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

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

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

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

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