Network calculus for parallel processing
In this note, we present preliminary results on the use of "network calculus" for parallel processing systems, specifically MapReduce.
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arXiv subjects
J. Liebeherr
Publications and source records attributed to J. Liebeherr.
A Calculus for End-to-end Statistical Service Guarantees
The deterministic network calculus offers an elegant framework for determining delays and backlog in a network with deterministic service guarantees to individual traffic flows. This paper addresses the problem of extending the network calculus to a probabilistic framework with statistical service guarantees. Here, the key difficulty relates to expressing, in a statistical setting, an end-to-end (network) service curve as a concatenation of per-node service curves. The notion of an effective service curve is developed as a probabilistic bound on the service received by an individual flow. It is shown that per-node effective service curves can be concatenated to yield a network effective service curve.
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