Determining the optimal location of control cabinet components requires the exploration of a large configuration space. For real-world control cabinets it is impractical to evaluate all possible cabinet configurations. Therefore, we need to apply methods for intelligent exploration of cabinet configuration space that enable to find a near-optimal configuration without evaluation of all possible configurations. In this paper, we describe an approach for multi-objective optimization of control cabinet layout that is based on Pareto Simulated Annealing. Optimization aims at minimizing the total wire length used for interconnection of components and the heat convection within the cabinet. We simulate heat convection to study the warm air flow within the control cabinet and determine the optimal position of components that generate heat during the operation. We evaluate and demonstrate the effectiveness of our approach empirically for various control cabinet sizes and usage scenarios.
@cite_8 surveyed various methods (such as, Simulated Annealing) for decentralized scheduling in Grid computing environments. Grid scheduling involves mapping of a collection of tasks to resources with the aim of minimizing the total execution time of all considered tasks. Compared to a game theory scheduling method, the average scheduling time per task of Simulated Annealing was lower.
@cite_8
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In grid environments applications require dynamic scheduling for optimized assignment of tasks on available resources, so the optimization represents a key solution for scheduling. This paper presents an evaluation of multi-objective decentralized scheduling models for the problem of task allocation. It also presents a survey of existing optimization solutions for grid scheduling. The surveyed scheduling solutions are: random and best of n random, exhaustive search, simulated annealing, game theory, ad-hoc greedy scheduler, and genetic algorithm for decentralized scheduling. We carry out our experiments with various scheduling scenarios and with heterogeneous input tasks and computation resources. We also present the methods to evaluate and validate the described scheduling methods. We present several experimental results that offer a support for near-optimal algorithm selection. The problem of optimally scheduling tasks onto heterogeneous resources in grids, minimizing the makespan of these tasks, has proved to be NP-complete. There is no best scheduling algorithm for all grid computing systems. An alternative is to select an appropriate scheduling algorithm to use in a given grid environment because of the characteristics of the tasks, machines and network connectivity. In this paper a survey is presented on the problem and the different aspects of job scheduling in grids such as (a) fault-tolerance; (b) security; and (c) simulation of grid job scheduling strategies are discussed. This paper also presents a discussion on the future research topics and the challenges of job scheduling in grids. At present,grid task scheduling Algorithms focus on 1×n type grid,namely one scheduler and n resources but neglect m×n type grid.We built a Grid model of m×n type grid using M M 1 queue system,and promoted the concept of task scheduling Nash equilibrium among multi-schedulers.The optimal objective of each scheduler is mean complete time per task.The Nash equilibrium took advantage of PSO to be solved.By simulations,we conclude that the new algorithm is better than the algorithm based on the mean scheduling strategies in mean finished task numbers per time,mean load of network and mean load of Grid resources. Mixed-machine heterogeneous computing (HC) environments utilize a distributed suite of different high-performance machines, interconnected with high-speed links, to perform different computationally intensive applications that have diverse computational requirements. HC environments are well suited to meet the computational demands of large, diverse groups of tasks. The problem of optimally mapping (defined as matching and scheduling) these tasks onto the machines of a distributed HC environment has been shown, in general, to be NP-complete, requiring the development of heuristic techniques. Selecting the best heuristic to use in a given environment, however, remains a difficult problem, because comparisons are often clouded by different underlying assumptions in the original study of each heuristic. Therefore, a collection of 11 heuristics from the literature has been selected, adapted, implemented, and analyzed under one set of common assumptions. It is assumed that the heuristics derive a mapping statically (i.e., off-line). It is also assumed that a metatask (i.e., a set of independent, noncommunicating tasks) is being mapped and that the goal is to minimize the total execution time of the metatask. The 11 heuristics examined are Opportunistic Load Balancing, Minimum Execution Time, Minimum Completion Time, Min?min, Max?min, Duplex, Genetic Algorithm, Simulated Annealing, Genetic Simulated Annealing, Tabu, and A*. This study provides one even basis for comparison and insights into circumstances where one technique will out-perform another. The evaluation procedure is specified, the heuristics are defined, and then comparison results are discussed. It is shown that for the cases studied here, the relatively simple Min?min heuristic performs well in comparison to the other techniques.