Adaptive Quantization-based Communication Data Management for High-Performance Geo-computation in Grid Computing

Communication data management (CDM) is an important issue in complex and large-scale modern geo-computation applications. In this paper, we review existing CDM schemes and propose an efficient adaptive quantization-based CDM scheme for geo-computation in grid environment. This model is based on theory of quantization and uses distributed clusters which are concerned about the features and geographical adjacency of communication objects. And, this model uses various thresholds which are suitable for individual clusters and effectively reduces communication messages by clustering and diverse thresholds. For realization of this scheme, we design and develop a HLA (high level architecture)-based system for geo-computation in grid environment. For performance evaluation, this paper measures reduction rate of message traffic and location errors. This scheme has more effective message traffic reduction and less location errors with precise location accuracy rate than general quantization-based scheme. The empirical result apparently presents that adaptive quantization-based CDM scheme is a suitable scheme for high-performance geo-computation