Italy

Supporting Internet of Things (IoT) workflow enactment/execution on a combination of computational resources at the network edge and at a datacentre remains a challenge. Increasing volumes of data being generated through smart phones, IoT (Internet of Things) devices (which can vary significantly in scope and capability), need to be processed in a timely manner. Current practice involves using edge nodes (e.g. sensors or other low-capacity devices) as a means to acquire/collect data (i.e. as an &quot;observation&quot; mechanism). Subsequently, this data is transmitted to a datacentre/cloud for analysis/insight. Increasingly, the limitation with the use of a large-scale, centralised datacentre is being realised (such as speed of response for latency-sensitive applications), with the emergence of a number of paradigms to address this concern -- such as fog computing, edge computing, Cloud-of-Things, etc. All of these propose the use of dedicated servers (with varying capacity and capability) within micro/nano datacentres at the network edge, to overcome latency constraints associated with moving data to a central facility, and (lack of use of) increasing computational capability within edge devices. These paradigms also closely align with work in content distribution networks (e.g. from Akamai CDNs), which attempt to place data servers within one (or a small number of) hop of end users (currently 85% of users are supported in this way, with &gt;175K Akamai servers)

A key objective of this keynote talk is to understand how such emerging paradigms can be used to enable cloud systems (supported through large scale computational facilities) to be &quot;stretched&quot; to the network edge, to enable data-driven IoT workflows to be enacted efficiently over such combined infrastructure. We propose the combined use of (varying) capability at the network edge (referred to as an &quot;Edge Datacentre&quot; (EDC)) with capability within a Cloud Datacentre (CDC). Collectively, IoT devices and edge resources, like gateways (Raspberry Pi 3), software-defined network systems (Huawei CloudEngine 6800) and smart phones equipped with sensors, constitute a new set of computing resources -- and as potential components of an EDC. The keynote talk will have the following outline:

1.Overview of the research challenges involved with composing and orchestrating complex IoT workflows in cloud-edge continuum infrastructure

2.Discuss two case studies in healthcare and smart cities domain to understand how data-driven workflows can be applied to create/compose next-generation IoT applications.

3.Discuss our experience with running United Kingdom’s largest IoT infrastructure, namely, the Urban Observatory (http://www.urbanobservatory.ac.uk/)



CLOSER 2020

New Horizons in IoT Workflows Provisioning in Edge and Cloud Datacentres for Fast Data Analytics: The Osmotic Computing Approach

The internet of things (IoT) is a platform that allows a network of devices (sensors, smart meters, etc.) to communicate, analyse data and process information collaboratively in the service of individuals or organisations. The IoT network can generate large amounts of data in a variety of formats and using different protocols which can be stored and processed in the cloud. The conference looks to address the issues surrounding IoT devices, their interconnectedness and services they may offer, including efficient, effective and secure analysis of the data IoT produces using machine learning and other advanced techniques, models and tools, and issues of security, privacy and trust that will emerge as IoT technologies mature and become part of our everyday lives.

Big Data (BD) has core values of volume, velocity, variety and veracity. After collecting much data from IoT, BD can be jointly used with machine learning, AI, statistical and other advanced techniques, models and methods, which can create values for people and organizations adopting it, since forecasting, deep analysis and analytics can help identify weaknesses and make improvements based on different analysis.

Maintaining a high level of security and privacy for data in IoT are crucial and we welcome recommendations, solutions, demonstrations and best practices for all forms of security and privacy for IoT and BD.



IoTBDS 2022

The conference looks to address the issues surrounding IoT devices, their interconnectedness and services they may offer, including efficient, effective and secure analysis of the data IoT produces using machine learning and other advanced techniques, models and tools, and issues of security, privacy and trust that will emerge as IoT technologies mature and become part of our everyday lives.


Supporting Internet of Things (IoT) workflow enactment/execution on a combination of computational resources at the network edge and at a datacentre remains a challenge. Increasing volumes of data being generated through smart phones, IoT (Internet of Things) devices (which can vary significantly in scope and capability), need to be processed in a timely manner. Current practice involves using edge nodes (e.g. sensors or other low-capacity devices) as a means to acquire/collect data (i.e. as an "observation" mechanism). Subsequently, this data is transmitted to a datacentre/cloud for analysis/insight. Increasingly, the limitation with the use of a large-scale, centralised datacentre is being realised (such as speed of response for latency-sensitive applications), with the emergence of a number of paradigms to address this concern -- such as fog computing, edge computing, Cloud-of-Things, etc. All of these propose the use of dedicated servers (with varying capacity and capability) within micro/nano datacentres at the network edge, to overcome latency constraints associated with moving data to a central facility, and (lack of use of) increasing computational capability within edge devices. These paradigms also closely align with work in content distribution networks (e.g. from Akamai CDNs), which attempt to place data servers within one (or a small number of) hop of end users (currently 85% of users are supported in this way, with >175K Akamai servers)

A key objective of this keynote talk is to understand how such emerging paradigms can be used to enable cloud systems (supported through large scale computational facilities) to be "stretched" to the network edge, to enable data-driven IoT workflows to be enacted efficiently over such combined infrastructure. We propose the combined use of (varying) capability at the network edge (referred to as an "Edge Datacentre" (EDC)) with capability within a Cloud Datacentre (CDC). Collectively, IoT devices and edge resources, like gateways (Raspberry Pi 3), software-defined network systems (Huawei CloudEngine 6800) and smart phones equipped with sensors, constitute a new set of computing resources -- and as potential components of an EDC. The keynote talk will have the following outline:

1.Overview of the research challenges involved with composing and orchestrating complex IoT workflows in cloud-edge continuum infrastructure

2.Discuss two case studies in healthcare and smart cities domain to understand how data-driven workflows can be applied to create/compose next-generation IoT applications.

3.Discuss our experience with running United Kingdom’s largest IoT infrastructure, namely, the Urban Observatory (http://www.urbanobservatory.ac.uk/)



keynote

The 10th International Conference on Cloud Computing and Services Science, CLOSER 2020, focuses on the highly important area of Cloud Computing, inspired by some latest advances that concern the infrastructure, operations, and available services through the global network. Further, the conference considers as essential the link to Services Science, acknowledging the service-orientation in most current IT-driven collaborations. 


Edge AI and Human Augmentation are two major technology trends, driven by recent advancements in edge computing, IoT, and AI accelerators. As humans, things and AI continue to grow closer together, systems engineers and researchers are faced with new and unique challenges. In this talk, we analyse the role of Edge computing and AI in the cyber-human evolution and identify challenges that Edge computing systems will consequently be faced with. We take a closer look at how a cyber-physical fabric will be complemented by AI operationalisation to enable seamless end-to-end Edge intelligence systems.

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