5 research outputs found
A human centric approach to the Internet of things
This research focuses on human interaction with the IoT, not only from the perspective of the user, but also considering the requirements that smart objects should meet to support human activities.
It analyses how the IoT was originally conceived from a technology and data driven approach, and why there is a need to provide an IoT framework that considers humans’ tasks and goals. As such, the nature of the actions and interactions found in a human-based IoT are discussed in the context of social-like collaborations, where actors are in pursue of a common goal.
This thesis reframes Human-IoT interaction as a social, collaborative system, described in terms of its capacity to support the activities of the involved social actors in pursuit of a common goal. A structure is proposed to describe the nature of these interactions, and a methodology to model user behaviour based on the tasks and goals supporting a theme is proposed. The methodology is used to analyse the requirements of a domestic IoT system, leading to the implementation of a demonstrator system, and a study to validate the method.
This research posits that user experience should inform IoT system design to prevent misunderstanding of its purpose
DCDB Wintermute: Enabling Online and Holistic Operational Data Analytics on HPC Systems
As we approach the exascale era, the size and complexity of HPC systems
continues to increase, raising concerns about their manageability and
sustainability. For this reason, more and more HPC centers are experimenting
with fine-grained monitoring coupled with Operational Data Analytics (ODA) to
optimize efficiency and effectiveness of system operations. However, while
monitoring is a common reality in HPC, there is no well-stated and
comprehensive list of requirements, nor matching frameworks, to support
holistic and online ODA. This leads to insular ad-hoc solutions, each
addressing only specific aspects of the problem.
In this paper we propose Wintermute, a novel generic framework to enable
online ODA on large-scale HPC installations. Its design is based on the results
of a literature survey of common operational requirements. We implement
Wintermute on top of the holistic DCDB monitoring system, offering a large
variety of configuration options to accommodate the varying requirements of ODA
applications. Moreover, Wintermute is based on a set of logical abstractions to
ease the configuration of models at a large scale and maximize code re-use. We
highlight Wintermute's flexibility through a series of practical case studies,
each targeting a different aspect of the management of HPC systems, and then
demonstrate the small resource footprint of our implementation.Comment: Accepted for publication at the 29th ACM International Symposium on
High-Performance Parallel and Distributed Computing (HPDC 2020
Cognified distributed computing
Cognification - the act of transforming ordinary objects or processes into their intelligent counterparts through Data Science and Artificial Intelligence - is a disruptive technology that has been revolutionizing disparate fields ranging from corporate law to medical diagnosis. Easy access to massive data sets, data analytics tools and High-Performance Computing (HPC) have been fueling this revolution. In many ways, cognification is similar to the electrification revolution that took place more than a century ago when electricity became a ubiquitous commodity that could be accessed with ease from anywhere in order to transform mechanical processes into their electrical counterparts. In this paper, we consider two particular forms of distributed computing - Data Centers and HPC systems - and argue that they are ripe for cognification of their entire ecosystem, ranging from top-level applications down to low-level resource and power management services. We present our vision for what 'Cognified Distributed Computing' might look like and outline some of the challenges that need to be addressed and new technologies that need to be developed in order to make it a reality. In particular, we examine the role cognification can play in tackling power consumption, resiliency and management problems in these systems. We describe intelligent software-based solutions to these problems powered by on-line predictive models built from streamed real-time data. While we cast the problem and our solutions in the context of large Data Centers and HPC systems, we believe our approach to be applicable to distributed computing in general. We believe that the traditional systems research agenda has much to gain by crossing discipline boundaries to include ideas and techniques from Data Science, Machine Learning and Artificial Intelligence