A Statistical Analysis of Network Parameters for the Self-management of Lambda-Connections

Abstract

Abstract. Network monitoring plays an important role in network management. Through the analysis of network parameters (e.g., flow throughput), managers can observe network behavior and make decisions based on them. The choice of network parameters although should be relevant for each specific objective. In this paper, we focus on the analysis of network parameters that are relevant for our self-management of lambda-connections proposal. This proposal consists of an automatic decision process to offload large IP flows onto lambda-connections. This paper aims at statistically analyzing a list of potential network parameters as relevant estimators for flow volume. The main contribution of this work is the introduction of a statistical methodology to validate that some few network parameters can be considered as good predictors for flow volume. These predictors are therefore of great interest to be used in our automatic decision process.

Self-management of hybrid optical and packet switching networks

“…Our goal here is to observe which set of these parameters are relevant estimators to predict flow size. For that, we introduce a statistical methodology to validate our goal [55]. The following subsections present how network data was collected to our experiment as well as how our statistical methodology is comprised.…”

Self-management of Hybrid Networks – Hidden Costs Due to TCP Performance Problems

“…Since these decisions can be slow and error prone, we envision a move towards self-management approaches, which automatically move IP flows to lightpaths on the fly [6][7][8]. Network operators would only be required to initially configure the self-management process with decision policies, such as setting thresholds and priorities.…”

Anticipation of Traffic Demands to Guarantee QoS in IP/Optical Networks

“…In [17], the authors characterize big flows by observing their size, duration, throughput (rate), and burstiness, as well as by examining their correlation considering the traditional 5-tuple flow definition: group packets with the same source and destination IP addresses, same transport protocol, and same transport protocol source and destination port numbers. Alternatively, [1] proposes a novel set of parameters to predict the traffic volume generated by elephant flows, which are summarized in Table 1. Note that volume prediction is employed in the self-management approach to reduce the cost of high-volume flows staying at the IP level, in terms of consumption of network resources, by detecting them in a timely manner so that they can be transferred to the optical layer.…”

“…This way, data flows can traverse IP/optical networks through either an optical path or a chain of routing decisions at the IP layer, where IP routers are interconnected with fiber links [1]. This is especially interesting for large flows for two reasons.…”

“…These flows are called "elephants" and have the following characteristics: they are persistent in time and few in number but account for most of the Internet traffic volume [3]. This is, though, a very complex decision to be made by human operators, because it involves analyzing several network parameters for all IP flows traversing the network and then selecting the flows in a timely manner to balance the network properly [1]. In parallel to traffic-driven lightpath provisioning, much effort is being put into investigating the best approaches to establishing lightpaths and then monitoring them once they are in service so that QoS is assured with minimum cost and sufficient efficiency.…”