Titlebook: High Performance Computing; First HPCLATAM - CLC Gonzalo Hernández,Carlos Jaime Barrios Hernández,M Conference proceedings 2014 Springer-Ve

osculate

ONYM

interference

SI-Based Scheduling of Parameter Sweep Experiments on Federated Cloudshere custom virtual machines (VM) are launched in appropriate hosts belonging to different providers to execute scientific experiments and minimize response time. Here, scheduling is performed at three levels. First, at the ., datacenters are selected by their network latencies via three policies –L

Reservation

Distributed Cache Strategies for Machine Learning Classification Tasks over Cluster Computing Resourerative nature where the same data records are processed several times. Data caching becomes key to minimize data transmission through iterations at each node and, thus, contribute to the overall scalability. In this work we propose a two level caching architecture (disk and memory) and benchmark di

中子

A Flexible Strategy for Distributed and Parallel Execution of a Monolithic Large-Scale Sequential Apcores architectures and hardly ever are distributed. In this paper we propose a flexible strategy for execution of those legacy codes, identifying main modules involved in the process. Key technologies involved and a tentative implementation are provided allowing to understand challenges and limitat

放牧

A Model to Calculate Amazon EC2 Instance Performance in Frost Prediction Applicationss can be predicted using Agricultural Monitoring Systems (AMS). AMS provide information to start and stop frosts defense systems and thus reduce economic losses. In recent years, the emergence of infrastructures called Sensor Clouds improved AMS in several aspects such as scalability, reliability, f

confide

Ensemble Learning of Run-Time Prediction Models for Data-Intensive Scientific Workflowsnt of such applications is the prediction of tasks performance. This paper proposes a novel approach that enables the construction models for predicting task’s running-times of data-intensive scientific workflows. Ensemble Machine Learning techniques are used to produce robust combined models with h

heirloom

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