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Advanced SLA Compliance Monitoring Methodology

This research introduces an active measurement methodology to accurately measure path characteristics crucial for SLA compliance, including mean delay, delay distribution, loss rate, and delay variation. It also presents a new approach to infer lower bounds on delay distribution based on delay measures of selected paths.

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Advanced SLA Compliance Monitoring Methodology

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  1. Accurate And Efficient SLA Compliance Monitoring J. Sommers, P. Barford, N. Duffield, A. Ron Presented by Mishari Almishari

  2. Main Contributions • Introduction of a New Active Measurement methodology to measure path characteristics Necessary for SLA • Mean Delay • Delay Distribution • Loss Rate • Delay Variation • New method to infer lower bounds on delay distribution given delay measures of some paths

  3. Active Measurement • Delay • Use Simpson’s method: Aj, bj, Cj • Low error rate • Apply Simpson’s method to a discrete-time probe • Geometric distribution (with pdelay), draw k • Slots I, I +2(k+1), I + (K + 1) • Average estimates

  4. Active Measurement • Distribution Estimation • Sample X1,…Xn • P (Xk <= X) = G(n, F(X), k) • Lower and upper bounds • Delay Variation • DV matrix, Rij/ Sij • Find Eigen Vector, and L1 of E – E’ represents Variance • Loss Rate

  5. Inferring Lower Bounds • Routing Matrix • Scalar additive Model: Xe, Yr • The spanning Set • Using Measures of the spanning paths, we can conclude lower bounds on the delay distribution of other paths • Boundaries..

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