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NETWORK PERFORMANCE MANAGEMENT USING APPLICATION-CENTRIC KEY PERFORMANCE INDICATORS
Title
NETWORK PERFORMANCE MANAGEMENT USING APPLICATION-CENTRIC KEY PERFORMANCE INDICATORS.
Creator
McGill, Susan, Shumaker, Randall, University of Central Florida
Abstract / Description
The Internet and intranets are viewed as capable of supplying "Anything, Anywhere, Anytime" and e-commerce, e-government, e-community, and military C4I are now deploying many and varied applications to serve their needs. Network management is currently centralized in operations centers. To assure customer satisfaction with the network performance they typically plan, configure and monitor the network devices to insure an excess of bandwidth, that is overprovision. If this proves uneconomical...
Show moreThe Internet and intranets are viewed as capable of supplying "Anything, Anywhere, Anytime" and e-commerce, e-government, e-community, and military C4I are now deploying many and varied applications to serve their needs. Network management is currently centralized in operations centers. To assure customer satisfaction with the network performance they typically plan, configure and monitor the network devices to insure an excess of bandwidth, that is overprovision. If this proves uneconomical or if complex and poorly understood interactions of equipment, protocols and application traffic degrade performance creating customer dissatisfaction, another more application-centric, way of managing the network will be needed. This research investigates a new qualitative class of network performance measures derived from the current quantitative metrics known as quality of service (QOS) parameters. The proposed class of qualitative indicators focuses on utilizing current network performance measures (QOS values) to derive abstract quality of experience (QOE) indicators by application class. These measures may provide a more user or application-centric means of assessing network performance even when some individual QOS parameters approach or exceed specified levels. The mathematics of functional analysis suggests treating QOS performance values as a vector, and, by mapping the degradation of the application performance to a characteristic lp-norm curve, a qualitative QOE value (good/poor) can be calculated for each application class. A similar procedure could calculate a QOE node value (satisfactory/unsatisfactory) to represent the service level of the switch or router for the current mix of application traffic. To demonstrate the utility of this approach a discrete event simulation (DES) test-bed, in the OPNET telecommunications simulation environment, was created modeling the topology and traffic of three semi-autonomous networks connected by a backbone. Scenarios, designed to degrade performance by under-provisioning links or nodes, are run to evaluate QOE for an access network. The application classes and traffic load are held constant. Future research would include refinement of the mathematics, many additional simulations and scenarios varying other independent variables. Finally collaboration with researchers in areas as diverse as human computer interaction (HCI), software engineering, teletraffic engineering, and network management will enhance the concepts modeled.
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Date Issued
2007
Identifier
CFE0001818, ucf:47371
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001818
OPTIMIZATION OF NETWORK PARAMETERS AND SEMI-SUPERVISION IN GAUSSIAN ART ARCHITECTURES
Title
OPTIMIZATION OF NETWORK PARAMETERS AND SEMI-SUPERVISION IN GAUSSIAN ART ARCHITECTURES.
Creator
Chalasani, Roopa, Georgiopoulos, Michael, University of Central Florida
Abstract / Description
In this thesis we extensively experiment with two ART (adaptive resonance theory) architectures called Gaussian ARTMAP (GAM) and Distributed Gaussian ARTMAP (dGAM). Both of these classifiers have been successfully used in the past on a variety of applications. One of our contributions in this thesis is extensively experiments with the GAM and dGAM network parameters and appropriately identifying ranges for these parameters for which these architectures attain good performance (good...
Show moreIn this thesis we extensively experiment with two ART (adaptive resonance theory) architectures called Gaussian ARTMAP (GAM) and Distributed Gaussian ARTMAP (dGAM). Both of these classifiers have been successfully used in the past on a variety of applications. One of our contributions in this thesis is extensively experiments with the GAM and dGAM network parameters and appropriately identifying ranges for these parameters for which these architectures attain good performance (good classification performance and small network size). Furthermore, we have implemented novel modifications of these architectures, called semi-supervised GAM and dGAM architectures. Semi-supervision is a concept that has been used effectively before with the FAM and EAM architectures and in this thesis we are answering the question of whether semi-supervision has the same beneficial effect on the GAM architectures too. Finally, we compared the performance of GAM, dGAM, EAM, FAM and their semi-supervised versions on a number of datasets (simulated and real datasets). These experiments allowed us to draw appropriate conclusions regarding the comparative performance of these architectures.
