2 research outputs found
Sharp Bounds in Stochastic Network Calculus
The practicality of the stochastic network calculus (SNC) is often questioned
on grounds of potential looseness of its performance bounds. In this paper it
is uncovered that for bursty arrival processes (specifically Markov-Modulated
On-Off (MMOO)), whose amenability to \textit{per-flow} analysis is typically
proclaimed as a highlight of SNC, the bounds can unfortunately indeed be very
loose (e.g., by several orders of magnitude off). In response to this uncovered
weakness of SNC, the (Standard) per-flow bounds are herein improved by deriving
a general sample-path bound, using martingale based techniques, which
accommodates FIFO, SP, EDF, and GPS scheduling. The obtained (Martingale)
bounds gain an exponential decay factor of in
the number of flows . Moreover, numerical comparisons against simulations
show that the Martingale bounds are remarkably accurate for FIFO, SP, and EDF
scheduling; for GPS scheduling, although the Martingale bounds substantially
improve the Standard bounds, they are numerically loose, demanding for
improvements in the core SNC analysis of GPS