Abstract The addition of redundancy to data structures can be used to improve the ability of a software system to detect and correct errors, and to continue to operate according to its specifications. A case study is presented which indicates how such redundancy can be deployed and exploited at reasonable cost to improve software fault tolerance. Experimental results are reported for the small data base system considered.

The increasing cost of computer system failure has stimulated interest in improving software reliability. One way to do this is by adding redundant structural data to data structures. Such redundancy can be used to detect and correct (structural) errors in instances of a data structure. The intuitive approach of this paper, which makes heavy use of examples, is complemented by the more formal development of the companion paper, "Redundancy in Data Structures: Some Theoretical Results."

A companion paper, "Redundancy in Data Structures: Improving Software Fault Tolerance," provides an infonnal introduction to robust data structures. Here, we present the underlying theory for them, and use it to discuss the synthesis and cost effectiveness of robust data structures.

A periodic network is a queuing network whose steady-state behavior is not constant in time, but repeats itself in a cycle. This behavior may be caused by the introduction of periodic servers, e.g., paging drums. The model presented is a generalization of some other models of queuing networks, and provides a more general definition of steady-state behavior. A theoretical solution is presented. Examples of theoretical and approximate solutions are presented for a well-known queuing network model of a computer system.

In order to help satisfy an apparent need for tools for monitoring the activities of a computer network, a system of special hardware and software, called a Computer Network Monitoring System (CNMS) is being implemented in the University of Waterloo Computer Communications Networks Group (CCNG). The paper discusses the motivation and derivation of the CNMS, then provides functional descriptions of most of the major hardware and software components, illustrates use of the CNMS, and lists experiments and applications. The CNMS consists of: 1) a set of hybrid monitors, each of which is controlled by a locally or remotely located computer; 2) monitor control and data analysis software; 3) a network traffic generator; 4) measurement software in each computer monitored. Each computer to be monitored is attached to a monitor. Telephone lines, possibly different from those of the network, connect the monitors to the controlling computer.