4,554 research outputs found
Next Generation Cloud Computing: New Trends and Research Directions
The landscape of cloud computing has significantly changed over the last
decade. Not only have more providers and service offerings crowded the space,
but also cloud infrastructure that was traditionally limited to single provider
data centers is now evolving. In this paper, we firstly discuss the changing
cloud infrastructure and consider the use of infrastructure from multiple
providers and the benefit of decentralising computing away from data centers.
These trends have resulted in the need for a variety of new computing
architectures that will be offered by future cloud infrastructure. These
architectures are anticipated to impact areas, such as connecting people and
devices, data-intensive computing, the service space and self-learning systems.
Finally, we lay out a roadmap of challenges that will need to be addressed for
realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201
VIoLET: A Large-scale Virtual Environment for Internet of Things
IoT deployments have been growing manifold, encompassing sensors, networks,
edge, fog and cloud resources. Despite the intense interest from researchers
and practitioners, most do not have access to large-scale IoT testbeds for
validation. Simulation environments that allow analytical modeling are a poor
substitute for evaluating software platforms or application workloads in
realistic computing environments. Here, we propose VIoLET, a virtual
environment for defining and launching large-scale IoT deployments within cloud
VMs. It offers a declarative model to specify container-based compute resources
that match the performance of the native edge, fog and cloud devices using
Docker. These can be inter-connected by complex topologies on which
private/public networks, and bandwidth and latency rules are enforced. Users
can configure synthetic sensors for data generation on these devices as well.
We validate VIoLET for deployments with > 400 devices and > 1500 device-cores,
and show that the virtual IoT environment closely matches the expected compute
and network performance at modest costs. This fills an important gap between
IoT simulators and real deployments.Comment: To appear in the Proceedings of the 24TH International European
Conference On Parallel and Distributed Computing (EURO-PAR), August 27-31,
2018, Turin, Italy, europar2018.org. Selected as a Distinguished Paper for
presentation at the Plenary Session of the conferenc
Low-Code as Enabler of Digital Transformation in Manufacturing Industry
[EN] Currently, enterprises have to make quick and resilient responses to changing market
requirements. In light of this, low-code development platforms provide the technology mechanisms
to facilitate and automate the development of software applications to support current enterprise
needs and promote digital transformation. Based on a theory-building research methodology through
the literature and other information sources review, the main contribution of this paper is the current
characterisation of the emerging low-code domain following the foundations of the computer-aided
software engineering field. A context analysis, focused on the current status of research related to
the low-code development platforms, is performed. Moreover, benchmarking among the existing
low-code development platforms addressed to manufacturing industry is analysed to identify the
current lacking features. As an illustrative example of the emerging low-code paradigm and respond
to the identified uncovered features, the virtual factory open operating system (vf-OS) platform
is described as an open multi-sided low-code framework able to manage the overall network of a
collaborative manufacturing and logistics environment that enables humans, applications, and Internet
of Things (IoT) devices to seamlessly communicate and interoperate in the interconnected environment,
promoting resilient digital transformation.This work was supported in part by the European Commission under the Grant Agreements No. 723710 and 825631.Sanchis, R.; Garcia-Perales, O.; Fraile Gil, F.; Poler, R. (2020). Low-Code as Enabler of Digital Transformation in Manufacturing Industry. Applied Sciences. 10(1):1-17. https://doi.org/10.3390/app10010012S117101Sanchis, R., & Poler, R. (2019). Enterprise Resilience Assessment—A Quantitative Approach. Sustainability, 11(16), 4327. doi:10.3390/su11164327Lowcomote: Training the Next Generation of Experts in Scalable Low-Code Engineering Platformshttps://www.se.jku.at/lowcomote-training-the-next-generation-of-experts-in-scalable-low-code-engineering-platforms/Waszkowski, R. (2019). Low-code platform for automating business processes in manufacturing. IFAC-PapersOnLine, 52(10), 376-381. doi:10.1016/j.ifacol.2019.10.060Lundell, B., & Lings, B. (2004). Changing perceptions of CASE technology. Journal of Systems and Software, 72(2), 271-280. doi:10.1016/s0164-1212(03)00087-6Fuggetta, A. (1993). A classification of CASE technology. Computer, 26(12), 25-38. doi:10.1109/2.247645Troy, D., & McQueen, R. (1997). An approach for developing domain specific CASE tools and its application to manufacturing process control. Journal of Systems and Software, 38(2), 165-192. doi:10.1016/s0164-1212(96)00120-3Huff, C. C. (1992). Elements of a realistic CASE tool adoption budget. Communications of the ACM, 35(4), 45-54. doi:10.1145/129852.129856Orlikowski, W. J. (1993). CASE Tools as Organizational Change: Investigating Incremental and Radical Changes in Systems Development. MIS Quarterly, 17(3), 309. doi:10.2307/249774Iivari, J. (1996). Why are CASE tools not used? Communications of the ACM, 39(10), 94-103. doi:10.1145/236156.236183Zolotas, C., Chatzidimitriou, K. C., & Symeonidis, A. L. (2018). RESTsec: a low-code platform for generating secure by design enterprise services. Enterprise Information Systems, 12(8-9), 1007-1033. doi:10.1080/17517575.2018.1462403GAVRILĂ, V., BĂJENARU, L., & DOBRE, C. (2019). Modern Single Page Application Architecture: A Case Study. Studies in Informatics and Control, 28(2). doi:10.24846/v28i2y201911Wu, Y., Wang, S., Bezemer, C.-P., & Inoue, K. (2018). How do developers utilize source code from stack overflow? Empirical Software Engineering, 24(2), 637-673. doi:10.1007/s10664-018-9634-5Hamming, R. W. (1950). Error Detecting and Error Correcting Codes. Bell System Technical Journal, 29(2), 147-160. doi:10.1002/j.1538-7305.1950.tb00463.xForresterhttps://go.forrester.com/The Maturity of Visual Programming. Режим дoступуhttp://www. craft. ai/blog/the-maturity-of-visualprogrammingVirtual Factory Operating Systemwww.vf-OS.euvf-OS D1.1: Vision Consensushttps://www.vf-os.eu/resultsvf-OS Wikihttps://cigipsrv1.cigip.upv.es:4430/mediawiki/index.php/Wiki_Homevf-OS D2.1: Global Architecture Definitionhttps://www.vf-os.eu/resultsSiemens MindSpherehttps://new.siemens.com/vn/en/products/software/mindsphere.htmlPTC ThingWorx Platformhttps://www.ptc.com/en/resources/iiot/product-brief/thingworx-platformGE Predixhttps://www.ge.com/digital/iiot-platformIBM Cloudhttps://www.ibm.com/cloudMicrosoft Azure IOT Suitehttps://azure.microsoft.com/es-es/blog/microsoft-azure-iot-suite-connecting-your-things-to-the-cloud/Software AG ADAMOShttps://www.softwareag.com/corporate/company/adamos/default.htm
