Traffic Long-range Dependence Research Articles

Long-range dependence of traffic across schedulers with multiple service classes

Long-range dependence (LRD) is a widely verified property of Internet traffic, which severely impacts network performance yielding longer queuing delays. Moreover, LRD has been demonstrated to be almost ubiquitous and hard to remove or control. In this work, we investigated by extensive simulation the effect of schedulers on traffic LRD. We analyzed the output traffic of single schedulers and chains of schedulers merging LRD flows according to various service policies, viz. plain FIFO, strict-priority, earliest-deadline-first and general processor sharing (GPS). First, we noticed that traffic LRD is not affected much by crossing schedulers, for any service policy, when the merged flow has no LRD. Then, we showed that LRD may also spread across flows with different service priorities, with any service policy except balanced GPS, which ensures complete separation between classes. Finally, we experienced the same phenomenon also along chains of schedulers. These findings may explain, in part, why LRD is so widespread in Internet traffic.

Delay-throughput curves for timer-based OBS burstifiers with light load

The optical burst switching (OBS) burstifier delay-throughput curves are analyzed in this paper. The burstifier incorporates a timer-based scheme with minimum burst size, i.e., bursts are subject to padding in light-load scenarios. Precisely, due to this padding effect, the burstifier normalized throughput may not be equal to unity. Conversely, in a high-load scenario, padding will seldom occur. For the interesting light-load scenario, the throughput-delay curves are derived and the obtained results are assessed against those obtained by trace-driven simulation. The influence of long-range dependence and instantaneous variability is analyzed to conclude that there is a threshold timeout value that makes the throughput curves flatten out to unity. This result motivates the introduction of adaptive burstification algorithms, which provide a timeout value that minimizes delay, yet keeps the throughput very close to unity. The dependence of such optimum timeout value with traffic long-range dependence and instantaneous burstiness is discussed. Finally, three different adaptive timeout algorithms are proposed, which trade off complexity versus accuracy.

The extended alternating fractal renewal process for modeling traffic in high-speed communication networks

Extensive studies indicate that traffic in high-speed communication networks exhibits long-range dependence (LRD) and impulsiveness, which pose new challenges in network engineering. While many models have appeared for capturing the traffic LRD, fewer models exist that account for impulsiveness as well as LRD. One of the few existing constructive models for network traffic is the celebrated on/off model or the alternating fractal renewal process (AFRP). However, although the AFRP results in aggregated traffic with LRD, it fails to capture impulsiveness, yielding traffic with Gaussian marginal distribution. A new constructive model, namely the extended AFRP (EAFRP), is proposed here, which overcomes the limitations of the AFRP model. We show that for both single-user and aggregated traffic, it results in impulsiveness and long-range dependence, the LRD being defined here in a generalized sense. We provide queueing analysis of the proposed model, which clearly demonstrates the implications of the impulsiveness in traffic engineering, and validate all theoretical findings based on real traffic data.