Special Issue "Advances and Innovations in Cloud Computing Technologies and Applications (CloudTech 2018)"

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (15 April 2019).

Special Issue Editors

Prof. Dr. Abdellah Touhafi
Website
Guest Editor
Vrije Universiteit Brussel, Brussels, Belgium
Interests: security; wireless sensor network; network; sensors; computer engineering; FPGA; reconfigurable computing; hardware
Special Issues and Collections in MDPI journals
Prof. Dr. An Breaken
Website
Guest Editor
Vrije Universiteit Brussel, Brussels, Belgium
Interests: network security; algorithms; security; information security; wireless sensor network; cryptography; algebra; encryption; authentication; embedded systems; cryptology; smart card
Prof. Dr. Olivier Debeir
Website
Guest Editor
Université Libre de Bruxelles, Brussels, Belgium
Interests: deep convolutional networks; machine learning; thermal image superresolution; people detection in video images; microscopy image analysis; automatic document analysis; augmented reality applied to neuropathic pain treatment
Prof. Dr. Mostapha Zbakh
Website
Guest Editor
National College of IT (ENSIAS), Rabat, Morocco
Interests: security; cloud computing; GPU-computing; parallel computing
Prof. Dr. Mohamed Essaaidi
Website
Guest Editor
Abdelmalek Essaaidi University—Electronics & Micro Group, P.O. Box 2121, Tetuan, Morocco
Interests: cloud computing; IoT; WiMax

Special Issue Information

Dear Colleagues,

Cloud computing is becoming mainstream and has proven its ability to solve many challenges related to service-centered computing approaches. New techniques and methods for advanced scheduling, real time behavior, big data-based machine learning, and IoT-related cloud services are new trends.

CloudTech will address a rich selection of topics such as cloud computing technologies, mobile cloud computing, cloud security, big data, IoT, distributed and parallel computing techniques, HPC, end-user services, and much more.

Selected papers presented at the CloudTech 2018 (http://www.cloudtechconference.org/) are invited to submit their extended versions to this Special Issue of the journal Computers. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Computers and collected together on the Special Issue website.

Conference papers should be cited and noted on the first page of the paper; authors are asked to disclose that it is a conference paper in their cover letter and include a statement on what has been changed compared to the original conference paper. Please note that the submitted extended paper should contain at least 50% new content (e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases) and not exceed 30% copy/paste from the conference paper.

Prof. Dr. Abdellah Touhafi
Prof. An Breaken
Prof. Olivier Debeir
Prof. Mostapha Zbakh
Prof. Mohamed Essaaidi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

