A measurement-based model for workload dependence of CPU errors
IEEE Transactions on Computers - The MIT Press scientific computation series
Measurement-Based Analysis of Error Latency
IEEE Transactions on Computers
The Reliability of Single-Error Protected Computer Memories
IEEE Transactions on Computers
Cache Operations by MRU Change
IEEE Transactions on Computers
Influence of Workload on Error Recovery in Random Access Memories
IEEE Transactions on Computers - Fault-Tolerant Computing
An Experimental Study of Memory Fault Latency
IEEE Transactions on Computers
IEEE Transactions on Computers
Fault Injection for Dependability Validation: A Methodology and Some Applications
IEEE Transactions on Software Engineering
Computer
A Simulation-Based Study of a Triple Modular Redundant System Using DEFEND
Proceedings of the 5th International GI/ITG/GMA Conference on Fault-Tolerant Computing Systems, Tests, Diagnosis, Fault Treatment
From the fractal dimension of the intermiss gaps to the cache-miss ratio
IBM Journal of Research and Development - Q-Coder adaptive binary arithmetic coder
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Scoring and thresholding for availability
IBM Systems Journal
| Hi-index | 14.98 |
Fault observability based on the behavior of memory references is studied. Traditional studies view memory as one monolithic entity that must completely work to be considered reliable. The usage patterns of a particular program's memory are emphasized here. This paper develops a new model for the successful execution of a program taking into account the usage of the data by extending a cache memory performance model. Three variations, based on well known allocation schemes, are presented (i.e., whether the program's storage is preallocated, dynamically allocated, or constrained in allocation). This is contrasted to traditional memory reliability calculations to show that the actual mean time to failure may be more optimistic when program behavior is considered. It also develops expressions for the probability of unobserved faults. With several studies reporting correlations between increased workloads and increased failure rates, a new theory is proposed here that provides an explanation for this behavior. The model studies several program traces demonstrating that increased workloads could cause an increase of the observed failure rates in the range of 32% to 53%.