Cloud Computing and Big Data: Technologies, Applications and Security

406 Pages·2019·27.461 MB·English

by  Mostapha Zbakh, Mohammed Essaaidi, Pierre Manneback, Chunming Rong

Lecture Notes in Networks and Systems 49
Mostapha Zbakh 
Mohammed Essaaidi 
Pierre Manneback · Chunming Rong
Editors
Cloud Computing
and Big Data:
Technologies,
Applications
and Security
Lecture Notes in Networks and Systems
Volume 49
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
The series “Lecture Notes in Networks and Systems” publishes the latest
developmentsinNetworksandSystems—quickly,informallyandwithhighquality.
Originalresearchreportedin proceedings andpost-proceedings represents thecore
of LNNS.
Volumes published in LNNS embrace all aspects and subfields of, as well as
new challenges in, Networks and Systems.
The series contains proceedings and edited volumes in systems and networks,
spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor
Networks, Control Systems, Energy Systems, Automotive Systems, Biological
Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems,
Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems,
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dissemination of research output.
Theseriescoversthetheory,applications,andperspectivesonthestateoftheart
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Advisory Board
FernandoGomide,DepartmentofComputerEngineeringandAutomation—DCA,Schoolof
Electrical and Computer Engineering—FEEC, University of Campinas—UNICAMP,
São Paulo, Brazil
e-mail: [email protected]
Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University,
Istanbul,Turkey
e-mail: [email protected]
Derong Liu, Department of Electrical and Computer Engineering, University of Illinois
at Chicago, Chicago, USA and Institute of Automation, Chinese Academy of Sciences,
Beijing,China
e-mail: [email protected]
WitoldPedrycz,DepartmentofElectricalandComputerEngineering,UniversityofAlberta,
Alberta, Canada and Systems Research Institute, Polish Academy of Sciences, Warsaw,
Poland
e-mail: [email protected]
Marios M. Polycarpou, KIOS Research Center for Intelligent Systems and Networks,
DepartmentofElectricalandComputerEngineering,UniversityofCyprus,Nicosia,Cyprus
e-mail: [email protected]
Imre J.Rudas, ÓbudaUniversity, BudapestHungary
e-mail: [email protected]
Jun Wang, Departmentof Computer Science, CityUniversity ofHong Kong
Kowloon, HongKong
e-mail: [email protected]
More information about this series at http://www.springer.com/series/15179
Mostapha Zbakh Mohammed Essaaidi
(cid:129)
Pierre Manneback Chunming Rong
(cid:129)
Editors
Cloud Computing and Big
Data: Technologies,
Applications and Security
123
Editors
Mostapha Zbakh Pierre Manneback
ENSIASCollegeof Engineering Department ofComputer Science
Mohammed VUniversity Polytechnic of Mons
Agdal, Rabat, Morocco Mons,Belgium
Mohammed Essaaidi ChunmingRong
ENSIASCollegeof Engineering Department ofElectrical Engineering
Mohammed VUniversity andComputer Science
Agdal, Rabat, Morocco University of Stavanger
Stavanger, Norway
ISSN 2367-3370 ISSN 2367-3389 (electronic)
Lecture Notesin Networks andSystems
ISBN978-3-319-97718-8 ISBN978-3-319-97719-5 (eBook)
https://doi.org/10.1007/978-3-319-97719-5
LibraryofCongressControlNumber:2018950099
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Preface
Cloud computing has recently gained great attention from both academia and IT
industryasanewinfrastructurerequiringsmallerinvestmentsinhardwareplatform,
staff training, or licensing new software tools. It is a new paradigm that has fol-
lowed grid computing technology that has made a revolution in both data storage
and computation.
Cloud computing can be seen as any subscription-based or pay-per-use service
that extends the Internet existing capabilities. It can be used as a
“software-as-service (SaaS Cloud)” or as a “platform-as-service (PaaS Cloud)” or
as an “infrastructure-as-service (IaaS Cloud).” Data-storage-as-a-service (DaaS
Cloud) has also emerged in the past few years to provide users with storage
capabilities.
In parallel with this progress, big data technologies have been developed and
deployedsorapidlyandrelyheavilyoncloudcomputingplatformsforbothstorage
and processing of data.
These technologies are widely and increasingly used for applications and ser-
vices development in many fields, such as Web, health, and energy.
