Cloud computing and big data: Technologies and applications

Abstract

During the last decade, cloud computing has gained great attention from academia, industry, and government as a new infrastructure requiring slighter investments in hardware platform, staff training, or licensing new software tools. It is defined in the work of Borko and Armando1 as a new computing paradigm in which resources are provided as services and considered as an infrastructure characterized by its availability, ease of use, and no installation or configuration required from end users. In other words, cloud computing can be seen as a collection of resources and applications that offer the following services.2, 3 SaaS (Software-as-a-Service) that allows end users to run applications from their PCs, laptops, PDAs, smartphones, or tablets. IaaS (Infrastructure-as-a-Service) that allows users to use cloud's resources as a service. PaaS (Platform-as-a-Service) that gives the users a complete development platform including computing resources and operating systems to develop new services and applications. There are 4 types of cloud computing: public, private, hybrid, and community clouds. Public or external cloud allows all off-site users to use available resources over the Internet via Web applications or Web services. Private or internal cloud could be built for the exclusive use of 1 client, which takes responsibility for its management and control. The hybrid cloud combines multiple public and private cloud platforms. Finally, community cloud is a cloud-hosting type in which there is a mutual sharing of the setup among many organizations belonging to a particular community such as trading firms and banks. All these models are devoted to providing users with on-demand resources while ensuring Quality of Service (QoS) in hardware/CPU performance, bandwidth, and memory capacity together with autonomous and transparent system management. However, despite various efforts to improve the cloud performances, there are still several challenges that need to be addressed. For example, the scalability issue that can be addressed by integrating high-performance platforms and techniques to increase the computing performance and data storage. Furthermore, security and privacy issues and concerns such as Distributed Denial of Service (DDOS) and phishing attacks are considered among the biggest challenges against the widespread adoption of cloud computing services by organizations and customers. In parallel to the rapid development and deployment of cloud services that provide users with access to services (all the time, everywhere, and in a transparent way), the high volume of data that are generated from physical (ie, devices embedded in the surrounding physical environment and/or carried by the user) and Web sensors (ie, social media like Facebook and Twitter) reinforce the usefulness of cloud computing for high performance and real-time data processing. This type of data is named big data because of the 4 main V's: Volume, Velocity, Variety, and Veracity.2, 4 In other word, cloud computing is recognized by the community as a platform for big data storage and processing. Recent surveys stated that cloud computing will play an important role as it provides organizations with the ability to analyze and store data economically and efficiently. For example, remotely processing data was introduced as data have started to be migrated and managed in the cloud. For example, Digital Communications Inc (DCI) stated that by 2020, a significant portion of digital data will be managed in the cloud, and even if a byte in the digital universe is not stored in the cloud, it will pass, at some point, through the cloud. Many solutions have been proposed and developed to improve computation performance and storage of big data.2 Some of them tend to improve algorithms efficiency, provide new distributed paradigms, or develop powerful processing infrastructures. This special issue is intended to provide an overview of some key topics and state-of-the-art of recent advances in subjects relevant to cloud computing and big data. The general objectives are to address, explore, and exchange research findings on the challenges and current state-of-the-art of cloud computing and big data. Special emphasis is put on topics as diverse as storage and computing, architectures, performance, and applications. This special issue includes articles addressing the state-of-the-art in cloud computing and big data techniques and tools. A total of 10 papers were invited based on the original presentations at the 2015 International Conference of Cloud Computing Technologies and Applications (CloudTech'15).5 It is worth noting that CloudTech'15 is one of the most successful events on cloud computing and big data technologies and applications organized in the wonderful imperial Moroccan City of Marrakech on June 2 to 4, 2015. It addresses topics related to cloud technologies and big data such as architecture, processing, applications, and services including distributed computing and data centers, cloud security, end-user services, and big data analytics tools. CloudTech'15 received a total submission of 150 papers, including invited papers and regular papers from 15 countries. From this set of submitted papers, the Technical Program Committee selected 70 papers based on their originality, innovative relevance, and clarity of presentation. CloudTech'15 technical program included, besides the accepted papers, 8 keynote talks covering different hot topics related to cloud computing and big data technologies, applications, and services. Authors of the selected papers were invited to submit an expanded version to this special issue. These papers were reviewed in the same manner as regular submissions to this edition. After rigorous reviews, 7 papers were accepted for publication and are briefly described in the rest of this section. Mobile cloud computing can be considered as the next generation of cloud computing. In this context, Badawy et al6 in their article Optimizing Thin Client Caches for Mobile Cloud Computing examine user requirements for accessing the cloud through thin clients and mobile devices. They highlight first the research and development requirements that are required in the area of devices' architectures and then propose a novel efficient toolchain, called CERE (CachE Recommendation Engine), using genetic algorithms. They show that CERE tool could maximize the performance of a Web browser navigating to a set of popular websites running on a single ARM (Advanced RISC Machine) core. As a second