conference on Internet of Things and Big Data
Table 1: Big data issues.
Issues Existent solutions Advantages Disadvantages
Based on SLAs and data
Data is encrypted
Querying encrypted data is time-
-User consent
Provides a reasonable privacy
or transfers responsibility to the
It was proved that most de-identification
mechanisms can be reverse engineered
One of the big data systems'
characteristics is the ability to
deal with different data
coming at different velocities
The major types of data are
covered up
It is difficult to handle such variety of data
and such different velocities
Data Governance
Data governance documents
-Specify the way data is
-Specify data access policies;
-Role specification;
-Specify data life cycle
-The data life cycle is not easy to define;
-Enforcing data governance policies so
much can lead to counterproductive
Disaster recovery
Recovery plans
Specify the data recovery
locations and procedures
Normally there is only one destination
from which to secure data
Data Uploading
-Send HDDs to the cloud
-Upload data through the
Physically sending the data to
the cloud provider is quicker
than uploading data but it is
much more unsecure
Physically sending data to the cloud
provider is dangerous as HDDs can suffer
damage from the trip.
- Uploading data through the network is
time-consuming and, without encryption,
can be insecure
High Data
(Exabyte datasets)
-Cloud computing
Cloud computing is not so cost
expensive as HPCs but HPCs
are believed to handle Exabyte
datasets much better
HPCs are very much expensive ant its
total cost over a year is hard to maintain.
On the other hand, cloud is believed that
cannot cope with the requirements for
such huge datasets
Scalability exists at the three
levels in the cloud stack. At
the Platform level there is:
horizontal (Sharding) and
vertical scalability
Scalability allows the system to
grow on demand
Scalability is mainly manual and is very
much static. Most big data systems must
be elastic to cope with data changes
There are several elasticity
techniques such as Live
Migration, Replication and
Elasticity brings the system the
capability of accommodating
data peaks
Most load variations assessments are
manually made, instead of automatized
Further research must be employed to tackle this
problem. Regarding this particular area, we are
planning to use adaptable mechanisms in order to
develop a solution for implementing elasticity at
several dimensions of big data systems running on
cloud environments. The goal is to investigate the
mechanisms that adaptable software can use to trigger
scalability at different levels in the cloud stack. Thus,
accommodating data peaks in an automatic and
reactive way.
Within this paper we provide an overview of big
data in cloud environments, highlighting its
advantages and showing that both technologies work
very well together but also presenting the challenges
faced by the two technologies.
Big Data in Cloud Computing: Features and Issues
This research is supported by Early Bird project
funding, CMU Portugal, Applying Stitch to Modern
Distributed Key-Value Stores and was hosted by
Carnegie Mellon University under the program for
CMU-Portugal undergraduate internships
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