Joanna Berlinska

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In this paper we analyze MapReduce distributed computations as divisible load scheduling problem. The two operations of mapping and reducing can be understood as two divisible applications with precedence constraints. A divisible load model is proposed, and schedule dominance properties are analyzed. We investigate dominant schedule structures for MapReduce(More)
In this paper scheduling communications in data gathering networks is analyzed. We study collecting information by a set of sensors, each of which stores the data in its memory buffer and then passes them to a base station. The network lifetime ends as soon as the first node is out of memory. We use a divisible load model to propose a communication(More)
In this paper we study divisible load scheduling in systems with limited memory. Divisible loads are parallel computations which can be divided into independent parts of arbitrary sizes and processed in parallel on remote computers. The problem consists in distributing the load taking into account communication time, computation time, and limited memory(More)
We analyze scheduling multilayer divisible computations. Multilayer computations consist of a chain of parallel applications, such that one application produces input for the next one. A simple form of multilayer computations are MapReduce parallel applications. The operations of mapping and reducing are two divisible applications with precedence(More)
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