Coordenação de Infraestruturas Computacionais Distribuídas em larga escala baseadas em redes de broadcast de Sistemas de Televisão Digital. - Vieira, Diénert de Alencar (2013)
The On-Demand Distributed Computing Infrastructure (OddCI) makes use of a broad-cast communication network for allocating a large-scale set of processors aimed at High Throughput Computing (HTC). A broadcast network example is a Digital TV system whose signal is transmitted to thousands of receivers simultaneously. These receivers are machines with significant processing power available in our houses in increasing quantities and can be used as processing units. However, these potential processors are not completely dedicated, should be voluntarily ceded and may fail (turned off during the use), which makes them highly volatile resources. In other words, there are no guar-antees about the time they remain dedicated to a task. Thus, it is necessary to use mech-anisms able to deal with this volatility and to optimize the collective availability of the-se devices. This work aims at investigating OddCI architecture coordination heuristics, seeking for intelligent ways to allocate or release devices under the coverage of the broadcast network through sending collective messages with the goal of coordinating the allocated processors amount. In order to meet the established Service-Level Agree-ment (SLA), factors as resources population, volatility and number of simultaneous re-quests are considered among others. The efficiency of the coordination heuristics has been studied in a Digital TV network environment, through experiments with simula-tion. As results, we identified the most significant factors, the resulting effects and re-strictions in the evaluated scenarios. In the scenario where the backend infrastructure has limited capacity, the main factors were the size of the application image used by the instances and the number of concurrent instances, meeting the most extreme case of 4MB applications, 80 concurrent instances and volatilities of 40% with 50% of the re-quired parallelism. In the scenario where the minimum makespan was the goal, the main factors were the volatility, the population (and the devices availability), and meeting 50 concurrent instances with reduction of only 15% of the required average flow in the case of the smaller population with higher volatilities of up to 40%, showing how far the results have been favorable in each scenario.