This paper presents an Agent-Based modeling and simulation to design a decision support system (DSS) for the operation of Healthcare Emergency Departments (ED). This DSS aims to aid EDs managers in setting up strategies and management guidelines to optimize the operation of EDs. This ongoing research is being performed by the Research Group on Individual Oriented Modeling (IoM) of CAOS in the University Autonoma of Barcelona (UAB) in close collaboration with Hospital ED Staff. The simulation main objective is to optimize the performance of such complex and dynamic Healthcare ED. Optimization is performed to find the optimal ED staff configuration, which consists of doctors, triage nurses, and admission personnel, i.e. a multidimensional problem. Two different indexes, to minimize patient waiting time, and to maximize patient throughput, were proposed and tested and their results obtained appying an exhaustive search technique, yield promising results and better understanding of the problem.

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The optimal size of a large on-chip cache can be different for different programs: at some point, the reduction of cache misses achieved when increasing cache size hits diminishing returns, while the higher cache latency hurts performance. This paper presents the Amorphous Cache (AC), a reconfigurable L2 on-chip cache aimed at improving performance as well as reducing energy consumption. AC is composed of heterogeneous sub-caches as opposed to common caches using homogenous subcaches. The sub-caches are turned off depending on the application workload to conserve power and minimize latencies. A novel reconfiguration algorithm based on Basic Block Vectors is proposed to recognize program phases, and a learning mechanism is used to select the appropriate cache configuration for each program phase. We compare our reconfigurable cache with existing proposals of adaptive and non-adaptive caches. Our results show that the combination of AC and the novel reconfiguration algorithm provides the best power consumption and performance. For example, on average, it reduces the cache access latency by 55.8%, the cache dynamic energy by 46.5%, and the cache leakage power by 49.3% with respect to a non-adaptive cache.

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