Clouds are more and more becoming a credible alternative to parallel
dedicated resources. The pay-per-use pricing policy however highlights
the real cost of computing applications. This new criterion, the cost,
must then be assessed when scheduling an application in addition to
more traditional ones as the completion time or the execution
flow. In this paper, we tackle the problem of optimizing the cost of
renting computing instances to execute an application on the cloud while
maintaining a desired performance (throughput). The target application
is a stream application based on a DAG pattern, i.e., composed of
several tasks with dependencies, and instances of the same execution
task graph are continuously executed on the instances. We provide some
theoretical results on the problem of optimizing the renting cost for a given
throughput then propose some heuristics to solve the more complex
parts of the problem, and we compare them to optimal solutions found by
linear programming.
| Subject : | : | oral |
| Topics | : | DAG scheduling |
| PDF version | : |  PDF version |
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