88,966 research outputs found

    Dynamic load balancing of parallel road traffic simulation

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    The objective of this research was to investigate, develop and evaluate dynamic load-balancing strategies for parallel execution of microscopic road traffic simulations. Urban road traffic simulation presents irregular, and dynamically varying distributed computational load for a parallel processor system. The dynamic nature of road traffic simulation systems lead to uneven load distribution during simulation, even for a system that starts off with even load distributions. Load balancing is a potential way of achieving improved performance by reallocating work from highly loaded processors to lightly loaded processors leading to a reduction in the overall computational time. In dynamic load balancing, workloads are adjusted continually or periodically throughout the computation. In this thesis load balancing strategies were evaluated and some load balancing policies developed. A load index and a profitability determination algorithms were developed. These were used to enhance two load balancing algorithms. One of the algorithms exhibits local communications and distributed load evaluation between the neighbour partitions (diffusion algorithm) and the other algorithm exhibits both local and global communications while the decision making is centralized (MaS algorithm). The enhanced algorithms were implemented and synthesized with a research parallel traffic simulation. The performance of the research parallel traffic simulator, optimized with the two modified dynamic load balancing strategies were studied

    Dynamic load balancing of parallel road traffic simulation

    Get PDF
    The objective of this research was to investigate, develop and evaluate dynamic load-balancing strategies for parallel execution of microscopic road traffic simulations. Urban road traffic simulation presents irregular, and dynamically varying distributed computational load for a parallel processor system. The dynamic nature of road traffic simulation systems lead to uneven load distribution during simulation, even for a system that starts off with even load distributions. Load balancing is a potential way of achieving improved performance by reallocating work from highly loaded processors to lightly loaded processors leading to a reduction in the overall computational time. In dynamic load balancing, workloads are adjusted continually or periodically throughout the computation. In this thesis load balancing strategies were evaluated and some load balancing policies developed. A load index and a profitability determination algorithms were developed. These were used to enhance two load balancing algorithms. One of the algorithms exhibits local communications and distributed load evaluation between the neighbour partitions (diffusion algorithm) and the other algorithm exhibits both local and global communications while the decision making is centralized (MaS algorithm). The enhanced algorithms were implemented and synthesized with a research parallel traffic simulation. The performance of the research parallel traffic simulator, optimized with the two modified dynamic load balancing strategies were studied.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    An efficient adaptative predictive load balancing method for distributed systems

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    When allocating processors to processes in a distributed system, load balancing is a main concern of designers. By its implementation, system performance can be enhanced by equally distributing the dynamically changing workload and consequently user expectation are improved through an additional reduction on mean response time. In this way, through process migration, a rational and equitable use of the system computational power is achieved, preventing degradation of system performance due to unbalanced work of processors. This article presents an Adaptative Predictive Load Balancing Strategy (APLBS), a variation of Predictive Load Balancing Strategy (PLBS) reported elsewhere [1]. As PLBS, APLBS is a sender initiated, prediction-based strategy for load balancing. The predictive approach is based on estimates given by a weighted exponential average [12] of the load condition of each node in the system. The new approach tries to minimise traffic en the network selecting the most suitable subset of candidates to request migration and the novel aspect is that the size of this subset is adaptative with respect to the system workload. APLBS was contrasted against Random (R), PLBS and Flexible Load Sharing (FLS) [7] strategies on diverse scenarios where the load can be characterised as static or dynamic. A comparative analysis of mean response time, acceptance hit ratio and number of migration failures under each strategy is reported.Sistemas Distribuidos - Redes ConcurrenciaRed de Universidades con Carreras en Informática (RedUNCI
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