Personal computing currently faces a rapid trend from desktop machines towards mobile services, accessed via tablets, smartphones and similar terminal devices. With respect to computing power, today´s handheld devices are similar to Cray-2 supercomputers from the 1980s. Due to higher computational load (e.g. via multimedia apps) and the variety of radio interfaces (such as WiFi, 3G, and LTE), modern terminals are getting increasingly energy hungry. For instance, a single UMTS upload or a video recording process on today´s smartphones may consume as much as 1.5 Watts, i.e. roughly 50% of the maximal device power. In the near future, higher data rates and traffic, advanced media codecs, and graphics applications will ask for even more energy than the battery can deliver. At the same time, the power density limit might lead to a significant share of “Dark Silicon” at 22nm CMOS and below. Obviously, disruptive energy optimizations are required that go well beyond traditional technologies like DVFS (dynamic voltage and frequency scaling) and power-down of temporarily unused components. The GEMSCLAIM project aims at introducing novel approaches for reducing this “greed for energy”, thereby improving the user experience and enabling new opportunities for mobile computing. The focus is on three novel approaches: (1) cross layer energy optimization, ranging from the compiler over the operating system down to the target HW platform, (2) efficient programming support for energy-optimized heterogeneous Multicore platforms based on energy-aware service level agreements (SLAs) and energy-sensitive tunable parameters, and (3) introducing energy awareness into Virtual Platforms for the purpose of dynamically customizing the HW architecture for energy optimization and online energy monitoring and accounting. GEMSCLAIM will provide new methodologies and tools in these domains and will quantify the potential energy savings via benchmarks and a HW platform prototype.

The DiPET project investigates models and techniques that enable distributed stream processing applications to seamlessly span and redistribute across fog and edge computing systems. The goal is to utilize devices dispersed through the network that are geographically closer to users to reduce network latency and to increase the available network bandwidth. However, the network that user devices are connected to is dynamic. For example, mobile devices connect to different base stations as they roam, and fog devices may be intermittently unavailable for computing. In order to maximally leverage the heterogeneous compute and network resources present in these dynamic networks, the DiPET project pursues a bold approach based on transprecise computing. Transprecise computing states that computation need not always be exact and proposes a disciplined trade-off of precision against accuracy, which impacts on computational effort, energy efficiency, memory usage and communication bandwidth and latency. Transprecise computing allows to dynamically adapt the precision of computation depending on the context and available resources. This creates new dimensions to the problem of scheduling distributed stream applications in fog and edge computing environments and will lead to schedules with superior performance, energy efficiency and user experience. The DiPET project will demonstrate the feasibility of this unique approach by developing a transprecise stream processing application framework and transprecision-aware middleware. Use cases in video analytics and network intrusion detection will guide the research and underpin technology demonstrators.

The potential offered by the abundance of sensors, actuators and communications in IoT era is hindered by the limited computational capacity of local nodes, making the distribution of computing in time and space a necessity. Several key challenges need to be addressed in order to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. Our research takes upon these challenges by dynamically distributing resources when the demand is rapidly time varying. We first propose an analytic mathematical dynamical modelling of the resources, offered workload, and networking environment, that incorporates phenomena met in wireless communications, mobile edge computing data centres, and network topologies. We also propose a new set of estimators for the workload and resources time-varying profiles that continuously update the model parameters. Building on this framework, we aim to develop novel resource allocation mechanisms that take explicitly into account service differentiation and context-awareness, and most importantly, provide formal guarantees for well-defined QoS/QoE metrics. Our research goes well beyond the state of the art also in the design of control algorithms for cyber-physical systems (CPS), by incorporating resource allocation mechanisms to the decision strategy itself. We propose a new generation of controllers, driven by a co-design philosophy both in the network and computing resources utilization. This paradigm has the potential to cause a quantum leap in crucial fields in engineering, e.g., Industry 4.0, collaborative robotics, logistics, multi-agent systems etc. To achieve these breakthroughs, we utilize and combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory and Network Theory, fields where the consortium has internationally leading expertise. Although researchers from Computer and Network Science, Control Engineering and Applied Mathematics have proposed various approaches to tackle the above challenges, our research constitutes the first truly holistic, multidisciplinary approach that combines and extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities. Our developed theory will be extensively tested on available experimental testbed infrastructures of the participating entities. The efficiency of the overall proposed framework will be tested and evaluated under three complex use cases involving mobile autonomous agents in IoT environments: (i) distributed remote path planning of a group of mobile robots with complex specifications, (ii) rapid deployment of mobile agents for distributed computing purposes in disaster scenarios and (iii) mobility-aware resource allocation for crowded areas with pre-defined performance indicators to reach.