Fault tolerant methods using dynamically reconfigurable devices have been studied to overcome wear-out failures. However, quantitative comparisons have not been sufficiently assessed on device lifetime enhancement with these methods, whereas they have mainly been evaluated individually from various viewpoints such as additional hardware overheads, performance, and downtime for fault recovery. This paper presents quantitative lifetime evaluations performed by simulating the fault-avoidance procedures of five representative methods under the same conditions in wear-out scenarios, applications, and device architecture. The simulation results indicated that improvements of up to 70% mean-time-to-failure (MTTF) in comparison with ideal fault avoidance could be achieved by using methods of fault avoidance with ‘row direction shift’ and ‘dynamic partial reconfiguration’. ‘Column shift’, on the other hand, attained a high degree of stability with moderate improvements in MTTF. The experimental results also revealed that spare basic elements (BEs) should be prevented from aging so that improvements in MTTF would not be adversely affected. Moreover, we found that the selection of initial mappings guided by wire utilization could increase the lifetimes of partial reconfiguration based fault avoidance.

When P2P systems are used for data sensitive systems, the data availability has become an important issue. The availability-based replication using individual node availability is the most popular method keeping high data availability efficiently. However, since the individual node availability is derived by the individual lifetime information of each node, the availability-based replication may select useless replicas. In this paper, we explore the relative MTTF (Mean Time To Failure)-based incentive scheme for the more efficient availability-based replication. The relative MTTF is used to classify the guaranteed replicas which can get the incentive node availability, and these replicas help reduce the data traffic and the number of replicas without losing the target data availability. Results from trace-driven simulations show that the replication using our relative MTTF-based incentive scheme achieves the same target data availability with 41% less data traffic and 24% less replicas.

In this paper we investigate the reliability of general type shared protection systems i.e. M for N (M:N) that can typically be applied to various telecommunication network devices. We focus on the reliability that is perceived by an end user of one of N units. We assume that any failed unit is instantly replaced by one of the M units (if available). We describe the effectiveness of such a protection system in a quantitative manner. The mathematical analysis gives the closed-form solution of the availability, the recursive computing algorithm of the MTTFF (Mean Time to First Failure) and the MTTF (Mean Time to Failure) perceived by an arbitrary end user. We also show that, under a certain condition, the probability distribution of TTFF (Time to First Failure) can be approximated by a simple exponential distribution. The analysis provides useful information for the analysis and the design of not only the telecommunication network devices but also other general shared protection systems that are subject to service level agreements (SLA) involving user-perceived reliability measures.

This paper presents two man-machine reliability models. A system consists of one machine unit, one operator, and one event detecting monitor. The machine unit has three states, normal, abnormal, and failed. The event detecting monitor may fail in two ways. If a machine unit becomes abnormal, the event detecting monitor sends a signal, and the operator takes necessary actions. If the operator fails in the action in the cause of human error, the machine unit goes down. The condition of the operator is classified into two types, good and bad. The time to repair, and the human error rate both depend on the condition of the operator. The MTTF is obtained by using a Markov model and numerical computation. Furthermore, the optimal operating period which minimizes the overall cost is decided by using computer methods. Some numerical examples are shown.