Provably good scheduling of sporadic tasks with resource sharing on a two-type heterogeneous multiprocessor platform

Consider the problem of scheduling a set of implicit-deadline sporadic tasks to meet all deadlines on a two-type heterogeneous multiprocessor platform where a task may request at most one of |R| shared resources. There are m1 processors of type-1 and m2 processors of type-2. Tasks may migrate only when requesting or releasing resources. We present a new algorithm, FF-3C-vpr, which offers a guarantee that if a task set is schedulable to meet deadlines by an optimal task assignment scheme that only allows tasks to migrate when requesting or releasing a resource, then FF-3Cvpr also meets deadlines if given processors 4+6*ceil(|R|/min(m1,m2)) times as fast. As far as we know, it is the first result for resource sharing on heterogeneous platforms with provable performance.

A framework for programming sensor networks with scheduling and resource-sharing optimizations

Several projects in the recent past have aimed at promoting Wireless Sensor Networks as an infrastructure technology, where several independent users can submit applications that execute concurrently across the network. Concurrent multiple applications cause significant energy-usage overhead on sensor nodes, that cannot be eliminated by traditional schemes optimized for single-application scenarios. In this paper, we outline two main optimization techniques for reducing power consumption across applications. First, we describe a compiler based approach that identifies redundant sensing requests across applications and eliminates those. Second, we cluster the radio transmissions together by concatenating packets from independent applications based on Rate-Harmonized Scheduling.

Resource sharing in global fixed-priority preemptive multiprocessor scheduling

In this paper we consider global fixed-priority preemptive multiprocessor scheduling of constrained-deadline sporadic tasks that share resources in a non-nested manner. We develop a novel resource-sharing protocol and a corresponding schedulability test for this system. We also develop the first schedulability analysis of priority inheritance protocol for the aforementioned system. Finally, we show that these protocols are efficient (based on the developed schedulability tests) for a class of priority-assignments called reasonable priority-assignments.

Provably good multiprocessor scheduling with resource sharing

We present a 12(1 + 3R/(4m)) competitive algorithm for scheduling implicit-deadline sporadic tasks on a platform comprising m processors, where a task may request one of R shared resources.

Simulation relations, interface complexity, and resource optimality for real-time hierachical systems

Compositional schedulability analysis of hierarchical realtime systems is a well-studied problem. Various techniques have been developed to abstract resource requirements of components in such systems, and schedulability has been addressed using these abstract representations (also called component interfaces). These approaches for compositional analysis incur resource overheads when they abstract components into interfaces. In this talk, we define notions of resource schedulability and optimality for component interfaces, and compare various approaches.

Wind resource modelling in complex terrain using different mesoscale–microscale coupling techniques

Wind resource evaluation in two sites located in Portugal was performed using the mesoscale modelling system Weather Research and Forecasting (WRF) and the wind resource analysis tool commonly used within the wind power industry, the Wind Atlas Analysis and Application Program (WAsP) microscale model. Wind measurement campaigns were conducted in the selected sites, allowing for a comparison between in situ measurements and simulated wind, in terms of flow characteristics and energy yields estimates. Three different methodologies were tested, aiming to provide an overview of the benefits and limitations of these methodologies for wind resource estimation. In the first methodology the mesoscale model acts like “virtual” wind measuring stations, where wind data was computed by WRF for both sites and inserted directly as input in WAsP. In the second approach, the same procedure was followed but here the terrain influences induced by the mesoscale model low resolution terrain data were removed from the simulated wind data. In the third methodology, the simulated wind data is extracted at the top of the planetary boundary layer height for both sites, aiming to assess if the use of geostrophic winds (which, by definition, are not influenced by the local terrain) can bring any improvement in the models performance. The obtained results for the abovementioned methodologies were compared with those resulting from in situ measurements, in terms of mean wind speed, Weibull probability density function parameters and production estimates, considering the installation of one wind turbine in each site. Results showed that the second tested approach is the one that produces values closest to the measured ones, and fairly acceptable deviations were found using this coupling technique in terms of estimated annual production. However, mesoscale output should not be used directly in wind farm sitting projects, mainly due to the mesoscale model terrain data poor resolution. Instead, the use of mesoscale output in microscale models should be seen as a valid alternative to in situ data mainly for preliminary wind resource assessments, although the application of mesoscale and microscale coupling in areas with complex topography should be done with extreme caution.

