Abstract
Distributed computing may be looked at from many points of view. Task scheduling is the viewpoint, where a distributed application can be described as a Directed Acyclic Graph and every node of the graph is executed independently. There are, however, data dependencies and the nodes have to be executed in a specified order. Hence the parallelism of the execution is limited. The scheduling problem is difficult and therefore heuristics are used. However, many inaccuracies are caused by the model used for the system, in which the heuristics are being tested. In this paper we present a tool for simulating the execution of the distributed application on a “real” computer network, and try to tell how the executionis influenced compared to the model.
Highlights
Heterogeneous computation platforms have become very popular in the past decade
In this paper we have presented the problem of task scheduling
Since the problem is difficult, heuristics are used, and great progress has been achieved in this area
Summary
Heterogeneous computation platforms have become very popular in the past decade. They are cheap and easy to construct and offer good computation power. Task scheduling is one of many approaches used for distributed algorithms. The application that is to be scheduled can be described as a Directed Acyclic Graph (DAG), i.e. AM = (V, E, B, C), where:. The application has to be scheduled on a heterogeneous computation system (CS) which can be described as a general graph, but there is one very important restriction. The graph represents a connection structure, and even though there may be no direct connection of two computation nodes there must be an edge between all of the nodes that are able to communicate. This restriction leads to the observation that the CS is always a complete graph.
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