Inotherwords,cloudcomputingandbigdatatechnologiesareconsideredwithin
the current and future research frontiers. They also cover several fields including
business, scientific research, and public and private administrations.
This book addresses topics related to cloud and big data technologies, archi-
tectures and applications including distributed computing and data centers, cloud
infrastructure and its security, end-user services, big data and their applications.
Most part of this manuscript is devoted to all security aspects related to cloud
computing and big data.
Thisbookaimstobeanup-to-datereferenceforresearchersandendusersonall
aspects related to cloud computing and big data technologies and application.
v
vi Preface
Topics
(cid:129) Cloud architecture
(cid:129) Mobile computing
(cid:129) Green computing
(cid:129) Resource allocation
(cid:129) HPC
(cid:129) GPU
(cid:129) Energy efficiency
(cid:129) Big data
(cid:129) Security and privacy
Target Audience
Information systems directors, academicians, researchers, students, developers,
policy-makerswillfindthisbookveryuseful,throughitstwenty-fourchaptersthat
cover several theoretical and experimental studies and researches in the fields of
cloud computing, big data, and security.
Organization of the book
Thisbookcoversseveralconceptsandfeaturesrelatedtocloudcomputingandbig
data theoretical background, technologies, and applications.It alsoaddresses some
advanced security issues related to them such as data privacy, access control, and
fault tolerance. It is organized as follows:
Chapter1presentstwohighlyefficientidentity-basedsigncryptionschemesthat
can be used as a building block for a proxy re-encryption scheme. These schemes
allowuserstostoresignedandencrypteddatainthecloud,wherethecloudserver
provider is able to check the authentication but not to derive the content of the
message.
Chapter2presentsathoroughstudyallowingtoidentifyasetofsecurityrisksin
acloudenvironmentinastructuredway,byclassifyingthembytypesofserviceas
well as by deployment and hosting models.
Chapter 3 proposes a new effective security model for mobile cloud
database-as-a-service(DBaaS)inwhichausercanchangehispassword,whenever
demanded. Furthermore, security analysis realizes the feasibility of the proposed
model for DBaaS and achieves efficiency. It also proposes an efficient authenti-
cation scheme to solve the authentication problem in MCC.
Chapter4proposesanewschemethataimstoimproveFADEsecuritybyusing
Trusted Platform Module (TPM). The proposed scheme provides a value-added
security layer compared to FADE with less overhead computational time.
Preface vii
Chapter 5 presents some new approaches for data protection in a cloud and
discusses a new secure architecture based on three layers.
Chapter 6 introduces a middleware solution that provides a set of services for
cost-effective management of crowdsensing data for mobile cloud computing.
Chapter 7 proposes a solution based on fragmentation to support a distributed
image processing architecture, as well as data privacy. The proposed methods
combine a clustering method, the fuzzy C-means (FCM) algorithm, and a genetic
algorithm (GA) to satisfy quality of service (QoS) requirements. This solution
reducestheexecutiontimeandsecurityproblems.Thisisaccomplishedbyusinga
multi-cloud system and parallel image processing approach.
Chapter 8 compares different scenarios of collaborative intrusion detection
systemsproposedalreadyinpreviousresearchwork.Thisstudyiscarriedoutusing
CloudAnalyst which is developed to simulate large-scale cloud applications in
order to study the behavior of such applications under various deployment con-
figurations and to choose the most efficient implementation in terms of response
time and the previous parameters.
Chapter 9 presents a t-closeness method for multiple sensitive numerical
(MSN)attributes.Itcouldbeappliedtobothsingleandmultiplesensitivenumerical
attributes. In the case where the data set contains attributes with high correlation,
then this method will be applied only to one numerical attribute.
Chapter 10 proposes a conceptual model with architectural elements and pro-
posedtoolsformonitoringinReal-TimeAnalyticalProcessing(RTAP)modesmart
areas. This model is based on lambda architecture, in order to resolve the problem
of latency which is imposed in transactional requests (GAB network).
Chapter 11 presents a new noise-free fully homomorphic encryption scheme
based on quaternions. Trans-ciphering is supposed to be an efficient solution to
optimizedatastorageinthecontextofoutsourcingcomputationstoaremotecloud
computingasitisconsideredapowerfultooltominimizeruntimeintheclientside.
