Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)

The Helmholtz Association funded the "Large-Scale Data Management and Analysis" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Resource efficient processing and communication in sensor/actuator environments

The future of computer systems will not be dominated by personal computer like hardware platforms but by embedded and cyber-physical systems assisting humans in a hidden but omnipresent manner. These pervasive computing devices can, for example, be utilized in the home automation sector to create sensor/ actuator networks supporting the inhabitants of a house in everyday life. The efficient usage of resources is an important topic at design time and operation time of mobile embedded and cyber-physical systems. Therefore, this thesis presents methods which allow an efficient use of energy and processing resources in sensor/actuator networks. These networks comprise different nodes cooperating for a “smart” joint control function. Sensor/actuator nodes are typical cyber-physical systems comprising sensors/actuators and processing and communication components. Processing components of today’s sensor nodes can comprise many-core chips. This thesis introduces new methods for optimizing the code and the application mapping of the aforementioned systems and presents novel results with regard to design space explorations for energy-efficient and embedded many-core systems. The considered many-core systems are graphics processing units. The application code for these graphics processing units is optimized for a particular platform variant with the objectives of minimal energy consumption and/or of minimal runtime. These two objectives are targeted with the utilization of multi-objective optimization techniques. The mapping optimizations are realized by means of multi-objective design space explorations. Furthermore, this thesis introduces new techniques and functions for a resource-efficient middleware design employing service-oriented architectures. Therefore, a service-oriented architecture based middleware framework is presented which comprises a lightweight service orchestration. In addition to that, a flexible resource management mechanism will be introduced. This resource management adapts resource utilization and services to an environmental context and provides methods to reduce the energy consumption of sensor nodes

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Cooperative scheduling and load balancing techniques in fog and edge computing

Fog and Edge Computing are two models that reached maturity in the last decade. Today, they are two solid concepts and plenty of literature tried to develop them. Also corroborated by the development of technologies, like for example 5G, they can now be considered de facto standards when building low and ultra-low latency applications, privacy-oriented solutions, industry 4.0 and smart city infrastructures. The common trait of Fog and Edge computing environments regards their inherent distributed and heterogeneous nature where the multiple (Fog or Edge) nodes are able to interact with each other with the essential purpose of pre-processing data gathered by the uncountable number of sensors to which they are connected to, even by running significant ML models and relying upon specific processors (TPU). However, nodes are often placed in a geographic domain, like a smart city, and the dynamic of the traffic during the day may cause some nodes to be overwhelmed by requests while others instead may become completely idle. To achieve the optimal usage of the system and also to guarantee the best possible QoS across all the users connected to the Fog or Edge nodes, the need to design load balancing and scheduling algorithms arises. In particular, a reasonable solution is to enable nodes to cooperate. This capability represents the main objective of this thesis, which is the design of fully distributed algorithms and solutions whose purpose is the one of balancing the load across all the nodes, also by following, if possible, QoS requirements in terms of latency or imposing constraints in terms of power consumption when the nodes are powered by green energy sources. Unfortunately, when a central orchestrator is missing, a crucial element which makes the design of such algorithms difficult is that nodes need to know the state of the others in order to make the best possible scheduling decision. However, it is not possible to retrieve the state without introducing further latency during the service of the request. Furthermore, the retrieved information about the state is always old, and as a consequence, the decision is always relying on imprecise data. In this thesis, the problem is circumvented in two main ways. The first one considers randomised algorithms which avoid probing all of the neighbour nodes in favour of at maximum two nodes picked at random. This is proven to bring an exponential improvement in performance with respect to the probe of a single node. The second approach, instead, considers Reinforcement Learning as a technique for inferring the state of the other nodes thanks to the reward received by the agents when requests are forwarded. Moreover, the thesis will also focus on the energy aspect of the Edge devices. In particular, will be analysed a scenario of Green Edge Computing, where devices are powered only by Photovoltaic Panels and a scenario of mobile offloading targeting ML image inference applications. Lastly, a final glance will be given at a series of infrastructural studies, which will give the foundations for implementing the proposed algorithms on real devices, in particular, Single Board Computers (SBCs). There will be presented a structural scheme of a testbed of Raspberry Pi boards, and a fully-fledged framework called ``P2PFaaS'' which allows the implementation of load balancing and scheduling algorithms based on the Function-as-a-Service (FaaS) paradigm

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected