Cloud computing, <scp>IoT</scp>, and big data: Technologies and applications

This special issue presents recent contributions and results in the fields of cloud computing, IoT, and big data technologies. It includes a set of selected articles that have been presented at the 2017 International Conference on Cloud Computing and Applications (CloudTech'17).1 CloudTech'17 was held in Rabat on October 24-26, 2017, and it is one of the most successful events in the fields of cloud computing and big data technologies. CloudTech addresses topics related to cloud computing and big data technologies, architectures, and applications, including data privacy and security. The set of papers accepted for publication in this special issue are briefly described in the rest of this document and are structured in four main research areas: (i) platforms and architectures, (ii) cloud computing and big data analysis, (iii) virtualization techniques, and (iv) data security and privacy. Cloud computing infrastructures are playing an important role by providing organizations with the ability to analyze and store data economically and efficiently.2 In fact, they are used in many applications domains because of their adapted pay-per-use model that allows offering scalable and cost effectiveness solutions.3-5 However, while cloud technologies are providing high-performance functionalities and services for both individuals and enterprises with best availability and price, many applications require a strong need for low latency data transmission, elasticity, resource adaptability, and low processing.6, 7 In this direction, Yang et al in their paper “RSCVC: Row-based semantic cache with incremental versioning consistency”8 tackle the issue of limited resources, mainly the communication bandwidth, which becomes the bottlenecks for data transmission. They propose a row-based semantic cache method with incremental versioning consistency, named RSCVC. In fact, a semantic cache algorithm was introduced to improve the query response, while the time version-based consistency method was proposed to support the semantic cache by keeping the cache system stable in high concurrency situations. Experiments have been conducted and reported results show the effectiveness of RSCVC against other state-of-the-art methods. As a second contribution in this research area, Haugerud et al, in their paper “On automated cloud bursting and hybrid cloud setups using Apache Mesos,”9 stated that hybrid cloud-based solutions are required to enable users to select the best suitable cloud infrastructures according to their locations, availability, and performance. They proposed an open source solution, which allows building a computer cluster on top of private and public computer resources. They mainly introduce a cloud bursting model that allows running applications on top of a private cloud by dynamically bursting into a public cloud, especially when private cloud resources are unsatisfactory. This model could handle peak demands and meet expanding capacity requirements by only paying for extra and needed compute resources. Experiments have been conducted to show the effectiveness of the proposed cloud bursting model and its related techniques. A third contribution in this research area is provided by Calzarossa et al in the paper “Evaluation of cloud autoscaling strategies under different incoming workload patterns.”10 Authors stated that flexible and dynamic resource provisioning techniques are required in order to cope with the time varying and unpredictable workloads. In fact, autoscaling strategies are required and could be used to adapt resources accordingly. They have evaluated autoscaling strategies according to different incoming workloads as well as their related patterns. Mainly, simulations have been conducted in order to evaluate and assess the benefits of autoscaling techniques on the performance of the cloud according to different workload conditions. The high-volume, high-velocity, and/or high-variety of data generated from different sources, such as social networks, web pages, sensors, mobile, and IoT devices, has increased the demand for communication and computing power. Therefore, the development of applications and services in different fields (eg, health, agriculture, transportation) requires information processing techniques and tools. In fact, there are many applications, which require real-time inference, prediction, and recommendations to users, such as in transportation, health, and agriculture. These applications require, however, a strong need for low latency data transmission and processing.11 As a first contribution in this area, Mahmoudi et al, in their paper “Multimedia processing using deep learning technologies, HPC cloud resources and big data volumes,”12 introduce a real time cloud-based platform for computer vision applications, which includes a bunch of processing algorithms (machine and deep learning algorithms) that can exploit HPC cloud resources and execute applications in real-time. It also includes tools that allow managing large-scale database using big data technologies. Especially it offers to users an access to different applications and their related libraries and hardware drivers, so there is no need to download, install, or configure software/hardware. Experiments have been conducted and results show the potential of the proposed platform in improving the quality and performance of computer vision applications. As a second contribution in this research area, Debauche et al, in their paper “Cloud architecture for plant phenotyping research,”13 propose to use cloud computing infrastructures for high throughput data collection and real-time processing of plant phenotyping