Chapter 12 designs an approach that embraces model-driven engineering prin-
ciples toautomatethegenerationoftheSLAcontractanditsreal-timemonitoring.
It proposes three languages dedicated, respectively, to the customer, the supplier,
and the contract specification by using machine learning to learn QoS behavior at
runtime.
Chapter 13 proposes a new approach for content-based images indexing. It
provides a parallel and distributed computation using Hadoop Image Processing
Interface (HIPI) framework and Hadoop Distributed File System (HDFS) as a
storage system, and exploiting graphics processing units (GPUs) high power.
Chapter14drawsanewmethodtoclassifythetweetsintothreeclasses:positive,
negative, or neutral in a semantic way using WordNet and AFINN1 dictionaries,
and in a parallel way using Hadoop framework with Hadoop Distributed File
System (HDFS) and MapReduce programming model. It also proposes a new
sentiment analysis approach by combining several approaches and technologies
such as information retrieval, semantic similarity, opinion mining or sentiment
analysis and big data.
viii Preface
Chapter15presentsparallelanddistributedexternalclusteringvalidationmodels
based on MapReduce for three indexes, namely: F-measure, normalized mutual
information, and variation of information.
Chapter 16 conducts a systematic literature review (SLR) of workflow
scheduling strategies that have been proposed for cloud computing platforms to
help researchers systematically and objectively gather and aggregate research evi-
dences about this topic. It presents a comparative analysis of the studied strategies
and highlights workflow scheduling issues for further research.
Chapter 17 presents different techniques to achieve green computing with an
emphasis on cloud computing.
Chapter 18 exposes a GPU- and multi-GPU-based method for both sparse and
dense optical flow motion tracking using the Lucas–Kanade algorithm. It allows
real-time sparse and dense optical flow computation on videos in Full HD or even
4K format.
Chapter 19 examines multiple machine learning algorithms, explores their
applications in the various supply chain processes, and presents a long short-term
memory model for predicting the daily demand in a Moroccan supermarket.
Chapter 20 evaluates the performance of dynamic schedulers proposed by
StarPU library and analyzes the scalability of PCG algorithm. It shows the choice
of the best combination of resources in order to improve their performance.
Chapter21proposesamachinelearningapproachtobuildamodelforpredicting
the runtime of optimization algorithms as a function of problem-specific instance
features.
Chapter22formalizestheWebservicecompositionproblemasasearchproblem
inanAND/ORservicedependencygraph,wherenodesrepresentavailableservices
and arcs represent the semantic input/output dependencies among these services.
Chapter 23 presents a text-to-speech synthesizer for Moroccan Arabic based on
NLP rule-based and probabilistic models. It contains a presentation of Moroccan
Arabic linguistics, an analysis of NLP techniques in general, and Arabic NLP
techniques in particular.
Chapter 24 presents a context-aware routing protocol based on the particle
swarm optimization (PSO) in random waypoint (RWP)-based dynamic WSNs.
Mostapha Zbakh
Mohammed Essaaidi
Pierre Manneback
Chunming Rong
Acknowledgments
The editors would like to thank all of the authors who submitted their chapters to
this book. We thank also all reviewers for their time and tangible work they have
made to successfully complete the reviewing process. We also sincerely thank
Dr. Thomas Ditzinger, Springer Executive Editor, Interdisciplinary and Applied
Sciences&Engineering,andMrsVarshaPrabakaran,SpringerProjectCoordinator
in Books Production Service for the opportunity of having this book, for their
assistanceduringitspreparationprocessandforgivingtheauthorstheopportunity
to publish their works in Springer Book in LNNS series. Many thanks also to the
Editorial Board and Springer’s staff for their support. Finally, we would like to
thank the following Editorial Committee members for professional and timely
reviews: Youssef Baddi (Morocco), An Braeken (Belgium), Dan Grigoras (UK),
MunirKashif(SaudiArabia),MaKun(China),SidiAhmedMahmoudi(Belgium),
MahmoudNasser(Morocco),YassirSamadi(Morocco),ClaudeTadonki(France),
SaidTazi(France),AbdellatifElGhazi(Morocco),AbdelmounaamRezgui(USA),
Helen Karatza (Greece), and Abdellah Touhafi (Belgium).
ix