Inferring Latent Traffic Demand Offered to an Overloaded Link with Modeling QoS-Degradation Effect

Abstract

In this paper, we propose a CTRIL (Common Trend and Regression with Independent Loss) model to infer latent traffic demand in overloaded links as well as how much it is reduced due to QoS (Quality of Service) degradation. To appropriately provision link bandwidth for such overloaded links, we need to infer how much traffic will increase without QoS degradation. Because original latent traffic demand cannot be observed, we propose a method that compares the other traffic time series of an underloaded link, and by assuming that the latent traffic demands in both overloaded and underloaded are common, and actualized traffic demand in the overloaded link is decreased from common pattern due to the effect of QoS degradation. To realize the method, we developed a CTRIL model on the basis of a state-space model where observed traffic is generated from a latent trend but is decreased by the QoS degradation. By applying the CTRIL model to actual HTTP (Hypertext transfer protocol) traffic and QoS time series data, we reveal that 1% packet loss decreases traffic demand by 12.3%, and the estimated latent traffic demand is larger than the observed one by 23.0%.

Modeling Upper Layer Reaction to QoS Degradation as a Congestion Avoidance Mechanism

“…Then in Sect. 3, we discuss the possibility of traffic reduction as an upper-layer reaction to QoS degradation and a method of inferring latent traffic demand, which was shown in our previous paper [1]. In Sect.…”

“…We adopted a state space model to represent the above assumption [1]. Figure 2 shows the overall structure of the model, which is described hereafter.…”

“…We now discuss our previous experimental results [1]. We used 48-hour-long data measured from August 2nd to 3rd, 2013 at congested and uncongested links.…”

“…Though these reactions are not intended to reduce network traffic load, they reduce it as a result and mitigate congestion. We previously introduced the possibility that with these upper-layer reactions, traffic demand may be latent when congestion occurs and QoS is degraded [1]. We also proposed a method of inferring the latent traffic demand by developing a model for the degree of traffic reduction as a function of QoS degradation, which is called the Common Trend and Regression with Independent Loss (CTRIL) model.…”

“…During congestion, users and/or applications tend to reduce their traffic demand in reaction to quality of service (QoS) degradation to avoid negative service experience. We previously estimated that 2% packet loss results in 23% traffic reduction because of this upper-layer reaction [1]. We view this reduction as an upper-layer congestion-avoidance mechanism and construct a closed-loop model of this mechanism, which we call the Upper-Layer Closed-Loop (ULCL) model.…”