Cynthia S. Hood

Proactive network fault detection

The increasing role of communication networks in todays society results in a demand for higher levels of network availability and reliability. At the same time, fault management is becoming more difficult due to the dynamic nature and heterogeneity of networks. We propose an intelligent monitoring system using adaptive statistical techniques. The system continually learns the normal behavior of the network and detects deviations from the norm. Within the monitoring system, the measurements are segmented, and features extracted from the segments are used to describe the normal behavior of the measurement variables. This information is combined in the structure of a Bayesian network. The proposed system is thereby able to detect unknown or unseen faults. Experimental results on real network data demonstrate that the proposed system can detect abnormal behavior before a fault actually occurs.

Spectral Occupancy and Interference Studies in support of Cognitive Radio Technology Deployment

This paper describes the high value of cognitive radio technology and characterizes the opportunity space in four distinct classes. A Chicago-based spectrum occupancy study illustrates the opportunity showing that 82.6% of the spectral capacity is unused. A set of spectral signatures is presented for common devices in the unlicensed frequency band with the view that this technique can be widely deployed across the spectrum. The limitations of current network simulation tools in an interference environment are identified. Finally, the paper briefly discusses several of the non-technology related issues that impact the deployment of cognitive radio techniques.

Reliable file transfer in Grid environments

Grid-based computing environments are becoming increasingly popular for scientific computing. One of the key issues for scientific computing is the efficient transfer of large amounts of data across the Grid. In this paper we present a reliable file transfer (RFT) service that significantly improves the efficiency of large-scale file transfer. RFT can detect a variety of failures and restart the file transfer from the point of failure. It also has capabilities for improving transfer performance through TCP tuning.

Intelligent agents for proactive fault detection

As the Internet becomes a critical component of our society, a key challenge is to maintain network availability and reliability. Intelligent processing agents that reside at network nodes use an adaptive learning method to detect abnormal network behavior before a fault actually occurs. In a test at Rensselaer Polytechnic Institute, an agent on a router detected a file server failure 12 minutes before it occurred.

Exploring the relationship between parallel application run-time and network performance in clusters

Highly variable parallel application execution time is a persistent issue in cluster computing environments, and can be particularly acute in systems composed of networks of workstations (NOWs). We are looking at this issue in terms of consistency. In particular, we are focusing on network performance. Before we can use techniques from fault management to attain consistency, this paper presents our preliminary analysis of run-time variability from logs and experiments, exposing important issues related to systemic inconsistency in NOW clusters. The characterization of application sensitivity can be used to set network performance goals, thereby defining operational requirements. Network performance depends on the virtual topology imposed by the schedulers allocation of nodes and the communication patterns of the set of running applications. Therefore it is important to look at both the network and the clusters centralized node mapper (scheduler) as critical subsystems.

Teaching programming and language concepts using LEGOs

In this paper we describe an innovative method for using LEGO® bricks to teach programming and other computing concepts. LEGO® bricks are used to express a special purpose language to build creations out of LEGOs®. Using this language, students can execute and create programs. Both fundamental and more advanced concepts can be taught. The use of LEGOs® increases the tactile and kinesthetic aspects of the learning experience and helps to make abstract concepts more concrete. We have used this method to teach a variety of concepts to college freshmen, K-12 teachers, and K-12 students.

Probabilistic network fault detection

To improve network management in todays high-speed communication networks, we propose an intelligent system using adaptive learning machines. The system learns the normal behavior of the network. Deviations from the norm are detected and the information is combined in the probabilistic framework of a Bayesian network. The proposed system is thereby able to detect unknown or unseen faults. As demonstrated on real network data, this method can detect abnormal behavior before a fault actually occurs, giving the network management system (human or automated) the ability to avoid a potentially serious problem.

Automated proactive anomaly detection

To address the increasing complexities of fault management, we propose an automated, proactive monitoring system using adaptive statistical techniques. Requiring only a minimal amount of network specific information a priori, the system continually collects data, uses the data to learn the normal behavior of the network, and detects deviations from the norm. The proposed system is thereby able to detect unknown or unseen faults. Experimental results on real network data demonstrate that the proposed system can detect abnormal behavior before a fault actually occurs.

Integrated networks that overflow speech and data between component networks

As cellular networks diversify by expanding the number of services, cell sizes, and generations of technologies supported, it becomes possible to overflow dual-mode terminals using vertical handovers to other cellular networks or ‘component networks’. This study varies call placement algorithms, by defining, modeling, and evaluating Overflow and Return policies.

Network performance variability in NOW clusters

Performance management of clusters and grids poses many challenges. Sharing large distributed sets of resources can provide efficiencies, but it also introduces complexity in terms of providing and maintaining adequate performance. Current application requirements focus on the amount of resources needed without explicitly characterizing the performance required from those resources. In clusters and grids, inconsistent or highly variable application run-time is an indication of systemic inconsistency, with ramifications for those running the application and those managing the resources. We are focusing on the contribution of the interconnection network to application run-time variability. This work presents experimental results characterizing parallel application run-time sensitivity to communication performance variability using an application communication emulator (ACE).