Tight arrival curve at the output of a work-conserving blind multiplexing serve

As a means of supporting quality of service guarantees, aggregate multiplexing has attracted a lot of attention in the networking community, since it requires less complexity than flow-based scheduling. However, contrary to what happens in the case of flow-based multiplexing, few results are available for aggregate-based multiplexing. In this paper, we consider a server multiplexer fed by several flows and analyze the impact caused by traffic aggregation on the flows at the output of the server. No restriction is imposed on the server multiplexer other than the fact that it must operate in a work- conserving fashion. We characterize the best arrival curves that constrain the number of bits that leave the server, in any time interval, for each individual flow. These curves can be used to obtain the delays suffered by packets in complex scenarios where multiplexers are interconnected, as well as to determine the maximum size of the buffers in the different servers. Previous results provide tight delay bounds for networks where servers are of the FIFO type. Here, we provide tight bounds for any work-conserving scheduling policy, so that our results can be applied to heterogeneous networks where the servers (routers) can use different work-conserving scheduling policies such as First-In First-Out (FIFO), Earliest Deadline First (EDF), Strict Priority (SP), Guaranteed Rate scheduling (GR), etc

Worst case burstiness increase due to FIFO multiplexing

We consider a FIFO multiplexer fed by flows that are individually constrained by arrival curves, and look for the best possible arrival curve for every output flow. This problem arises in scenarios where aggregate multiplexing is performed, such as differentiated services or front ends to optical switches. We obtain an exact result for a fluid model and for piecewise linear concave arrival curves, which are common in practice and correspond to combinations of leaky buckets

El Estado de Inocencia y la Garantía de la no Autoincriminación en Materia Tributaria: Un Análisis a Partir de la Conexidad Entre el Procedimiento Administrativo y el Proceso Penal

El artículo no presenta resumenIn this article, the author attempts to establish the limitations and consequences resulting from the use of evidence that, being obtained from the exercise of the powers of investigation and enforcement that owns the Treasury, then is carried to the tax criminal proceedings, as the basis for the complaint made by the Tax Administration. For this, the author approaches the taxpayer’s right against self-incrimination (or not to incriminate himself ) as well as some general concepts of the state of innocence, and then analyzes how these concepts are related to the tax law, specifically with the evidence inside it. The article also contains a number of ideas that attempts to summarize the thoughts and positions taken on the issue in current doctrine and jurisprudence in Argentina, establishing in turn, parallel situations and comparisons with the Peruvian tax system

System Stability Under Adversarial Injection of Dependent Tasks

Technological changes (NFV, Osmotic Computing, Cyber-physical Systems) are making very important devising techniques to efficiently run a flow of jobs formed by dependent tasks in a set of servers. These problem can be seen as generalizations of the dynamic job-shop scheduling problem, with very rich dependency patterns and arrival assumptions. In this work, we consider a computational model of a distributed system formed by a set of servers in which jobs, that are continuously arriving, have to be executed. Every job is formed by a set of dependent tasks (i. e., each task may have to wait for others to be completed before it can be started), each of which has to be executed in one of the servers. The arrival of jobs and their properties is assumed to be controlled by a bounded adversary, whose only restriction is that it cannot overload any server. This model is a non-trivial generalization of the Adversarial Queuing Theory model of Borodin et al., and, like that model, focuses on the stability of the system: whether the number of jobs pending to be completed is bounded at all times. We show multiple results of stability and instability for this adversarial model under different combinations of the scheduling policy used at the servers, the arrival rate, and the dependence between tasks in the jobs

Algunas consideraciones sobre la ley penal tributaria y previsional : el instituto de la extinción de la acción penal

Fil: Álvarez Echagüe, Juan Manuel. Universidad de Buenos Aires. Facultad de Derecho. Cátedra Derecho Tributario. Buenos Aires, Argentin

Un caso de big data punta a punta: análisis de datos de transporte y su uso en el negocio

En este artículo se presentan los resultados de un proyecto de análisis de datos de una empresa de transporte, que involucró la recolección, preparación, visualización, transformación y análisis de 3 años de datos de viajes de colectivos, incluyendo boletos y posicionamiento geográfico. Este caso cubre el proyecto de punta a punta, incluyendo la incorporación de los resultados en el proceso de negocio. Nosotros sostenemos que una característica clave de los proyectos de big data debe encontrarse en el proceso que se lleva a cabo y que inicia con la captura de grandes cantidades de datos, pasando por el procesamiento (que en muchos casos requiere una infraestructura especial o particular, con más de una computadora, en modo distribuido) hasta el análisis y el aprovechamiento de la información en el negocio. En nuestro punto de vista, este último paso (la inserción de la información en la toma de decisiones) es tan importante como el uso de bases NoSQL o Hadoop o procesar varios terabytes.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Un caso de big data punta a punta: análisis de datos de transporte y su uso en el negocio

En este artículo se presentan los resultados de un proyecto de análisis de datos de una empresa de transporte, que involucró la recolección, preparación, visualización, transformación y análisis de 3 años de datos de viajes de colectivos, incluyendo boletos y posicionamiento geográfico. Este caso cubre el proyecto de punta a punta, incluyendo la incorporación de los resultados en el proceso de negocio. Nosotros sostenemos que una característica clave de los proyectos de big data debe encontrarse en el proceso que se lleva a cabo y que inicia con la captura de grandes cantidades de datos, pasando por el procesamiento (que en muchos casos requiere una infraestructura especial o particular, con más de una computadora, en modo distribuido) hasta el análisis y el aprovechamiento de la información en el negocio. En nuestro punto de vista, este último paso (la inserción de la información en la toma de decisiones) es tan importante como el uso de bases NoSQL o Hadoop o procesar varios terabytes.Sociedad Argentina de Informática e Investigación Operativa (SADIO