Matlab2Trace: A Matlab to Trace translator to visualise and analyse concurrent system activities and execution traces

Matlab provides an environment to analyse and visualise data and develop algorithms. However, there is limited support for visualising and analysing system activities executing concurrently, for instance, on a multiprocessor platform. Trace (https://esi.nl/research/output/tools/trace) is software that specialises in visualising and analysing concurrent system activities and execution traces. We present a Matlab to Trace translator that directly generates a trace-input file from the Matlab environment. Concurrent system activities and execution traces of the algorithms developed inside the Matlab environment can be visualised and analysed in Trace using the generated trace-input file. The translator takes as input the logical or absolute starting and ending time of the algorithmic execution, and the number (and labels) of processing cores. TRACE visualizes concurrent activities in a Gantt-chart-like view which provides colouring, grouping and filtering options. TRACE also provides several analysis methods, which sets it apart from the many other Gantt-chart visualization tools: i) Critical-path analysis can be used to detect tasks and resources that are bottlenecks for performance; ii) Distance analysis can be used to compare execution traces with respect to structure, e.g. to check a model trace against an implementation trace; iii) MTL checking provides a means to formally specify and verify properties of execution traces using Metric Temporal Logic. It is useful to express and check, for instance, performance properties such as the “processing latency is at most 50 ms”; iv) The streaming performance DSL is a domain-specific language that captures often-used performance properties for stream-processing systems (e.g., image or video processing), and which eases the use of the MTL checker; and v) The resource usage feature can quickly give insight in the details of the resource usage. The Matlab2Trace can be downloaded from https://github.com/TUE-EE-ES/Matlab2Trace

Effective link quality estimation as a means to improved end-to-end packet delivery in high traffic mobile ad hoc networks

Accurate link quality estimation is a fundamental building block in quality aware multi hop routing. In an inherently lossy, unreliable and dynamic medium such as wireless, the task of accurate estimation becomes very challenging. Over the years ETX has been widely used as a reliable link quality estimation metric. However, more recently it has been established that under heavy traffic loads ETX performance gets significantly worse. We examine the ETX metric's behavior in detail with respect to the MAC layer and UDP data; and identify the causes of its unreliability. Motivated by the observations made in our analysis, we present the design and implementation of our link quality measurement metric xDDR – a variation of ETX. This article extends xDDR to support network mobility. Our experiments show that xDDR substantially outperforms minimum hop count, ETX and HETX in terms of end-to-end packet delivery ratio in static as well as mobile scenarios.</p

Condition based spare parts supply

We consider a spare parts stock point that serves an installed base of machines. Each machine contains the same critical component, whose degradation behavior is described by a Markov process. We consider condition based spare parts supply, and show that an optimal, condition based inventory policy is 20% more efficient on average than a standard, state-independent base stock policy. We further propose an efficient and effective heuristic policy

Model-driven quality and resource management for CPSs

A Cyber-Physical System (CPS) integrates cyber systems, human users, networks and physical systems. Thus, a CPS needs visual context and awareness to make autonomous and correct decisions. Advanced image and video processing is computationally intensive and challenging. Moreover, a CPS comprises increasingly complex and distributed configurations, which is reflectedin the growing number of sensors, actuators and other smart devices. This leadsto an exponential number of dynamic system configurations. To make mattersworse, a CPS needs to simultaneously satisfy many rigorous constraints, e.g.,hard deadlines, safety, quality, and performance. Hence, the system designeris confronted with an immense number of potential configurations of which anumber meet the constraints and only a fraction are optimal regarding certainqualities. This makes finding the optimal configurations hard, especially duringrun-time. A domain-specific language (DSL) for quality and resource managment (QRM) is presented to specify these configurations conveniently and reasonabout them in an automated manner

Control of platooned vehicles in presence of traffic shock waves

Vehicle platooning has been attracting attention recently because of its ability to improve road capacity, safety and fuel efficiency. Vehicles communicate using Vehicle-toVehicle (V2V) wireless communication, making their status (acceleration, position, etc.) available to other vehicles. Shock waves, i.e. zones of reduced traffic speed that propagate upstream, are a well known emergent traffic phenomenon. Since vehicles entering such a zone need to decelerate sharply, shock waves cause a deterioration of fuel economy, driving comfort, and safety. While typically caused by bad driving behavior, recent studies have shown that it is possible to diminish or dissipate shock waves by applying certain good driving behavioral patterns. In this work, we use the information about the traffic situation to adapt the reference speed profile of the platoon we control, in order to mitigate the effect of a shock wave coming from downstream. The platoon leader receives the velocity of the vehicles downstream of the platoon and distance gap between them using V2V communication and it computes the shock wave speed. We show that by doing this we reduce the fuel consumption of the vehicles in the platoon, and improve the traffic situation by helping dissipate the shock wave. We validate our results using microscopic models with the help of a toolchain composed of Matlab, and the SUMO traffic simulator

Throughput-buffering trade-off exploration for cyclo-static and synchronous dataflow graphs.

Multimedia applications usually have throughput constraints. An implementation must meet these constraints, while it minimizes resource usage and energy consumption. The compute intensive kernels of these applications are often specified as Cyclo-Static or Synchronous Dataflow Graphs. Communication between nodes in these graphs requires storage space which influences throughput. We present an exact technique to chart the Pareto space of throughput and storage trade-offs, which can be used to determine the minimal buffer space needed to execute a graph under a given throughput constraint. The feasibility of the exact technique is demonstrated with experiments on a set of realistic DSP and multimedia applications. To increase scalability of the approach, a fast approximation technique is developed that guarantees both throughput and a, tight, bound on the maximal overestimation of buffer requirements. The approximation technique allows to trade off worst-case overestimation versus run-time