Assigning real-time tasks on heterogeneous multiprocessors with two types of processors

Consider the problem of scheduling a set of implicitdeadline sporadic tasks on a heterogeneous multiprocessor so as to meet all deadlines. Tasks cannot migrate and the platform is restricted in that each processor is either of type-1 or type-2 (with each task characterized by a different speed of execution upon each type of processor). We present an algorithm for this problem with a timecomplexity of O(n·m), where n is the number of tasks and m is the number of processors. It offers the guarantee that if a task set can be scheduled by any non-migrative algorithm to meet deadlines then our algorithm meets deadlines as well if given processors twice as fast. Although this result is proven for only a restricted heterogeneous multiprocessor, we consider it significant for being the first realtime scheduling algorithm to use a low-complexity binpacking approach to schedule tasks on a heterogeneous multiprocessor with provably good performance.

Implementing multicore real-time scheduling algorithms based on task splitting using ada 2012

Multiprocessors, particularly in the form of multicores, are becoming standard building blocks for executing reliable software. But their use for applications with hard real-time requirements is non-trivial. Well-known realtime scheduling algorithms in the uniprocessor context (Rate-Monotonic [1] or Earliest-Deadline-First [1]) do not perform well on multiprocessors. For this reason the scientific community in the area of real-time systems has produced new algorithms specifically for multiprocessors. In the meanwhile, a proposal [2] exists for extending the Ada language with new basic constructs which can be used for implementing new algorithms for real-time scheduling; the family of task splitting algorithms is one of them which was emphasized in the proposal [2]. Consequently, assessing whether existing task splitting multiprocessor scheduling algorithms can be implemented with these constructs is paramount. In this paper we present a list of state-of-art task-splitting multiprocessor scheduling algorithms and, for each of them, we present detailed Ada code that uses the new constructs.

Control of baker’s yeast fermentation: PID and fuzzy algorithms

A MATLAB/SIMULINK-based simulator was employed for studies concerning the control of baker’s yeast fed-batch fermentation. Four control algorithms were implemented and compared: the classical PID control, two discrete versions- modified velocity and position algorithms, and a fuzzy law. The simulation package was seen to be an efficient tool for the simulation and tests of control strategies of the nonlinear process.

Engineering PROFIBUS networks with heterogeneous transmission media

A significant number of process control and factory automation systems use PROFIBUS as the underlying fieldbus communication network. The process of properly setting up a PROFIBUS network is not a straightforward task. In fact, a number of network parameters must be set for guaranteeing the required levels of timeliness and dependability. Engineering PROFIBUS networks is even more subtle when the network includes various physical segments exhibiting heterogeneous specifications, such as bus speed or frame formats, just to mention a few. In this paper we provide underlying theory and a methodology to guarantee the proper operation of such type of heterogeneous PROFIBUS networks. We additionally show how the methodology can be applied to the practical case of PROFIBUS networks containing simultaneously DP (Decentralised Periphery) and PA (Process Automation) segments, two of the most used commercial-off-the-shelf (COTS) PROFIBUS solutions. The importance of the findings is however not limited to this case. The proposed methodology can be generalised to cover other heterogeneous infrastructures. Hybrid wired/wireless solutions are just an example for which an enormous eagerness exists.

Iterative refinement approach for QOS-Aware service configuration

In heterogeneous environments, diversity of resources among the devices may affect their ability to perform services with specific QoS constraints, and drive peers to group themselves in a coalition for cooperative service execution. The dynamic selection of peers should be influenced by user’s QoS requirements as well as local computation availability, tailoring provided service to user’s specific needs. However, complex dynamic real-time scenarios may prevent the possibility of computing optimal service configurations before execution. An iterative refinement approach with the ability to trade off deliberation time for the quality of the solution is proposed. We state the importance of quickly finding a good initial solution and propose heuristic evaluation functions that optimise the rate at which the quality of the current solution improves as the algorithms have more time to run.

