232 resultados para Scheduling optimization
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10th Conference on Telecommunications (Conftele 2015), Aveiro, Portugal.
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8th International Workshop on Multiple Access Communications (MACOM2015), Helsinki, Finland.
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4th International Conference on Future Generation Communication Technologies (FGCT 2015), Luton, United Kingdom.
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13th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC 2015). 21 to 23, Oct, 2015, Session W1-A: Multiprocessing and Multicore Architectures. Porto, Portugal.
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This work proposes a real-time algorithm to generate a trajectory for a 2 link planar robotic manipulator. The objective is to minimize the space/time ripple and the energy requirements or the time duration in the robot trajectories. The proposed method uses an off line genetic algorithm to calculate every possible trajectory between all cells of the workspace grid. The resultant trajectories are saved in several trees. Then any trajectory requested is constructed in real-time, from these trees. The article presents the results for several experiments.
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Redundant manipulators have some advantages when compared with classical arms because they allow the trajectory optimization, both on the free space and on the presence of abstacles, and the resolution of singularities. For this type of manipulators, several kinetic algorithms adopt generalized inverse matrices. In this line of thought, the generalized inverse control scheme is tested through several experiments that reveal the difficulties that often arise. Motivated by theseproblems this paper presents a new method that ptimizes the manipulability through a least squre polynomialapproximation to determine the joints positions. Moreover, the article studies influence on the dynamics, when controlling redundant and hyper-redundant manipulators. The experiment confirm the superior performance of the proposed algorithm for redundant and hyper-redundant manipulators, revealing several fundamental properties of the chaotic phenomena, and gives a deeper insight towards the future development of superior trajectory control algorithms.
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Paper/Poster presented in Work in Progress Session, 28th GI/ITG International Conference on Architecture of Computing Systems (ARCS 2015). 24 to 26, Mar, 2015. Porto, Portugal.
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Consumer-electronics systems are becoming increasingly complex as the number of integrated applications is growing. Some of these applications have real-time requirements, while other non-real-time applications only require good average performance. For cost-efficient design, contemporary platforms feature an increasing number of cores that share resources, such as memories and interconnects. However, resource sharing causes contention that must be resolved by a resource arbiter, such as Time-Division Multiplexing. A key challenge is to configure this arbiter to satisfy the bandwidth and latency requirements of the real-time applications, while maximizing the slack capacity to improve performance of their non-real-time counterparts. As this configuration problem is NP-hard, a sophisticated automated configuration method is required to avoid negatively impacting design time. The main contributions of this article are: 1) An optimal approach that takes an existing integer linear programming (ILP) model addressing the problem and wraps it in a branch-and-price framework to improve scalability. 2) A faster heuristic algorithm that typically provides near-optimal solutions. 3) An experimental evaluation that quantitatively compares the branch-and-price approach to the previously formulated ILP model and the proposed heuristic. 4) A case study of an HD video and graphics processing system that demonstrates the practical applicability of the approach.
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Poster presented in Work in Progress Session, 28th GI/ITG International Conference on Architecture of Computing Systems (ARCS 2015). 24 to 26, Mar, 2015. Porto, Portugal.
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Presented at 21st IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2015). 19 to 21, Aug, 2015, pp 122-131. Hong Kong, China.
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A optimização nas aplicações modernas assume um carácter fortemente interdisciplinar, relacionando-se com a necessidade de integração de diferentes técnicas e paradigmas na resolução de problemas reais complexos. O problema do escalonamento é recorrente no planeamento da produção. Sempre que uma ordem de fabrico é lançada, é necessário determinar que recursos serão utilizados e em que sequência as atividades serão executadas, para otimizar uma dada medida de desempenho. Embora ainda existam empresas a abordar o problema do escalonamento através de simples heurísticas, a proposta de sistemas de escalonamento tem-se evidenciado na literatura. Pretende-se nesta dissertação, a realização da análise de desempenho de Técnicas de Optimização, nomeadamente as meta-heurísticas, na resolução de problemas de optimização complexos – escalonamento de tarefas, particularmente no problema de minimização dos atrasos ponderados, 1||ΣwjTj. Assim sendo, foi desenvolvido um protótipo que serviu de suporte ao estudo computacional, com vista à avaliação do desempenho do Simulated Annealing (SA) e o Discrete Artificial Bee Colony (DABC). A resolução eficiente de um problema requer, em geral, a aplicação de diferentes métodos, e a afinação dos respetivos parâmetros. A afinação dos parâmetros pode permitir uma maior flexibilidade e robustez mas requer uma inicialização cuidadosa. Os parâmetros podem ter uma grande influência na eficiência e eficácia da pesquisa. A sua definição deve resultar de um cuidadoso esforço experimental no sentido da respectiva especificação. Foi usado, no âmbito deste trabalho de mestrado, para suportar a fase de parametrização das meta-heurísticas em análise, o planeamento de experiências de Taguchi. Da análise dos resultados, foi possível concluir que existem vantagem estatisticamente significativa no desempenho do DABC, mas quando analisada a eficiência é possível concluir que há vantagem do SA, que necessita de menos tempo computacional.
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Recent embedded processor architectures containing multiple heterogeneous cores and non-coherent caches renewed attention to the use of Software Transactional Memory (STM) as a building block for developing parallel applications. STM promises to ease concurrent and parallel software development, but relies on the possibility of abort conflicting transactions to maintain data consistency, which in turns affects the execution time of tasks carrying transactions. Because of this fact the timing behaviour of the task set may not be predictable, thus it is crucial to limit the execution time overheads resulting from aborts. In this paper we formalise a FIFO-based algorithm to order the sequence of commits of concurrent transactions. Then, we propose and evaluate two non-preemptive and one SRP-based fully-preemptive scheduling strategies, in order to avoid transaction starvation.
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Presented at IEEE Real-Time Systems Symposium (RTSS 2015). 1 to 4, Dec, 2015. San Antonio, U.S.A..
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Presented at IEEE Real-Time Systems Symposium (RTSS 2015). 1 to 4, Dec, 2015. San Antonio, U.S.A..
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Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.