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Date Issued
2005
Identifier
CFE0000474, ucf:46373
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000474
HIGH PERFORMANCE DATA MINING TECHNIQUES FOR INTRUSION DETECTION
Title
HIGH PERFORMANCE DATA MINING TECHNIQUES FOR INTRUSION DETECTION.
Creator
Siddiqui, Muazzam Ahmed, Lee, Joohan, University of Central Florida
Abstract / Description
The rapid growth of computers transformed the way in which information and data was stored. With this new paradigm of data access, comes the threat of this information being exposed to unauthorized and unintended users. Many systems have been developed which scrutinize the data for a deviation from the normal behavior of a user or system, or search for a known signature within the data. These systems are termed as Intrusion Detection Systems (IDS). These systems employ different techniques...
Show moreThe rapid growth of computers transformed the way in which information and data was stored. With this new paradigm of data access, comes the threat of this information being exposed to unauthorized and unintended users. Many systems have been developed which scrutinize the data for a deviation from the normal behavior of a user or system, or search for a known signature within the data. These systems are termed as Intrusion Detection Systems (IDS). These systems employ different techniques varying from statistical methods to machine learning algorithms.Intrusion detection systems use audit data generated by operating systems, application softwares or network devices. These sources produce huge amount of datasets with tens of millions of records in them. To analyze this data, data mining is used which is a process to dig useful patterns from a large bulk of information. A major obstacle in the process is that the traditional data mining and learning algorithms are overwhelmed by the bulk volume and complexity of available data. This makes these algorithms impractical for time critical tasks like intrusion detection because of the large execution time.Our approach towards this issue makes use of high performance data mining techniques to expedite the process by exploiting the parallelism in the existing data mining algorithms and the underlying hardware. We will show that how high performance and parallel computing can be used to scale the data mining algorithms to handle large datasets, allowing the data mining component to search a much larger set of patterns and models than traditional computational platforms and algorithms would allow.We develop parallel data mining algorithms by parallelizing existing machine learning techniques using cluster computing. These algorithms include parallel backpropagation and parallel fuzzy ARTMAP neural networks. We evaluate the performances of the developed models in terms of speedup over traditional algorithms, prediction rate and false alarm rate. Our results showed that the traditional backpropagation and fuzzy ARTMAP algorithms can benefit from high performance computing techniques which make them well suited for time critical tasks like intrusion detection.
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Date Issued
2004
Identifier
CFE0000056, ucf:46142
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000056
ORGANIZATIONAL SOCIAL CAPITAL AND PERCEIVED PERFORMANCE OF DRUG LAW ENFORCEMENT DEPARTMENTS: A CASE STUDY IN TURKEY
Title
ORGANIZATIONAL SOCIAL CAPITAL AND PERCEIVED PERFORMANCE OF DRUG LAW ENFORCEMENT DEPARTMENTS: A CASE STUDY IN TURKEY.
Creator
Sahin, Ismail, Wan, Thomas, University of Central Florida
Abstract / Description
Supply reduction efforts by drug law enforcement departments are a significant factor in improving the effectiveness of drug control policies. As with other public organizations, the performance of drug law enforcement departments is one of the most important concerns for policy makers. Therefore, improving the performance of these departments is crucial in order for governments to constrict illegal drug markets and prevent illegal drug distribution. The literature suggests that social...