A hybrid genetic algorithm for the multi-mode resource-constrained project scheduling

A construction project is a group of discernible tasks or activities that are conduct-ed in a coordinated effort to accomplish one or more objectives. Construction projects re-quire varying levels of cost, time and other resources. To plan and schedule a construction project, activities must be defined sufficiently. The level of detail determines the number of activities contained within the project plan and schedule. So, finding feasible schedules which efficiently use scarce resources is a challenging task within project management. In this context, the well-known Resource Constrained Project Scheduling Problem (RCPSP) has been studied during the last decades. In the RCPSP the activities of a project have to be scheduled such that the makespan of the project is minimized. So, the technological precedence constraints have to be observed as well as limitations of the renewable resources required to accomplish the activities. Once started, an activity may not be interrupted. This problem has been extended to a more realistic model, the multi-mode resource con-strained project scheduling problem (MRCPSP), where each activity can be performed in one out of several modes. Each mode of an activity represents an alternative way of combining different levels of resource requirements with a related duration. Each renewable resource has a limited availability for the entire project such as manpower and machines. This paper presents a hybrid genetic algorithm for the multi-mode resource-constrained pro-ject scheduling problem, in which multiple execution modes are available for each of the ac-tivities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme. It is evaluated the quality of the schedules and presents detailed comparative computational re-sults for the MRCPSP, which reveal that this approach is a competitive algorithm.

A genetic algorithm for the resource constrained multi-project scheduling problem

This paper presents a genetic algorithm for the resource constrained multi-project scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a heuristic that builds parameterized active schedules based on priorities, delay times, and release dates defined by the genetic algorithm. The approach is tested on a set of randomly generated problems. The computational results validate the effectiveness of the proposed algorithm.

Low-loss austempered ductile iron gears: experimental evaluation comparing materials and lubricants

An experimental study to evaluate the power dissipation of gears was performed. Three low-loss gear models were manufactured using standard 20° pressure angle tools. Austempered ductile iron (ADI) and 20MnCr5 carburized steel gears were tested in an FZG gear test machine using mineral, ester and polyalphaolephine (PAO)-based oils. The results compare power dissipation, the influence of different tooth flank geometries, materials and lubricants. This work concludes that conventional power-transmission gears can be replaced by these improved and more efficient low–loss models, which can be produced using common tools and that steel gears can be successfully replaced by austempered ductile iron gears.

A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem

This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

A two-level genetic algorithm for the multi-mode resource-constrained project scheduling problem

This paper presents a genetic algorithm for the multimode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme by introducing an improvement procedure. It is evaluated the quality of the schedule and present detailed comparative computational results for the MRCPSP, which reveal that this approach is a competitive algorithm.

A random key based genetic algorithm for the resource constrained project scheduling problem

This paper presents a genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities of the activities are defined by the genetic algorithm. The heuristic generates parameterized active schedules. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

Mechanical behaviour analysis of polyester polymer mortars modified with recycled GFRP waste materials

In this study the effect of incorporation of recycled glass-fibre reinforced polymer (GFRP) waste materials, obtained by means of milling processes, on mechanical behaviour of polyester polymer mortars was assessed. For this purpose, different contents of recycled GFRP waste powder and fibres, with distinct size gradings, were incorporated into polyester based mortars as sand aggregates and filler replacements. Flexural and compressive loading capacities were evaluated and found better than unmodified polymer mortars. GFRP modified polyester based mortars also show a less brittle behaviour, with retention of some loading capacity after peak load. Obtained results highlight the high potential of recycled GFRP waste materials as efficient and sustainable reinforcement and admixture for polymer concrete and mortars composites, constituting an emergent waste management solution.

Reusability assessment of thermoset polymeric composite wastes as reinforcement and filler replacement for polymer concrete materials

The development and applications of thermoset polymeric composites, namely fiber reinforced polymers (FRP), have shifted in the last decades more and more into the mass market [1]. Production and consume have increased tremendously mainly for the construction, transportation and automobile sectors [2, 3]. Although the many successful uses of thermoset composite materials, recycling process of byproducts and end of lifecycle products constitutes a more difficult issue. The perceived lack of recyclability of composite materials is now increasingly important and seen as a key barrier to the development or even continued used of these materials in some markets.

A new horizon for online teaching and learning

The Polytechnic Institute of Oporto (IPP), which has a solid history of online education and innovation through the use of technology, has been particularly interested and focused on Massive Open Online Courses (MOOC) developments. The aim of this paper is to present the whole process from initial discussions to completion of the “Mathematics Without Limits” MOOC Project that exists in IPP and also to contribute for a change in the way as teaching and learning Mathematics is seen and practiced nowadays. In 2013, IPP developed its own platform, which gave us the opportunity to explore new educational techniques as a pedagogical resource as well as to enhance students’ motivation, through a set of interactive materials at their disposal, totally adapted to their needs. Students lack of motivation is mainly justified by their weak Math preparation, poor consolidated basis on the subject and different backgrounds of the students. To tackle this issue and based on our Math online courses teaching experience, we decided to create short duration MOOC, expecting to aid retention of students and also to reverse the path of students giving up on Math by giving them a friendly way of managing their own learning commitment. We also think that this MOOC will be a good approach to level out some math skills among freshmen.