contribution, Rossi et al,7 in their article A Service Oriented Cloud-based Architecture for Mobile Geolocated Emergency Services, propose a service-oriented architecture that can be used as a reference for implementing back-end of mobile applications that require sending crowdsourced geolocated reports. They implement a real application and then evaluate its performance under Microsoft Azure by varying the user load and main deployment parameters. New techniques and concepts are required to cope with challenges that are still not addressed by current approaches, mainly tasks and job scheduling, efficient power management, the use and integration of heterogeneous technologies, scalability in connections, reliable communications, data privacy, replication, and processing. For example, Babamir and Khalili,8 in their article Optimal scheduling workflows in Cloud Computing Environment Using Pareto based Grey Wolf Optimizer, propose an optimal scheduling workflow algorithm for cloud computing environments. This algorithm extends the recent heuristic algorithm, Gray Wolves Optimization (GWO), by considering dependency graph of workflow tasks. They used the WorkflowSim simulator, and the results show that the proposed algorithm outperforms 2 other task-scheduling algorithms in cost and throughput. Privacy and security in cloud computing is one of the most important issues, which still need to be tackled. As a contribution in this field, Souza et al, in their paper9 Privacy-ensuring electronic health records on the cloud, present a solid access control framework in the context of Electronic Health Records (EHRs). This framework encompasses hybrid cryptography at the client side and an authentication technique to guarantee a secure key management protocol. They use the homomorphic and order-preserving encryption as a viable solution for the computation of regular searches over EHRs in the cloud, while preserving data confidentiality and patients' privacy. Furthermore, they introduce a trusted element, in the form of a browser extension that can prevent attacks from malicious providers. An HER-based prototype was developed to show the usefulness of the proposed framework. The development of cloud computing application for Internet-of-the-Things (IoT) and big data processing is another topic that requires the integration of new techniques and concepts. For example, Balouek-Thomert et al, in their article10 Nu@ge: A container-based cloud computing service federation, introduce [email protected], a research project aiming at building a federation of container-sized data centers. The authors first present an overview of the concepts behind [email protected] and then provide a software stack that enables companies to interconnect independent data centers. The proposed architecture enables cooperation between local customized-cloud managers and a federation-wide middleware. A prototype of a container-sized data center has been deployed to show the [email protected] efficiency. Gomes et al, in their article11 A Comprehensive and Scalable Middleware for Ambient Assisted Living Based on Cloud Computing and IoT, introduce a middleware for Ambient-Assisted Living (AAL) based on cloud computing and IoT aspects to cope with several challenges, mainly scalability, reliability, and the suitability to different AAL scenarios. The authors handled these challenges through 2 components: the Scalable Data Distribution Layer (SDDL) and M-Hub. The first relies on cloud computing principles to provide scalable communication and storage and compute services, whereas the second is an IoT middleware service that runs on mobile devices. In other words, authors envision the emerging technique of health care monitoring systems using the 2 major components: a local service running on small and portable devices to wirelessly connect both the patient and a remote cloud; and the cloud services backed by many virtual machines running on data centers. The proposed software infrastructure was validated through a set of experiments, and results are reported to show its scalability and its ability to discover and connect to smart objects. Ma and Yang, in their article12 Stream-based Live Data Replication Approach of In-memory Cache, tackle the traditional data replication issue by investigating an approach from in-memory data cache. More precisely, the authors propose a live data replication approach of in-memory document stores using stream processing framework. Some experiments have been conducted and the results reported show that the proposed approach is more suitable for the replication of continuous in-stream changed data compared with MapReduce-based batch replication. The papers presented in this special issue provide research works related to recent advances in cloud computing and big data. In particular, these research works focus on different topics including resources optimization, privacy and security, mobile cloud, cloud computing, and big data applications and services. We hope that the readers of this special issue will benefit from the research ideas and concepts presented in these research works. The guest editors of this special issue would like to express their special thanks to all of the authors who submitted their papers to this special issue. We are also grateful to the reviewers for their time and dedication without which the review process would not have been successfully completed on time. On behalf of the conference Technical Program Committees, we extend our sincere thanks to all the authors of CloudTech'15 as well as to the authors of this special issue for sharing their excellent research works. We also sincerely thank the editor in chief of this journal, Prof W Fox for the opportunity of having this special issue, his assistance during its preparation process, and for giving the authors the opportunity to present their research work in the International Journal of Concurrency and Computation: Practice and Experience (CCPE). Furthermore, we wish to thank the CCPE Editorial Board and the Journal's staff for their support. Finally, we acknowledge the following Editorial Committee members for their professional and timely reviews: Zhao Dongfang (USA), Munir Kashif (Saudi Arabia), Alvares Frederico (France), Mezzour Ghita (Morocco), Petcu Dana (Romania), Aiello Marco (Netherlands), Jin Hai (China), Bellavista Paolo (Italy), Gao Chen (China), Chung Yeh-Ching (Taiwan), Zine-Dine Khalid (Morocco), Narayana Vikram (USA), Abid Mohamed (Morocco), Vasanth Iyer (USA), Mousannif Hajar (Morocco), Iturriaga Santiago (Uruguay), Li Tonglin (USA), and Durillo Juan (Austria).