The aim of this work is to propose feasible solution for the simulation of the SNMP (Simple Network Management Protocol) protocol in the OPNET Modeler simulation environment. This work describes in detail individual procedures, which were chosen for this simulation . The work also contains an introduction to the OPNET Modeler simulation environment, the SNMP protocol, the DiffServ model and the DiffServ-MIB database. The main part of this work describes the algorithms programmed to model the functions of the SNMP protocol. Source codes of the algorithms programmed are included as well. The work deals especially with the functions for obtaining values from different levels of a multilevel model attributes, packet creation functions with dynamically changing length of user-data field, for the implementation process of a simplified DiffServ MIB database and with the functions for statistic creation.

This work deals with a monitoring application for multicast sessions. The general aim is to develop the application for monitoring multicast sessions from network elements of a laboratory computer network. The first part describes possibilities of monitoring computer networks in terms of monitoring techniques and protocols. The second part contains a topology of the laboratory computer network and several basic commands for setting of the multicast in Cisco IOS. The third part is devoted to the developed application for monitoring the multicast in Java language. The last part describes the testing on an actual multicast network and an example of such testing is stated there.

No description available.

This work outlines ways of getting dynamic state into OMNeT++ model. SNMP as major technology was selected for reaching the goals. Protocols CDP and LLDP are participating in topology detection and in theoretical layer Breadth- first search is used. The output of this tool is file syntactically specied by NED language which is describing computer network.

Indiana University-Purdue University Indianapolis (IUPUI) / Eagle Creek Reservoir (ECR), located in the Midwestern U.S., is a freshwater limnic system plagued by seasonal Harmful Algal Blooms (HABs) which generate water-fouling Geosmin (GSM) and 2-Methylisoborneol (MIB) Taste and Odor (T&O) compounds. Past investigations of T&O event dynamics have identified Actinomycetes as responsible for MIB production and several genera of cyanobacteria for GSM production. During 2018, a temporally and spatially expansive sampling regimen of the reservoir was carried out and a battery of biological, chemical, physical, and hyperspectral experiments performed. The resulting data was analyzed using time series, cross-correlation, lag time, and multivariate analyses as well as machine learning algorithms to pick apart and interrogate any relationships between HABs, T&O events, and environmental parameters. The results show that local weather and watershed conditions exert significant control over the state of the reservoir and the behavior of the algal community. GSM and MIB peaked during early May under well-mixed, cold, and nutrient-rich water column conditions, then declined under summer thermal stratification before making a small resurgence during late season mixing. Bloom die-off and decay was effectively ruled out as a mechanism controlling T&O concentrations, and no links were found between T&O concentrations and algal biomass. Strong evidence was found that GSM/MIB concentrations were a response by bloom microbes to changing nutrient conditions within the reservoir, and it was determined that nutrient fluxes from the watershed 30-40 days prior to peak T&O concentrations are likely instrumental in the development of the slow- ix growing microbes characteristic of the reservoir. Attempts were made to assess spatial and temporal variability but no significant spatial differences were identified; differences between sampling sites were far smaller than differences between different sampling dates. The findings here add to the growing body of literature showing T&O and HAB dynamics are more closely linked to the relative abundance and speciation of nutrients than other parameters. Additionally, these findings carry important implications for the management of ECR and other similar freshwater reservoirs while highlighting the importance of reducing watershed eutrophication.

Indiana University-Purdue University Indianapolis (IUPUI) / The transport of excess nutrients, sediment, and other contaminants to surface waters has been shown to cause a number of environmental and human health concerns. An understanding of the export pathways that these contaminants follow to surrounding water bodies is crucial to the anticipation and management of peak concentration events. Several studies have demonstrated that the majority of annual contaminant loading in the Midwest occurs during periods of elevated discharge. However, many studies use a limited number of sampling points to determine concentration patterns, loadings, and fluxes which decreases accuracy. Through high-resolution storm sampling conducted in a 2945 km2 (1137 mi2) area of central Indiana’s Upper White River Watershed, this research has documented the complex concentration signals and fluxes associated with a suite of cations, nutrients, and contaminants and isolated their primary transport pathways. Additionally, by comparing the results of similar studies conducted on smaller areas within this watershed, differences in concentration patterns and fluxes, as they relate to drainage area, have also been documented. Similar to the results of previous studies, NO3- concentrations lacked a well-defined relationship relative to discharge and was attributed to primarily subsurface contribution. DOC was exported along a shallow, lateral subsurface pathway, TP and TSS via overland flow, and TKN through a combination of both. Near or in-channel scouring of sediment increased DOC, TKN, TP, and TSS concentrations during Storm 2. Atrazine export was attributed to a combination of overland and subsurface pathways. 2-MIB and geosmin derived from different sources and pathways despite being produced by similar organisms. 2-MIB concentration patterns were characterized by dilution of an in-stream source during Storm 1 and potential sediment export during Storm 2 while in-stream concentrations or a sediment source of geosmin was rapidly exhausted during Storm 1. Many of the concentration patterns were subject to an exaggerated averaging effect due to the mixing of several larger watersheds, especially during Storm 1. This research illustrates the need for high-frequency sampling to accurately quantify contaminant loads for total maximum daily load (TMDL) values, developing best management practices (BMPs), and confronting the challenges associated with modeling increasingly larger-scale watersheds.