A Calculus for End-to-end Statistical Service Guarantees

TL;DR

This paper extends the deterministic network calculus to a probabilistic framework, enabling end-to-end statistical service guarantees by developing effective service curves and their concatenation across network nodes.

Contribution

It introduces the concept of effective service curves for probabilistic bounds and demonstrates their concatenation to achieve end-to-end statistical guarantees.

Findings

Effective service curves provide probabilistic bounds on flow service.

Concatenation of per-node effective service curves yields network-wide guarantees.

The framework extends deterministic calculus to probabilistic network analysis.

Abstract

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.