Show moreSupply reduction efforts by drug law enforcement departments are a significant factor in improving the effectiveness of drug control policies. As with other public organizations, the performance of drug law enforcement departments is one of the most important concerns for policy makers. Therefore, improving the performance of these departments is crucial in order for governments to constrict illegal drug markets and prevent illegal drug distribution. The literature suggests that social capital may have significant implications for policy makers and practitioners in terms of enhancing organizational performance.Social capital has recently been examined at the organizational level. It may contribute to organizational effectiveness by increasing motivation, solving coordination problems, facilitating information flow between individuals and organizations, and developing knowledge within organizations. Because of the nature of the work, drug law enforcement departments or agencies require information sharing, cooperation, and motivation, all possible derivatives of social capital.Using a measurement model of organizational social capital, this study examines relationships among three dimensions of organizational social capital. The influence of social capital on the perceived performance of drug law enforcement departments is investigated using structural equation modeling. Possible correlations among these dimensions or domains of organizational social capital are also empirically tested.Using survey data from 12 city law enforcement departments in Turkey, this study examines three social capital dimensions: (1) the structural dimension, concerning the extent to which officers within a department informally interact with each other; (2) the relational dimension, referring to the normative qualities of relationships among officers, such as trust and reciprocity; and (3) the cognitive dimension, reflected by shared language, shared interpretation, and shared vision.Four research hypotheses were tested and supported by the statistical results. The studyÂÂ's findings indicate that the relational and cognitive social capital variables have a direct and positive relationship with the perceived performance of drug law enforcement departments. Relational and cognitive social capital, as latent constructs, were shown to have a strong relationship with organizational performance. Structural social capital, however, does not have a direct relationship with but may indirectly influence performance. This result indicates that structural social capital may influence organizational performance only indirectly, through its joint influence with two other social capital domains. On the other hand, strong and positive intercorrelations were found among the three dimensions. The results suggest that social capital is essential for drug law enforcement departments because police officers who know, understand, and trust each other are more likely to work together efficiently and effectively towards achieving organizational performance.According to the findings, informal structures shaped by informal relations among officers within the departments may also be an important factor for organizational performance. Investing in the development of social interactions and networks and building trust within organizations is important in order for administrators to improve organizational performance. The results of this conceptually grounded and empirical study suggest that drug law enforcement departments or agencies should pay close attention to promoting social capital among officers in order to fight effectively against drug trafficking.
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Date Issued
2010
Identifier
CFE0003182, ucf:48601
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003182
Network Partitioning in Distributed Agent-Based Models
Title
Network Partitioning in Distributed Agent-Based Models.
Creator
Petkova, Antoniya, Deo, Narsingh, Hughes, Charles, Bassiouni, Mostafa, Shaykhian, Gholam, University of Central Florida
Abstract / Description
Agent-Based Models (ABMs) are an emerging simulation paradigm for modeling complex systems, comprised of autonomous, possibly heterogeneous, interacting agents. The utility of ABMs lies in their ability to represent such complex systems as self-organizing networks of agents. Modeling and understanding the behavior of complex systems usually occurs at large and representative scales, and often obtaining and visualizing of simulation results in real-time is critical.The real-time requirement...
Show moreAgent-Based Models (ABMs) are an emerging simulation paradigm for modeling complex systems, comprised of autonomous, possibly heterogeneous, interacting agents. The utility of ABMs lies in their ability to represent such complex systems as self-organizing networks of agents. Modeling and understanding the behavior of complex systems usually occurs at large and representative scales, and often obtaining and visualizing of simulation results in real-time is critical.The real-time requirement necessitates the use of in-memory computing, as it is dif?cult and challenging to handle the latency and unpredictability of disk accesses. Combining this observation with the scale requirement emphasizes the need to use parallel and distributed computing platforms, such as MPI-enabled CPU clusters. Consequently, the agent population must be "partitioned" across different CPUs in a cluster. Further, the typically high volume of interactions among agents can quickly become a signi?cant bottleneck for real-time or large-scale simulations. The problem is exacerbated if the underlying ABM network is dynamic and the inter-process communication evolves over the course of the simulation. Therefore, it is critical to develop topology-aware partitioning mechanisms to support such large simulations.In this dissertation, we demonstrate that distributed agent-based model simulations bene?t from the use of graph partitioning algorithms that involve a local, neighborhood-based perspective. Such methods do not rely on global accesses to the network and thus are more scalable. In addition, we propose two partitioning schemes that consider the bottom-up individual-centric nature of agent-based modeling. The ?rst technique utilizes label-propagation community detection to partition the dynamic agent network of an ABM. We propose a latency-hiding, seamless integration of community detection in the dynamics of a distributed ABM. To achieve this integration, we exploit the similarity in the process flow patterns of a label-propagation community-detection algorithm and self-organizing ABMs.In the second partitioning scheme, we apply a combination of the Guided Local Search (GLS) and Fast Local Search (FLS) metaheuristics in the context of graph partitioning. The main driving principle of GLS is the dynamic modi?cation of the objective function to escape local optima. The algorithm augments the objective of a local search, thereby transforming the landscape structure and escaping a local optimum. FLS is a local search heuristic algorithm that is aimed at reducing the search space of the main search algorithm. It breaks down the space into sub-neighborhoods such that inactive sub-neighborhoods are removed from the search process. The combination of GLS and FLS allowed us to design a graph partitioning algorithm that is both scalable and sensitive to the inherent modularity of real-world networks.