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Research

Open AccessArticle
MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures
Computers 2019, 8(3), 52; https://doi.org/10.3390/computers8030052 - 29 Jun 2019
Cited by 6
Abstract
Breast tumor segmentation in medical images is a decisive step for diagnosis and treatment follow-up. Automating this challenging task helps radiologists to reduce the high manual workload of breast cancer analysis. In this paper, we propose two deep learning approaches to automate the [...] Read more.
Breast tumor segmentation in medical images is a decisive step for diagnosis and treatment follow-up. Automating this challenging task helps radiologists to reduce the high manual workload of breast cancer analysis. In this paper, we propose two deep learning approaches to automate the breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building two fully convolutional neural networks (CNN) based on SegNet and U-Net. The obtained models can handle both detection and segmentation on each single DCE-MRI slice. In this study, we used a dataset of 86 DCE-MRIs, acquired before and after two cycles of chemotherapy, of 43 patients with local advanced breast cancer, a total of 5452 slices were used to train and validate the proposed models. The data were annotated manually by an experienced radiologist. To reduce the training time, a high-performance architecture composed of graphic processing units was used. The model was trained and validated, respectively, on 85% and 15% of the data. A mean intersection over union (IoU) of 68.88 was achieved using SegNet and 76.14% using U-Net architecture. Full article
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Open AccessArticle
Cloud-Based Image Retrieval Using GPU Platforms
Computers 2019, 8(2), 48; https://doi.org/10.3390/computers8020048 - 14 Jun 2019
Cited by 2
Abstract
The process of image retrieval presents an interesting tool for different domains related to computer vision such as multimedia retrieval, pattern recognition, medical imaging, video surveillance and movements analysis. Visual characteristics of images such as color, texture and shape are used to identify [...] Read more.
The process of image retrieval presents an interesting tool for different domains related to computer vision such as multimedia retrieval, pattern recognition, medical imaging, video surveillance and movements analysis. Visual characteristics of images such as color, texture and shape are used to identify the content of images. However, the retrieving process becomes very challenging due to the hard management of large databases in terms of storage, computation complexity, temporal performance and similarity representation. In this paper, we propose a cloud-based platform in which we integrate several features extraction algorithms used for content-based image retrieval (CBIR) systems. Moreover, we propose an efficient combination of SIFT and SURF descriptors that allowed to extract and match image features and hence improve the process of image retrieval. The proposed algorithms have been implemented on the CPU and also adapted to fully exploit the power of GPUs. Our platform is presented with a responsive web solution that offers for users the possibility to exploit, test and evaluate image retrieval methods. The platform offers to users a simple-to-use access for different algorithms such as SIFT, SURF descriptors without the need to setup the environment or install anything while spending minimal efforts on preprocessing and configuring. On the other hand, our cloud-based CPU and GPU implementations are scalable, which means that they can be used even with large database of multimedia documents. The obtained results showed: 1. Precision improvement in terms of recall and precision; 2. Performance improvement in terms of computation time as a result of exploiting GPUs in parallel; 3. Reduction of energy consumption. Full article
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Open AccessArticle
Modelling and Simulation of a Cloud Platform for Sharing Distributed Digital Fabrication Resources
Computers 2019, 8(2), 47; https://doi.org/10.3390/computers8020047 - 12 Jun 2019
Cited by 1
Abstract
Fabrication as a Service (FaaS) is a new concept developed within the framework of the NEWTON Horizon 2020 project. It is aimed at empowering digital fabrication laboratories (Fab Labs) by providing hardware and software wrappers to expose numerically-controlled expensive fabrication equipment as web [...] Read more.
Fabrication as a Service (FaaS) is a new concept developed within the framework of the NEWTON Horizon 2020 project. It is aimed at empowering digital fabrication laboratories (Fab Labs) by providing hardware and software wrappers to expose numerically-controlled expensive fabrication equipment as web services. More specifically, FaaS leverages cloud and IoT technologies to enable a wide learning community to have remote access to these labs’ computer-controlled tools and equipment over the Internet. In such context, the fabrication machines can be seen as networked resources distributed over a wide geographical area. These resources can communicate through machine-to-machine protocols and a centralized cloud infrastructure and can be digitally monitored and controlled through programmatic interfaces relying on REST APIs. This paper introduces FaaS in the context of Fab Lab challenges and describes FaaS deployment within NEWTON Fab Labs, part of the NEWTON European Horizon 2020 project on technology enhanced learning. The NEWTON Fab Labs architecture is described in detail targeting software, hardware and network architecture. The system has been extensively load-tested simulating real use-case scenarios and it is presently in production. In particular, this paper shows how the measured data has been used to build a simulation model to estimate system performance and identify possible bottlenecks. The measurements performed show that the platform delays exhibit a tail distribution with Pareto-like behaviour; this finding has been used to build a simple mathematical model and a simulator on top of CloudSim to estimate the latencies of the critical paths of the NEWTON Fab Lab platform under several load conditions. Full article
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Open AccessFeature PaperArticle
An Efficient Energy-Aware Tasks Scheduling with Deadline-Constrained in Cloud Computing
Computers 2019, 8(2), 46; https://doi.org/10.3390/computers8020046 - 10 Jun 2019
Cited by 3
Abstract
Nowadays, Cloud Computing (CC) has emerged as a new paradigm for hosting and delivering services over the Internet. However, the wider deployment of Cloud and the rapid increase in the capacity, as well as the size of data centers, induces a tremendous rise [...] Read more.
