China
Africa
Explore chapters and articles related to this topic
Introduction
Published in Sunilkumar Manvi, Gopal K. Shyam, Cloud Computing, 2021
Sunilkumar Manvi, Gopal K. Shyam
Cloud computing is bound to be compared with service oriented architectures (SOA), Grid computing, Utility computing and Cluster computing. Cloud computing and SOA are persued independently. Platform and storage services of Cloud computing offers value addition to SOA's efforts. With technologies like Grid computing, computing resources can be provisioned as a utility. Whereas, Cloud computing goes a step further with on-demand resource provisioning. It also removes the necessity of over-provisioning to accomodate the demands of several customers. Utility computing is paying for resource usage, similar to the way we pay for a public utility (such as electricity, gas, and so on).
Introduction
Published in John W. Rittinghouse, James F. Ransome, Cloud Computing, 2017
John W. Rittinghouse, James F. Ransome
Utility computing can be defined as the provision of computational and storage resources as a metered service, similar to those provided by a traditional public utility company. This, of course, is not a new idea. This form of computing is growing in popularity, however, as companies have begun to extend the model to a cloud computing paradigm providing virtual servers that IT departments and users can access on demand. Early enterprise adopters used utility computing mainly for non-mission-critical needs, but that is quickly changing as trust and reliability issues are resolved.
Industrial Internet of Things (IIoT)
Published in Chanchal Dey, Sunit Kumar Sen, Industrial Automation Technologies, 2020
Utility computing is based on ‘pay per use’ concept. Computational resources of the cloud are made available on demand and the user is charged for it. Utility computing embraces cloud computing, grid computing and the associated IT services.
Virtual Machine Migration-Based Intrusion Detection System in Cloud Environment Using Deep Recurrent Neural Network
Published in Cybernetics and Systems, 2022
B. V. Srinivas, Indrajit Mandal, Seetharam Keshavarao
CCT has been designed and shown a significant role in recent decades in the production and human life (Sabahi 2011; Zhang et al. 2020). CCT is designed by conventional computer technologies, like parallel computing (Chen et al. 2009), grid computing (Lim et al. 2007), network storage, virtualization with the network technologies and utility computing (Soldatos, Serrano, and Hauswirth 2012; Zhang et al. 2020). The revolution of CC offered enhanced flexibility, functionality to the developers, cost-effectiveness, individuals and the business to the world, and availability AlKadi et al. (2019). CC is the pool of large resources when compared with the conventional network. However, the people obtained the features from the number of resources of the network based on their requirements (Zhang et al. 2020). The CC system is highly vulnerable to security threats dsue to the growing development. The dynamic nature of the cloud framework makes examining the VM's activity more complex (Abdalla & Ahmed 2020). Hence, neither network based nor host-based intrusion detection mechanism is required to meet in necessity in the virtual environment (Farshchi et al. 2018; Agrawal, Wiktorski, and Rong 2016; Pandeeswari and Kumar 2016).
An overview of current technologies and emerging trends in factory automation
Published in International Journal of Production Research, 2019
Mariagrazia Dotoli, Alexander Fay, Marek Miśkowicz, Carla Seatzu
Future factories are expected to be complex systems of systems aimed at providing a new generation of applications and services, as well as at supporting effective interactions between systems and collaborative cross-layer management (Colombo 2014). The major technical stimulus for modern manufacturing will be a dissemination of emerging Internet technologies in industry. The underling future factory vision is based on the conversion of factories into a smart environment by adopting the Internet of Things and Services (IoT&S) able to create networks incorporating the entire manufacturing process. A bridge between enterprise networks and IoT&S is provided by platforms like the 6LoWPAN standard released to ensure applicability of IPv6 to low power, low rate wireless radio communication based on the IEEE 802.15.4 standard (see Section 5). Finally, 6LoWPAN is a powerful platform for integration of formerly separated proprietary networks into a global IP-based infrastructure and for the implementation of the concepts of IoT&S (Moritz et al. 2013; Samaras, Hassapis, and Gialelis 2013). To enable effective integration among various heterogeneous systems and devices in modern factories, the SOAs are expected to be used more extensively. Service-based applications in industrial automation support automatic composition, orchestration and configuration of distributed automation functions and systems. Emerging trends based on the SOA facilitate device virtualisation using Web services by embedding Web service protocols into a chip and providing their integration with control devices. Virtualisation addresses factory needs for scalability and more efficient use of resources. The widespread adoption of virtualisation, service-oriented architecture and utility computing drives the use of cloud computing in factory automation. The benefits of the cloud and cloud-based services give potential to rise a new generation of service-based industrial systems whose functionalities lie in on-device and in-cloud (Colombo 2014). The interconnection of subsystems through public communication networks and the Internet, as well as the introduction of wireless communication technologies, significantly contribute to the increase of the exposure of industrial automation systems to security threats. Unfortunately, due to the specifics of industrial networks, the methods and tools developed to protect general-purpose computer networks cannot be effectively adopted to cope with cyber-attacks on production systems. Moreover, the progress in the efficiency of solutions applied to the management of security of factory automation systems is still lower than the increase of sophistication of cyberattacks (Cheminod, Durante, and Valenzano 2013). As a result, the effective management of security for factory automation is one of the challenges for future manufacturing. The scientific community is expected to propose and develop new advanced techniques to support security experts and managers in preventing production systems from threats targeted to industrial automation systems (Cheminod, Durante, and Valenzano 2013).