Optimal Posted Prices for Online Cloud Resource Allocation

We study online resource allocation in a cloud computing platform, through a posted pricing mechanism: The cloud provider publishes a unit price for each resource type, which may vary over time; upon arrival at the cloud system, a cloud user either takes the current prices, renting resources to execute its job, or refuses the prices without running its job there. We design pricing functions based on the current resource utilization ratios, in a wide array of demand-supply relationships and resource occupation durations, and prove worst-case competitive ratios of the pricing functions in terms of social welfare. In the basic case of a single-type, non-recycled resource (i.e., allocated resources are not later released for reuse), we prove that our pricing function design is optimal, in that any other pricing function can only lead to a worse competitive ratio. Insights obtained from the basic cases are then used to generalize the pricing functions to more realistic cloud systems with multiple types of resources, where a job occupies allocated resources for a number of time slots till completion, upon which time the resources are returned back to the cloud resource pool

Dynamic resource provisioning in cloud computing: A randomized auction approach

Abstract—This work studies resource allocation in a cloud market through the auction of Virtual Machine (VM) instances. It generalizes the existing literature by introducing combinatorial auctions of heterogeneous VMs, and models dynamic VM pro-visioning. Social welfare maximization under dynamic resource provisioning is proven NP-hard, and modeled with a linear inte-ger program. An efficient α-approximation algorithm is designed, with α ∼ 2.72 in typical scenarios. We then employ this algorithm as a building block for designing a randomized combinatorial auction that is computationally efficient, truthful in expectation, and guarantees the same social welfare approximation factor α. A key technique in the design is to utilize a pair of tailored primal and dual LPs for exploiting the underlying packing structure of the social welfare maximization problem, to decompose its fractional solution into a convex combination of integral solutions. Empirical studies driven by Google Cluster traces verify the efficacy of the randomized auction. I

Online Training Through Time for Spiking Neural Networks

Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with low latency. Particularly, backpropagation through time (BPTT) with surrogate gradients (SG) is popularly used to achieve high performance in a very small number of time steps. However, it is at the cost of large memory consumption for training, lack of theoretical clarity for optimization, and inconsistency with the online property of biological learning and rules on neuromorphic hardware. Other works connect spike representations of SNNs with equivalent artificial neural network formulation and train SNNs by gradients from equivalent mappings to ensure descent directions. But they fail to achieve low latency and are also not online. In this work, we propose online training through time (OTTT) for SNNs, which is derived from BPTT to enable forward-in-time learning by tracking presynaptic activities and leveraging instantaneous loss and gradients. Meanwhile, we theoretically analyze and prove that gradients of OTTT can provide a similar descent direction for optimization as gradients based on spike representations under both feedforward and recurrent conditions. OTTT only requires constant training memory costs agnostic to time steps, avoiding the significant memory costs of BPTT for GPU training. Furthermore, the update rule of OTTT is in the form of three-factor Hebbian learning, which could pave a path for online on-chip learning. With OTTT, it is the first time that two mainstream supervised SNN training methods, BPTT with SG and spike representation-based training, are connected, and meanwhile in a biologically plausible form. Experiments on CIFAR-10, CIFAR-100, ImageNet, and CIFAR10-DVS demonstrate the superior performance of our method on large-scale static and neuromorphic datasets in small time steps.Comment: Accepted by NeurIPS 202

Proyecto de Fortalecimiento Institucional – Desarrollo de Políticas de Internacionalización de la Educación Superioren en la UNCUYO

