https://www.mdu.se/

Internet of Things (IoT) applications transcend traditional telecom to include enterprise verticals such as transportation, healthcare, agriculture, energy and utilities. Given the vast number of devices and heterogeneity of the applications, both ICT infrastructure and IoT application providers face unprecedented complexity challenges in terms of volume, privacy, interoperability and intelligence. Cognitive automation will be crucial to overcoming the intelligence challenge.

Typically, architecture optimization searches for good architecture candidates based on analyzing a model of the system. Model-based analysis inherently relies on abstractions and estimates, and as such produces approximations which are used to compare architecture candidates. However, approximations are often not sufficient due to the difficulty of accurately estimating certain extra-functional properties. In this paper, we present an architecture optimization approach where the speed of model-based optimization is combined with the accuracy of monitored system runs. Model-based optimization is used to quickly find a good architecture candidate, while optimization based on monitored system runs further refines this candidate. Using measurements assures a higher accuracy of the metrics used for optimization compared to using performance predictions. We demonstrate the feasibility of the approach by implementing it in our framework for optimizing the allocation of software tasks to the processing cores of a multicore embedded system.

The growing enactment of Global Software Engineering in industry has triggered educational institutions to perceive the importance of preparing students for distributed software development. During the last twelve years we have disclosed advantages and pitfalls of GSE to our students through our Distributed Software Development course. After running the projects according to the iterative process model for eleven years, we decided to shift to an agile development model, SCRUM. This decision was due to the growing industrial adoption of agile methods, but more importantly to increase proactiveness, sense of responsibility, and to balance the workload among the project team members. In this paper we describe the process and outcomes of our first attempt at introducing SCRUM in our distributed course.

Modern embedded systems are becoming increasingly performance intensive, since, on the one hand, they include more complex functionality than before, and one the other hand, the functionality that was typically realized with hardware is often moved to software. Multicore technology, previously successfully used for general-purpose systems, is penetrating into the domain of embedded systems. While it does increase the performance capacity, it also introduces the problem of how to allocate software tasks to the cores of the hardware platform, as different allocations exhibit different extra-functional properties. An intuitive example is allocating too many tasks to a core --- the core will be overloaded and tasks will miss their deadlines.

This thesis addresses the issue of task allocation in multicore embedded systems. The overall goal of the thesis is to advance the way soft real-time multicore systems are developed, by providing new methods and tools that enable deciding already at design-time which task to run on which core, with respect to a number of timing-related extra-functional properties. To achieve this goal, we developed a model-based framework for task allocation optimization. The framework uses model simulation in order to obtain performance predictions for particular task allocations. This in turn enables testing a large number of allocation candidates in search for one that exhibits good timing-related performance. Apart from defining and implementing the framework, three additional contributions are provided, each tackling a particular aspect of the framework: the influence of task allocation on communication duration is studied and interpreted in the context of design-time model-based analysis; a novel heuristic for guiding task allocation optimization is defined; and finally, a novel optimization method combining performance prediction and performance measurement is defined.

In many domains of embedded systems, the increasing performance demands are tackled by increasing performance capacity through the use of multicore technology. However, adding more processing units also introduces the issue of task allocation --- decisions have to be made which software task to run on which core in order to best utilize the hardware platform. In this paper, we present an optimization mechanism for allocating tasks to cores of a soft real-time embedded system, that aims to minimize end-to-end response times of task chains, while keeping the number of deadline misses below the desired limit. The optimization relies on a novel heuristic that proposes new allocation candidates based on information how tasks delay each other. The heuristic was evaluated in a series of experiments, which showed that it both finds better allocations, and does it in fewer iterations than two heuristics that we used for comparison.

When a project had followed advices from the best practices, we can raise a question whether the success (or failure) of the project came from following (or not following) the best practices, or whether there were additional reasons that led to the positive (or negative) outcome. In this paper we analyze a case of a student project performed as a part of our Distributed Software Development course. The project followed the advices from the "Ten Tips to Succeed in Global Software Engineering Education" publication. This paper analyzes the project work with respect to the advices. Focusing on the perspective of a student participating in the project, the paper tries to answer whether following the advices is sufficient for a positive project outcome.

The Black Pearl is a custom made autonomous underwater vehicle developed at Mälardalen University, Sweden. It is built in a modular fashion, including its mechanics, electronics and software. After a successful participation at the RoboSub competition in 2012 and winning the prize for best craftsmanship, this year we made minor improvements to the hardware, while the focus of the robot's evolution shifted to the software part. In this paper we give an overview of how the Black Pearl is built, both from the hardware and software point of view.

In order to get accurate performance predictions, design-time architectural analysis of multicore embedded systems has to consider communication overhead. When communicating tasks execute on the same core, the communication typically happens through the local cache. On the other hand, when they run on separate cores, the communication has to go through the shared memory. As the shared memory has a significantly larger latency than the local cache, we expect a significant difference between intra-core and inter-core task communication. In this paper, we present a series of experiments we ran to identify the size of this difference, and discuss its impact on architectural analysis of multicore embedded systems. In particular, we show that the impact of the difference is much lower than anticipated.

This article gives a short overview of the contribution of DICES project. The goal of the project is to advance theories and technologies used in development of distributed embedded systems. Three examples of the contributions are presented: a) reverse engineering of web based applications, design extraction and extraction of reusable user-interface controls, b) a fretwork for building systems that use UPnP devices and treat them as components in the same way as software components, and c) PRIDE - development environment for designing, modeling and developing embedded systems, based on ProCom technology.

Students and teachers do not necessarily have the same understanding of a course – of the purpose, the objective, and in particular of the course elements – the way the course is performed, the examination procedure, and similar. In distributed-development courses, in which students and teachers are dispersed over different locations, this difference can be larger than in “ordinary” courses, but also less visible, due to limited communication. In this paper we discuss these different perspectives, their rationales, possible consequences on the course performance and on the result, as well as lessons learned from students’ feedback.