Cloud computing and big data: Technologies and applications

Cloud computing has emerged as a new paradigm of computing, in which scalable and virtualized resources are dynamically provided (anytime, everywhere, and in a transparent way) as services over the Internet.1-3 In cloud computing environments, users can use anytime a variety of devices like PCs, laptops, smart phones, and PDAs to access programs, storage, and application-development platforms using services offered by cloud computing providers. In fact, users can benefit from the high availability, easy scalability, and low costs in using cloud computing resources (ie, software, infrastructure, and platform).4 For instance, cloud computing offers a collection of IT services referred to as Software-as-a-Service to allow users remotely performing their applications. Infrastructure-as-a-Service refers to computing resources as services, whereas Platform-as-a-Service offer some tools and resources for applications' development, including operating systems. Data-Storage-as-a-Service has also emerged in the past few years to provide users with storage capabilities. Generally, cloud computing infrastructures have emerged to allow managing transparently all hardware/software issues such as job scheduling and resources allocation by hiding all implementations, so users can focus on how to access and use remote resources and services instead of focusing on computing and data storage/access issues. Furthermore, various efforts have been recently dedicated to improve the performances of all services offered by cloud computing environments. However, there are still many issues that need to be tackled, mainly, scalability, availability, security, and privacy.4 Despite these issues, cloud computing will remain an environment that continues to play a considerable role in many existing and emerging application domains.5,6 In parallel to this progress, big data technologies have been developed and deployed so rapidly and are relying heavily on cloud computing infrastructures for both storage and data processing. In many studies, these technologies are considered among the most remarkable technologies for developing context-driven applications and services in many domains such as transportation, health, and energy.7-9 In other words, big data technologies have been some of the current and future research frontiers. They revolutionize many fields, including business, scientific research, and public administration. However, the high-volume, high-velocity, and/or high-variety of available information require new forms of processing to enable enhanced decision making, insight discovery, and processes' optimization.10,11 Even though these technologies have been developing very fast over the past few years, we are also facing, in addition to inconsistency and incompleteness, a lot of challenges when handling big data; difficulties mainly lie in real-time data gathering, storage, mining, predictive analytics, and visualization.12 Furthermore, IoT technologies have shown great potential for collecting large amounts of data streams from sensor readings. In fact, a myriad of sensors can be deployed for gathering contextual data that could be integrated with other data such as location, weather data, and social media data. The processing of these heterogeneous data allows the development of context-aware applications and services, which can, for example, provide real-time traffic routing throughout the city, detect, and immediately act on environmental pollution peaks or automatically optimize the logistics chain by allowing instantaneous reactions (eg, via actuators). These data streams have to be first pre-processed locally before sending it to the cloud infrastructures using IoT platforms (eg, ThingSpeak) and via wireless technologies (eg, Wifi and LTE) for storage and processing using machine learning algorithms (eg, deep learning). These heavy processing and high communication factors are, however, serious challenges for the development of large-scale IoT applications over cloud computing environments.7,9 The aim of this special issue is to present recent contributions and results in the fields of cloud computing, IoT and big data applications, systems architecture and services, virtualization, security and privacy, high-performance computing, and applications, with an emphasis on how to build cloud computing platforms with real impacts.