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Date Issued
2017
Identifier
CFE0006903, ucf:51706
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006903
Development of Traffic Safety Zones and Integrating Macroscopic and Microscopic Safety Data Analytics for Novel Hot Zone Identification
Title
Development of Traffic Safety Zones and Integrating Macroscopic and Microscopic Safety Data Analytics for Novel Hot Zone Identification.
Creator
Lee, JaeYoung, Abdel-Aty, Mohamed, Radwan, Ahmed, Nam, Boo Hyun, Kuo, Pei-Fen, Choi, Keechoo, University of Central Florida
Abstract / Description
Traffic safety has been considered one of the most important issues in the transportation field. With consistent efforts of transportation engineers, Federal, State and local government officials, both fatalities and fatality rates from road traffic crashes in the United States have steadily declined from 2006 to 2011.Nevertheless, fatalities from traffic crashes slightly increased in 2012 (NHTSA, 2013). We lost 33,561 lives from road traffic crashes in the year 2012, and the road traffic...
Show moreTraffic safety has been considered one of the most important issues in the transportation field. With consistent efforts of transportation engineers, Federal, State and local government officials, both fatalities and fatality rates from road traffic crashes in the United States have steadily declined from 2006 to 2011.Nevertheless, fatalities from traffic crashes slightly increased in 2012 (NHTSA, 2013). We lost 33,561 lives from road traffic crashes in the year 2012, and the road traffic crashes are still one of the leading causes of deaths, according to the Centers for Disease Control and Prevention (CDC). In recent years, efforts to incorporate traffic safety into transportation planning has been made, which is termed as transportation safety planning (TSP). The Safe, Affordable, Flexible Efficient, Transportation Equity Act (-) A Legacy for Users (SAFETEA-LU), which is compliant with the United States Code, compels the United States Department of Transportation to consider traffic safety in the long-term transportation planning process. Although considerable macro-level studies have been conducted to facilitate the implementation of TSP, still there are critical limitations in macroscopic safety studies are required to be investigated and remedied. First, TAZ (Traffic Analysis Zone), which is most widely used in travel demand forecasting, has crucial shortcomings for macro-level safety modeling. Moreover, macro-level safety models have accuracy problem. The low prediction power of the model may be caused by crashes that occur near the boundaries of zones, high-level aggregation, and neglecting spatial autocorrelation.In this dissertation, several methodologies are proposed to alleviate these limitations in the macro-level safety research. TSAZ (Traffic Safety Analysis Zone) is developed as a new zonal system for the macroscopic safety analysis and nested structured modeling method is suggested to improve the model performance. Also, a multivariate statistical modeling method for multiple crash types is proposed in this dissertation. Besides, a novel screening methodology for integrating two levels is suggested. The integrated screening method is suggested to overcome shortcomings of zonal-level screening, since the zonal-level screening cannot take specific sites with high risks into consideration. It is expected that the integrated screening approach can provide a comprehensive perspective by balancing two aspects: macroscopic and microscopic approaches.
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Date Issued
2014
Identifier
CFE0005195, ucf:50653
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005195