Nowadays, Cloud Computing (CC) has emerged as a new paradigm for hosting and delivering services over the Internet. However, the wider deployment of Cloud and the rapid increase in the capacity, as well as the size of data centers, induces a tremendous rise in electricity consumption, escalating data center ownership costs and increasing carbon footprints. This expanding scale of data centers has made energy consumption an imperative issue. Besides, users’ requirements regarding execution time, deadline, QoS have become more sophisticated and demanding. These requirements often conflict with the objectives of cloud providers, especially in a high-stress environment in which the tasks have very critical deadlines. To address these issues, this paper proposes an efficient Energy-Aware Tasks Scheduling with Deadline-constrained in Cloud Computing (EATSD). The main goal of the proposed solution is to reduce the energy consumption of the cloud resources, consider different users’ priorities and optimize the makespan under the deadlines constraints. Further, the proposed algorithm has been simulated using the CloudSim simulator. The experimental results validate that the proposed approach can effectively achieve good performance by minimizing the makespan, reducing energy consumption and improving resource utilization while meeting deadline constraints. Full article
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Open AccessArticle
Autonomous Wireless Sensor Networks in an IPM Spatial Decision Support System
Computers 2019, 8(2), 43; https://doi.org/10.3390/computers8020043 - 28 May 2019
Abstract
Until recently data acquisition in integrated pest management (IPM) relied on manual collection of both pest and environmental data. Autonomous wireless sensor networks (WSN) are providing a way forward by reducing the need for manual offload and maintenance; however, there is still a [...] Read more.
Until recently data acquisition in integrated pest management (IPM) relied on manual collection of both pest and environmental data. Autonomous wireless sensor networks (WSN) are providing a way forward by reducing the need for manual offload and maintenance; however, there is still a significant gap in pest management using WSN with most applications failing to provide a low-cost, autonomous monitoring system that can operate in remote areas. In this study, we investigate the feasibility of implementing a reliable, fully independent, low-power WSN that will provide high-resolution, near-real-time input to a spatial decision support system (SDSS), capturing the small-scale heterogeneity needed for intelligent IPM. The WSN hosts a dual-uplink taking advantage of both satellite and terrestrial communication. A set of tests were conducted to assess metrics such as signal strength, data transmission and bandwidth of the SatCom module as well as mesh configuration, energetic autonomy, point to point communication and data loss of the WSN nodes. Finally, we demonstrate the SDSS output from two vector models forced by WSN data from a field site in Belgium. We believe that this system can be a cost-effective solution for intelligent IPM in remote areas where there is no reliable terrestrial connection. Full article
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Open AccessArticle
Security Pattern for Cloud SaaS: From System and Data Security to Privacy Case Study in AWS and Azure
Computers 2019, 8(2), 34; https://doi.org/10.3390/computers8020034 - 03 May 2019
Cited by 2
Abstract
The Cloud is fast becoming a popular platform for SaaS, a popular software delivery model. This is because the Cloud has many advantages over the traditional private infrastructure, such as increased flexibility, no maintenance, less management burden, easy access and easy to share [...] Read more.
The Cloud is fast becoming a popular platform for SaaS, a popular software delivery model. This is because the Cloud has many advantages over the traditional private infrastructure, such as increased flexibility, no maintenance, less management burden, easy access and easy to share information. However, there are many concerns around issues like system security, communication security, data security, privacy, latency and availability. In addition, when designing and developing Cloud SaaS application, these security issues need to be addressed in order to ensure regulatory compliance, security and trusted environment for Cloud SaaS users. In this paper, we explore the security patterns for Cloud SaaS. We work on the patterns covering different security aspects from system and data security to privacy. Our goal is to produce the security best practices and security knowledge documentation that SaaS developer can use as a guideline for developing Cloud SaaS applications from the ground up. In addition to that, we also provide a case study of security patterns and solutions in AWS and Azure. Full article
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Open AccessArticle
Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks
Computers 2019, 8(1), 27; https://doi.org/10.3390/computers8010027 - 19 Mar 2019
Cited by 3
Abstract
This paper presents a new key management protocol for group-based communications in non-hierarchical wireless sensor networks (WSNs), applied on a recently proposed IP-based multicast protocol. Confidentiality, integrity, and authentication are established, using solely symmetric-key-based operations. The protocol features a cloud-based network multicast manager [...] Read more.
This paper presents a new key management protocol for group-based communications in non-hierarchical wireless sensor networks (WSNs), applied on a recently proposed IP-based multicast protocol. Confidentiality, integrity, and authentication are established, using solely symmetric-key-based operations. The protocol features a cloud-based network multicast manager (NMM), which can create, control, and authenticate groups in the WSN, but is not able to derive the actual constructed group key. Three main phases are distinguished in the protocol. First, in the registration phase, the motes register to the group by sending a request to the NMM. Second, the members of the group calculate the shared group key in the key construction phase. For this phase, two different methods are tested. In the unicast approach, the key material is sent to each member individually using unicast messages, and in the multicast approach, a combination of Lagrange interpolation and a multicast packet are used. Finally, in the multicast communication phase, these keys are used to send confidential and authenticated messages. To investigate the impact of the proposed mechanisms on the WSN, the protocol was implemented in ContikiOS and simulated using COOJA, considering different group sizes and multi-hop communication. These simulations show that the multicast approach compared to the unicast approach results in significant smaller delays, is a bit more energy efficient, and requires more or less the same amount of memory for the code. Full article
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