An efficient optimistic message logging scheme for recoverable mobile computing systems

A number of checkpointing and message logging algorithms have been proposed to support fault tolerance of mobile computing systems. However, little attention has been paid to the optimistic message logging scheme. Optimistic logging has a lower failure-free operation cost compared to other logging schemes. It also has a lower failure recovery cost compared to the checkpointing schemes. This paper presents an efficient scheme to implement optimistic logging for the mobile computing environment. In the proposed scheme, the task of logging is assigned to the mobile support station so that volatile logging can be utilized. In addition, to reduce the message overhead, the mobile support station takes care of dependency tracking and the potential dependency between mobile hosts is inferred from the dependency between mobile support stations. The performance of the proposed scheme is evaluated by an extensive simulation study. The results show that the proposed scheme requires a small failure-free overhead and the cost of unnecessary rollback caused by the imprecise dependency is adjustable by properly selecting the logging frequency.     

A Low Overhead Logging Scheme for Fast Recovery in Distributed Shared Memory Systems

This paper presents an efficient, writer-based logging scheme for recoverable distributed shared memory systems, in which logging of a data item is performed by its writer process, instead of every process that accesses the item logging it. Since the writer process maintains the log of data items, volatile storage can be used for logging. Only the readers' access information needs to be logged into the stable storage of the writer process to tolerate multiple failures. Moreover, to reduce the frequency of stable logging, only the data items accessed by multiple processes are logged with their access information when the items are invalidated, and also semantic-based optimization in logging is considered. Compared with the earlier schemes in which stable logging was performed whenever a new data item was accessed or written by a process, the size of the log and the logging frequency can be significantly reduced in the proposed scheme.     

An Asynchronous Protocol for Release Consistent Distributed Shared Memory Systems

Distributed shared memory (DSM) systems provide a simple programming paradigm for networks of workstations, which are gaining popularity due to their cost-effective high computing power. However, DSM systems usually exhibit poor performance due to the large communication delay between the nodes; and a lot of different memory consistency models have been proposed to mask the network delay. In this paper, we propose an asynchronous protocol for the release consistent memory model, which we call an Asynchronous Release Consistency (ARC) protocol. Unlike other protocols where the communication adheres to the synchronous request/receive paradigm, the ARC protocol is asynchronous, such that the necessary pages are broadcast before they are requested. Hence, the network delay can be reduced by proper prefetching of necessary pages. We have also compared the performance of the ARC protocol with the lazy release protocol by running standard benchmark programs; and the experimental results showed that the ARC protocol achieves a performance improvement of up to 29%.     

Monthly Precipitation Forecasting with a Neuro-Fuzzy Model

Quantitative and qualitative monthly precipitation forecasts are produced with ANFIS. To select the proper input variable set from 30 variables, including climatological and hydrological monthly recording data, the forward selection method, which is a wrapper method for feature selection, is applied. The error analysis of the results from training and checking the data sets suggests that 3 variables can be used as a suitable number of inputs for ANFIS, and the best five 3-input-variable sets were selected. The quantitative monthly precipitation forecasts were computed using each 3-input-variable set, and the ensemble averaging method over the five forecasts was used for calculations to reduce the uncertainties in the forecasts and to remove the negative rainfall forecasts. A qualitative forecast that is computed with the quantitative forecast also produced three types of categories that describe the next month??s precipitation condition and was compared with data from the weather agency of Korea.