Within Ericsson there is a continuous activity of traffic modeling. Traffic modeling is a practice to analyze traffic patterns and determine necessary resources to handle it optimally. This activity focuses on gathering and analyzing live network measurements, implementing and presenting traffic models. One example of concept in packet traffic modeling is transmission object log which is an aggregation of packet data traces from a measured network over a transmission period. These trace logs that are simple list of all transmission objects contain a vast number of data. When the amount of data increases in these logs several problems can occur such as expensive analysis time, costly data storage and even statistical analysis and data processing in software environments run out of memory. On the other hand, sophisticated and costly computing systems are required for analysis and storage of the data. Therefore, monitoring and analyzing these large traces motivate data reduction. The goal of this thesis is to reduce the number of traffic objects in large object trace logs while preserving the statistical characteristics of the original transmission objects. Sampling techniques are wildly used to cope with the issues of large amount of data in network monitoring. First, this thesis aims to assess the impact of two sampling techniques as a reduction method. Second, to analyze traffic characteristics and showing the effects of sampling, some statistical properties of both original and sampled datasets as well as their distribution plots will be discussed. The distortion introduced by sampling as the distance between the distribution of properties for sampled and unsampled traffic is also presented by a statistical metric. One of the issues in sampling technique is the sampling size. In order to estimate the sampling size and reduce the logs to a certain level, the concept of offline marginal utility as a complementary method to sampling is proposed in this report. The thesis also makes some suggestions as further works to reduce the logs by having less impact on the object characteristics.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-87684 |
| Date | January 2012 |
| Creators | Mehrabian, Maryam |
| Publisher | Linköpings universitet, Institutionen för datavetenskap, Linköpings universitet, Tekniska högskolan |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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