ENCOURAGE architecture: support for heterogeneous smart grids

Our society relies on energy for most of its activities. One application domain inciding heavily on the energy budget regards the energy consumption in residential and non-residential buildings. The ever increasing needs for energy, resulting from the industrialization of developing countries and from the limited scalability of the traditional technologies for energy production, raises both problems and opportunities. The problems are related to the devastating effects of the greenhouse gases produced by the burning of oil and gas for energy production, and from the dependence of whole countries on companies providing gas and oil. The opportunities are mostly technological, since novel markets are opening for both energy production via renewable sources, and for innovations that can rationalize energy usage. An enticing research effort can be the mixing of these two aspects, by leveraging on ICT technologies to rationalize energy production, acquisition, and consumption. The ENCOURAGE project aims to develop embedded intelligence and integration technologies that will directly optimize energy use in buildings and enable active participation in the future smart grid environment.The primary application domains targeted by the ENCOURAGE project are non-residential buildings (e.g.: campuses) and residential buildings (e.g.: neighborhoods). The goal of the project is to achieve 20% of energy savings through the improved interoperability between various types of energy generation, consumption and storage devices; interbuilding energy exchange; and systematic performance monitoring.

Orchestration of heterogeneous middleware services and its application to a comand and control platform

MSC Dissertation in Computer Engineering

Optimal location of supports in beam structures using genetic algorithms

Real structures can be thought as an assembly of components, as for instances plates, shells and beams. This later type of component is very commonly found in structures like frames which can involve a significant degree of complexity or as a reinforcement element of plates or shells. To obtain the desired mechanical behavior of these components or to improve their operating conditions when rehabilitating structures, one of the eventual parameters to consider for that purpose, when possible, is the location of the supports. In the present work, a beam-type structure is considered, and for a set of cases concerning different number and types of supports, as well as different load cases, the authors optimize the location of the supports in order to obtain minimum values of the maximum transverse deflection. The optimization processes are carried out using genetic algorithms. The results obtained, clearly show a good performance of the approach proposed. © 2014 IEEE.

Fitness function evaluation through fractional algorithms

This paper proposes a Genetic Algorithm (GA) for the design of combinational logic circuits. The fitness function evaluation is calculated using Fractional Calculus. This approach extends the classical fitness function by including a fractional-order dynamical evaluation. The experiments reveal superior results when comparing with the classical method.

Application of genetic algorithms in the design of an electrical potential of fractional order

Fractional calculus (FC) is currently being applied in many areas of science and technology. In fact, this mathematical concept helps the researches to have a deeper insight about several phenomena that integer order models overlook. Genetic algorithms (GA) are an important tool to solve optimization problems that occur in engineering. This methodology applies the concepts that describe biological evolution to obtain optimal solution in many different applications. In this line of thought, in this work we use the FC and the GA concepts to implement the electrical fractional order potential. The performance of the GA scheme, and the convergence of the resulting approximation, are analyzed. The results are analyzed for different number of charges and several fractional orders.

Chemical reaction network of flavylium ions in heterogeneous media

Dissertação apresentada para a obtenção do Grau de Doutor em Química, especialidade em Química-Física, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Optimal tuning of fractional controllers using genetic algorithms

This study addresses the optimization of fractional algorithms for the discrete-time control of linear and non-linear systems. The paper starts by analyzing the fundamentals of fractional control systems and genetic algorithms. In a second phase the paper evaluates the problem in an optimization perspective. The results demonstrate the feasibility of the evolutionary strategy and the adaptability to distinct types of systems.

Application of fractional algorithms in the control of a robotic bird

In this paper, it is studied the dynamics of the robotic bird in terms of time response and robustness. It is analyzed the wing angle of attack and the velocity of the bird, the tail influence, the gliding flight and the flapping flight. The results are positive for the construction of flying robots. The development of computational simulation based on the dynamic of the robotic bird should allow testing strategies and different algorithms of control such as integer and fractional controllers.

Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithms

This study addresses the optimization of rational fraction approximations for the discrete-time calculation of fractional derivatives. The article starts by analyzing the standard techniques based on Taylor series and Padé expansions. In a second phase the paper re-evaluates the problem in an optimization perspective by tacking advantage of the flexibility of the genetic algorithms.

Crossing genetic and swarm intelligence algorithms to generate logic circuits

Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. On the other hand, Particle swarm optimization (PSO) is a population based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as GAs. The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. PSO is attractive because there are few parameters to adjust. This paper presents hybridization between a GA algorithm and a PSO algorithm (crossing the two algorithms). The resulting algorithm is applied to the synthesis of combinational logic circuits. With this combination is possible to take advantage of the best features of each particular algorithm.