applications. Authors mainly associated a lambda cloud-based architecture with stream processing by using Apache Hadoop and Spark for gathering, storage, and near real-time processing of high streaming data from phenotyping and environmental parameters, such as temperature, relative humidity, and cation-exchange capacity. The platform has been validated and tested for its scalability and adaptability using different uses cases, such as digital phenotyping, cattle, and animal behavior. The third contribution in this area is described in the paper “A platform architecture for occupancy detection using stream processing and machine learning approaches,” which is presented by Elkhoukhi et al in Reference 14. Authors stated that in dynamic and frequent context's changes, it is difficult to develop applications and services without having real-time event/data acquisition and processing platforms. They stated that unlike static approaches, which use the principle of “store first and process later” (eg, Hadoop/Map reduce), dynamic approaches have been proposed for real-time data processing (eg, Spark, S4). These approaches include both remote and in-field processing techniques. Remote real-time processing approaches aim to reduce the processing time by using the cloud (eg, on big data cluster) for analyzing data remotely, while in-field processing techniques use the principle of edge computing, in which a part of processing could be executed on edge nodes. In this perspective, authors propose to combine IoT and big data technologies (SAMOA) by investigating the integration of machine learning algorithms for occupancy detection in buildings. The occupancy information has been used for controlling building's ventilation systems. Reported results show the effectiveness of the proposed solution for real-time data gathering, streams processing, and control. Hypervisor-based virtualization techniques,2, 15 such as Kernel Virtual Machine, Xen Server, and VMware, have been developed in the past decades for efficient resources management. In fact, by virtualizing system resources, such as CPUs and memory, it is possible to run multiple OS in a concurrent manner. However, these techniques introduce performance overheads, which could limit their efficiency in critical and challenging fields, such as in big data analytics and scientific computing. Container-based virtualization has been, therefore, proposed to handle this issue by reducing the overhead of hypervisor-based virtualization. In this direction, Casalicchio and Iannucci in their paper “The state-of-the-art in container technologies: application, orchestration and security”16 conducted an extensive survey of existing technologies by highlighting the challenges related to the adoption of container-based virtualization in high-performance computing, big data analytics, and Internet-of-Things applications, mainly in terms of performance, orchestration, and cyber-security. As a second contribution in this research field, Shirinbab et al, in their paper “Performance evaluation of containers and virtual machines when running Cassandra workload concurrently,”17 stated that the choice of the most suitable virtualization technology for big data platforms is a difficult task. In this direction, they have conducted a performance comparison of different virtualization techniques using an open-source NoSQL distributed data, Apache Cassandra, which is widely used in big data applications. Reported results show that container-based virtualization has lower overhead and system resource consumption compared to the VMware virtualized version. In addition to the abovementioned issues, mainly those related to resources management and virtualization, privacy, and security are still among the most important issues that have to be addressed especially for emerging IoT and big data applications.7, 18 As a contribution in this research field, Patonico et al, in their paper “Elliptic curve based proxy re-signcryption scheme for secure data storage on the cloud,”19 stated that data storage represents one of the most important services of cloud computing. They introduced the state of art techniques for establishing user authentication, which is used to ensure data ownership. Authors put more emphasize on proxy re-signcryption scheme for cloud storage and show how a certificate-based proxy re-signcryption scheme could be applied for data storage in cloud computing infrastructures. Experiments have been conducted to investigate and assess on the performance of the proposed scheme by demonstrating its usage in the context of data storage, mainly in cloud computing environments. This special issue includes a set of papers that provide new results and recent research works related to cloud computing, IoT, and big data technologies. Especially manuscripts of this issue focus on cloud platforms architectures, big data technologies for data processing, virtualization techniques, and data security. We hope that the readers will benefit from the research works presented in these manuscripts and will contribute to remaining challenges, especially those related to real-time processing on cloud infrastructures including data security and privacy. The guest editors would like to thank the Editor in Chief, Prof. W. Fox, for the opportunity of having this special issue and for giving the authors the opportunity to publish their works in the CCPE journal. We also sincerely thank all authors for their tangible contributions to this special issue. Special thanks to all reviewers for their time and the hard work they have made to successfully complete the reviewing process. We also sincerely thank the CCPE Editorial Board and the journal's staff for their support.