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The energy industry demands computing system technologies with advanced state-of-the-art techniques to achieve reliability and safety for monitoring and properly dealing with several complex constraints. These computing systems also require delivering correct data at the right time imposing hard real-time constraints, because there are lots of situations where missing critical data may be catastrophic. The challenges faced by computer engineers in the energy industry also include designing distributed real-time systems to process such complex control workload. Besides, the computing system may also demand high energy consumption on its own. In this chapter, we demonstrate how to construct a mathematical formulation applicable for these computing systems and how to solve it to distribute the hard real-time workload of the process control systems considering technological constraints and optimizing for low power consumption of such computing systems. We present two computational techniques of resolution: an exact algorithm based on Branch-and-Cut and a meta-heuristic based on Genetic Algorithm. While the exact algorithm combines a branch-and-cut strategy with response time based schedulability analysis, the genetic algorithm still considers the response time schedulability analysis but follows an evolutionary solving strategy. Both computational techniques deliver solutions for heterogeneous computing systems with a control application, considering precedence, preemption, mutual exclusion, timing, temperature, and capacity constraints. In computational experiments, we present the usage of such techniques in a case study based on a control system for a power plant monitoring application.
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- Designing Distributed Real-Time Systems to Process Complex Control Workload in the Energy Industry
Rosiane de Freitas
- Chapter 14
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