Peter A. Keiller

Performance Analysis of Homing Pigeon based Delay Tolerant Networks

This paper presents and analyzes a new type of delay tolerant network where each node owns a dedicated messenger (called a pigeon). The only form of inter-node communication is for a pigeon to periodically carry a batch of messages originated at the home node, deliver them to the corresponding destination nodes and return home. Clearly, given message expiration times, some messages may not reach their destinations by the deadline. Through theoretical analysis and simulations, we study the relationship between the arrival rate (at the home node), batch size, expiration time and delivery ratio (the percentage of messages reaching destinations before they expire) of messages. The simplistic assumptions we make render the problem tractable, and help us gather experience in this topic.

Comparative analysis of Bayesian and classical approaches for software reliability measurement

Software failure remains an important cause of reported system outage. Yet, developing reliable software is still not well understood by the programmer, the Software Engineer and the Program manager. Software reliability growth models (SRGMs) provide a framework to analyze software failures by using past failure data to predict the reliability of the software. Most models that have been used have limitations in predicting accurately. There is a need to conduct research aimed at improving the performance of these models. To accurately predict reliability, the models parameters should be estimated in such a way that the mathematical function of the model fits with the failure data. While the majority of previous software reliability studies have used classical methods to estimate models parameters, a few other studies have used a Bayesian approach. Bayesian approaches allow the incorporation of prior information into models and they have been claimed to be more successful than classical approaches in certain situations. Our research goal is to investigate if the use of Bayesian methods improves the predictability of SRGMs by conducting a direct comparative analysis of Bayesian and classical approaches for software reliability assessment.

Addressing the performance of two software reliability modeling methods

The problem of predicting the number of failures of a piece of software during the system test phase is addressed. Using software reliability growth models at different periods of usage of the software, predictions are made of the total number of failures one would expect at the end of the system test. Two different methods for using the models are considered: straightforward use of individual models (simple models), and dynamic selection among models based on the quality-of-prediction criteria (super models). Performance is judged by the average of the relative error of the predicted number of failures by the end of the testing period relative to the number of failures eventually observed during the interval. Three simple models and three super models are evaluated based on their performance on forty one data sets. The two methods are also investigated using smoothing techniques utilizing the Laplace trend test.

A service model for improving healthcare delivery in rural developing communities

The infrastructure needed in developing countries, especially in rural areas, often makes providing state-of-the-art healthcare cost prohibitive. We describe a highly asynchronous service model for healthcare delivery that is inexpensive, at least compared to the usual implementation of telemedicine, and involves technical service, public health, training and political aspects. The model is incremental, and will provide improved service even though at the initial stages it is not likely to be fully implemented. Our proposed service model provides relatively comprehensive, but not universal, healthcare coverage, and this paper discusses the service model’s economic and technical limitations.

A Heuristic Algorithm for Finding the Longest Pathways in a Biochemical Network

Finding the longest cycle is a novel concept in biochemical feedback loop analysis in systems biology. Biochemical networks are often represented as directed graphs in which vertices represent chemical compounds and edges represent chemical reactions between compounds. Therefore, a biochemical longest feedback loop can be formulated as the longest cycle in a directed graph. Because finding the longest cycle in a directed graph is NP-hard, in this paper, we proposed an intelligent heuristic algorithm to find the longest cycle in a directed graph. We tested the algorithm on both randomly generated complex networks and real biochemical networks extracted from the KEGG database. The results showed that our algorithm is able to find more than 70% of the real longest cycles in the 200 randomly generated complex networks and also can find the feedback loop in the longest pathway. Compared with the traditional breadth first search pathway finding algorithm, the search efficiency of the proposed algorithm has been improved dramatically. Among the feedbacks found from the KEGG database using the proposed algorithm, the longest feedback includes 8 compounds, 9 reactions, and 6 pathways across different modules.