The brain within its groove Runs evenly and true; But let a splinter swerve, 'Twere easier for you to put the water back When floods have slit the hills And scooped a turnpike for themselves, And blotted out the mills! Emily Dickinson iv AgradecimientosEl origen de esta investigación proviene de la propuesta que el profesor Camilo Cortés junto con sus colegas doctores y estudiantes envió a la convocatoria de regalías del año 2018 en el departamento del Vichada.Por su parte, el desarrollo de esta investigación fue soportado en gran parte por la reactividad del señor David Romero PhD. que pese a no ser el co-director de la tesis, merece el título sin dubitación y todo el crédito que viene con él.El autor agradece principalmente a estos compañeros investigadores, pues le permitieron participar en el proyecto y transversalmente aprender del tema de optimización, hasta donde más fue posible.Palabras clave: MILP, energía no suministrada, microrred de pequeña escala, dimensionamiento óptimo, indisponibilidad de generación, cargas críticas, deslastre de carga.

No abstract

This article presents a Differentiated Storage Services architecture for file and storage systems. By classifying data at the block-level, a filesystem can request that different classes of data (e.g., file, directory, executable, text) be handled with different policies (e.g., low-latency versus highbandwidth), and it is left to the storage system to enforce these policies. Our approach assumes that an I/O classifier can be included in-band with each I/O request (e.g., using a field in the SCSI block command set) and that the policy for each class can be specified out-of-band through the management interface of the storage system. We describe our prototypes based on Linux Ext3, Windows NTFS, and hybrid storage systems composed of rotating and solid-state disks. With very little modification, filesystems can identify latency sensitive I/O classes (e.g., small files, directories, metadata, and the journal) and request that the storage system provision the solid-state storage for just these classes; and this is simply one of many possibilities. As part of our ongoing work, across a variety of file and storage systems, we are exploring other policies and mechanisms that can be used to improve application performance, reliability, and security.

No abstract

Relative fitness is a new black-box approach to modeling the performance of storage devices. In contrast with an absolute model that predicts the performance of a workload on a given storage device, a relative fitness model predicts performance differences between a pair of devices. There are two primary advantages to this approach. First, because a relative fitness model is constructed for a device pair, the application-device feedback of a closed workload can be captured (e.g., how the I/O arrival rate changes as the workload moves from device A to device B). Second, a relative fitness model allows performance and resource utilization to be used in place of workload characteristics. This is beneficial when workload characteristics are difficult to obtain or concisely express (e.g., rather than describe the spatio-temporal characteristics of a workload, one could use the observed cache behavior of device A to help predict the performance of B).This paper describes the steps necessary to build a relative fitness model, with an approach that is general enough to be used with any black-box modeling technique. We compare relative fitness models and absolute models across a variety of workloads and storage devices. On average, relative fitness models predict bandwidth and throughput within 10-20% and can reduce prediction error by as much as a factor of two when compared to absolute models.