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
Cognitive Hyperconnected Digital Transformation
Cognitive Hyperconnected Digital Transformation provides an overview of the current Internet of Things (IoT) landscape, ranging from research, innovation and development priorities to enabling technologies in a global context. It is intended as a standalone book in a series that covers the Internet of Things activities of the IERC-Internet of Things European Research Cluster, including both research and technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT-EPI) and the IoT European Large-Scale Pilots Programme, presenting global views and state-of-the-art results regarding the challenges facing IoT research, innovation, development and deployment in the next years. Hyperconnected environments integrating industrial/business/consumer IoT technologies and applications require new IoT open systems architectures integrated with network architecture (a knowledge-centric network for IoT), IoT system design and open, horizontal and interoperable platforms managing things that are digital, automated and connected and that function in real-time with remote access and control based on Internet-enabled tools. The IoT is bridging the physical world with the virtual world by combining augmented reality (AR), virtual reality (VR), machine learning and artificial intelligence (AI) to support the physical-digital integrations in the Internet of mobile things based on sensors/actuators, communication, analytics technologies, cyber-physical systems, software, cognitive systems and IoT platforms with multiple functionalities. These IoT systems have the potential to understand, learn, predict, adapt and operate autonomously. They can change future behaviour, while the combination of extensive parallel processing power, advanced algorithms and data sets feed the cognitive algorithms that allow the IoT systems to develop new services and propose new solutions. IoT technologies are moving into the industrial space and enhancing traditional industrial platforms with solutions that break free of device-, operating system- and protocol-dependency. Secure edge computing solutions replace local networks, web services replace software, and devices with networked programmable logic controllers (NPLCs) based on Internet protocols replace devices that use proprietary protocols. Information captured by edge devices on the factory floor is secure and accessible from any location in real time, opening the communication gateway both vertically (connecting machines across the factory and enabling the instant availability of data to stakeholders within operational silos) and horizontally (with one framework for the entire supply chain, across departments, business units, global factory locations and other markets). End-to-end security and privacy solutions in IoT space require agile, context-aware and scalable components with mechanisms that are both fluid and adaptive. The convergence of IT (information technology) and OT (operational technology) makes security and privacy by default a new important element where security is addressed at the architecture level, across applications and domains, using multi-layered distributed security measures. Blockchain is transforming industry operating models by adding trust to untrusted environments, providing distributed security mechanisms and transparent access to the information in the chain. Digital technology platforms are evolving, with IoT platforms integrating complex information systems, customer experience, analytics and intelligence to enable new capabilities and business models for digital business
Enabling stream processing for people-centric IoT based on the fog computing paradigm
The world of machine-to-machine (M2M) communication is gradually moving from vertical single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organizations and people - A world of Internet of Things (IoT). The dominant approach for delivering IoT applications relies on the development of cloud-based IoT platforms that collect all the data generated by the sensing elements and centrally process the information to create real business value. In this paper, we present a system that follows the Fog Computing paradigm where the sensor resources, as well as the intermediate layers between embedded devices and cloud computing datacenters, participate by providing computational, storage, and control. We discuss the design aspects of our system and present a pilot deployment for the evaluating the performance in a real-world environment. Our findings indicate that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture
Benchmarking 5G MEC and Cloud infrastructures for planning IoT messaging of CCAM data
Vehicles embed lots of sensors supporting driving and safety. Combined with
connectivity, they bring new possibilities for Connected, Cooperative and
Automated Mobility (CCAM) services that exploit local and global data for a
wide understanding beyond the myopic view of local sensors. Internet of Things
(IoT) messaging solutions are ideal for vehicular data as they ship core
features like the separation of geographic areas, the fusion of different
producers on data/sensor types, and concurrent subscription support.
Multi-access Edge Computing (MEC) and Cloud infrastructures are key to hosting
a virtualized and distributed IoT platform. Currently, the are no benchmarks
for assessing the appropriate size of an IoT platform for multiple vehicular
data types such as text, image, binary point clouds and video-formatted
samples. This paper formulates and executes the tests to get a benchmarking of
the performance of a MEC and Cloud platform according to actors' concurrency,
data volumes and business levels parameters.Comment: 6 pages, 5 figures, 6 tables, IEEE International Conference on
Intelligent Transportation System
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