Data assignment in fault tolerant uploads for digital government applications: a genetic algorithms approach
Yan Chun Yang, Leslie Cheung, Leana Golubchik · International Conference on Digital Government Research · 2005
This paper investigates a data assignment problem in a fault tolerance protocol of Bistro, a wide area upload framework. Uploads correspond to an important class of applications, whose examples include a large number of digital government applications. Specifically, government at all levels is a major collector and provider of data, and there are clear benefits to disseminating and collecting data over the Internet, given its existing large-scale infrastructure and wide-spread reach in commercial, private, and government domains. In this project we focus on the collection of data over the Internet. By data collection, we mean applications such as Internal Revenue Service (IRS) applications with respect to electronic submission of income tax forms.In Bistro, clients upload their data to intermediaries, known as bistros, to reduce the traffic to the destination around a deadline. The destination server then computes a schedule for pulling the data from bistros after the deadline. In the Bistro framework, bistros can be unavailable or malicious. Thus, a fault tolerance protocol is a vital and fundamental component of the Bistro framework. In this paper, we are particularly interested in a data assignment problem in the Bistro fault tolerance protocol. We formulate this problem into a non-linear optimization problem and develop a genetic algorithm heuristic as an approximation. We evaluate our approach using simulations and compare the results of our heuristic with other simple heuristics as well as an optimal solution obtained from a brute-force approach.