O CONGRESSO DE INTERNACIONALIZAÇÃO DA EDUCAÇÃO SUPERIOR – CIES 2019 é um evento in- ternacional, que reúne professores, pesquisadores e estudantes de graduação e pós-graduação para divulgar a produção científica no campo da Internacionalização do Ensino Superior e fortalecer a cooperação internacional entre diferentes instituições de ensino e grupos de pesquisa no âmbito do MERCOSUL. A iniciativa é fruto de uma parceria entre pesquisadores da Universidade Federal da Integra- ção Latino-Americana (UNILA - Brasil), a Universidad Nacional del Litoral (UNL - Argentina), a Uni- versidad Nacional de Asunción (UNA - Paraguay) e a Universidad de la República (UDeLaR - Uru- guay), que atuam em projetos vinculados ao Setor Educacional do MERCOSUL, no Núcleo de Estudos e Investigações em Educação Superior. O evento será realizado nos dias 4, 5 e 6 de Setembro de 2019 no campus PTI da UNILA, dentro do Parque Tecnológico da Usina Hidrelétrica de Itaipu, na cidade de Foz do Iguaçu, Paraná, Brasil. A UNILA, sede do evento, é uma universidade temática criada em 2010 pelo governo federal do Brasil com a missão institucional de formar recursos humanos aptos a contribuir com a integra- ção latino-americana, com o desenvolvimento regional e com o intercâmbio cultural, científico e educacional da América Latina, especialmente no MERCOSUL. Sua finalidade, portanto, é conver- ter-se em um espaço de encontros, de trocas e de aprendizagem mútua, que reforçam o compro- misso em prol da pertinência, da excelência e da construção sustentável de um mundo melhor.La Universidad Nacional de Cuyo es una de las instituciones latinoamericanas que participan del Proyecto DHIP (Desarrollo de Políticas de Internacionalización de la Educación Superior, por sus siglas en inglés). Este proyecto, que también incluye a universidades de Argentina, Colombia, Paraguay, España, Italia y Portugal, se encuentra enmarcado en el programa ERASMUS+, y es cofinanciado por la Comisión Europea, dentro de las acciones de “Capacity building in the field of higher education” (Fortalecimiento institucional en el campo de la educación superior). El proyecto comenzó en octubre de 2018 y durará hasta 2020. Actualmente y a nivel global, las Instituciones de Educación Superior (IES) se proponen poner en marcha distintas iniciativas internacionales, manifestando su compromiso con la idea de convertirse en "instituciones educativas globales". Una mirada más cercana a lo que realmente está sucediendo muestra que con frecuencia estas iniciativas tienen un impacto marginal. En los últimos años, muchas universidades de América Latina han emprendido programas ambiciosos de internacionalización, que en muchos casos han dado resultados por debajo de lo esperado. El proyecto parte de la hipótesis de que existe por tanto una brecha significativa entre los esfuerzos de internacionalización de nuestras instituciones y los resultados concretos.Núcleo de Estudios e Investigaciones en Educación Superior del Mercosur - NUCLEO Grupo Interdisciplinar de Pesquisa em Educação na América Latina – EducAL/UNILA Instituto Mercosul de Estudos Avançados – IMEA/UNILA Pró-Reitoria de Relações Institucionais e Internacionais – PROINT/UNIL

Optimal Distributed Broadcasting with Per-neighbor Queues in Acyclic Overlay Networks with Arbitrary Underlay Capacity Constraints

Abstract—Broadcasting system such as P2P streaming systems represent important network applications that support up to millions of online users. An efficient broadcasting mechanism is at the core of the system design. Despite substantial efforts on developing efficient broadcasting algorithms, the following important question remains open: How to achieve maximum broadcast rate in a distributed manner with each user maintaining information queues only for its direct neighbors? In this work, we first derive an innovative formulation of the problem over acyclic overlay networks with arbitrary underlay capacity constraints. Then, based on the formulation, we develop a distributed algorithm to achieve the maximum broadcast rate and every user only maintains one queue per-neighbor. Due to its lightweight nature, our algorithm scales very well with network size and remains robust against high system dynamics. Finally, by conducting simulations we validate the optimality of our algorithm under different network capacity models. Simulation results further indicate that the convergence time of our algorithm grows linearly with the network size, which suggests an interesting direction for future investigation. I