TY - GEN

T1 - An efficient geometry data allocation algorithm in cloud computing environments

AU - Wang, Kun Wei

AU - Huang, Bo Wei

AU - Peng, Wen-Chih

PY - 2012

Y1 - 2012

N2 - The number of location-based services is growing and developing. Usually, these services put a huge amount of effort into geometry data computation. Thus, their workload is generally high. By exploring cloud computing techniques, one could utilize a number of computing nodes to distribute the workload of the systems. However, the workload is usually not equally balanced across computing nodes, if data is not welldistributed. To make the best use of computing nodes, we propose a sophisticated data distribution technology for geometry computation processing. Intuitively, one can simply divide geometry data into tiles so that the geometry data in each tile can be stored on one computing node. Unfortunately, since data in a tile shares spatial-proximity, processing a geometry computation on spatialproximity data still incurs a huge workload. To address this issue, we propose a new data distribution approach, Reversed Kmeans, to distribute geometry data that shares spatial-proximity across different computing nodes. In this way, we can use more computing nodes to process geometry computation and get better performance. To evaluate the performance of our proposed algorithm, we evaluate the utility of computing nodes and the response time when performing geometry computations. The experimental results show that the utility of the computing nodes is higher than existing methods, and the response time is the fastest of all methods.

AB - The number of location-based services is growing and developing. Usually, these services put a huge amount of effort into geometry data computation. Thus, their workload is generally high. By exploring cloud computing techniques, one could utilize a number of computing nodes to distribute the workload of the systems. However, the workload is usually not equally balanced across computing nodes, if data is not welldistributed. To make the best use of computing nodes, we propose a sophisticated data distribution technology for geometry computation processing. Intuitively, one can simply divide geometry data into tiles so that the geometry data in each tile can be stored on one computing node. Unfortunately, since data in a tile shares spatial-proximity, processing a geometry computation on spatialproximity data still incurs a huge workload. To address this issue, we propose a new data distribution approach, Reversed Kmeans, to distribute geometry data that shares spatial-proximity across different computing nodes. In this way, we can use more computing nodes to process geometry computation and get better performance. To evaluate the performance of our proposed algorithm, we evaluate the utility of computing nodes and the response time when performing geometry computations. The experimental results show that the utility of the computing nodes is higher than existing methods, and the response time is the fastest of all methods.

KW - Cloud computing

KW - Data allocation

KW - Geometry computation

UR - http://www.scopus.com/inward/record.url?scp=84874043845&partnerID=8YFLogxK

U2 - 10.1109/ICPADS.2012.44

DO - 10.1109/ICPADS.2012.44

M3 - Conference contribution

AN - SCOPUS:84874043845

SN - 9780769549033

T3 - Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS

SP - 260

EP - 267

BT - Proceedings of the 2012 IEEE 18th International Conference on Parallel and Distributed Systems, ICPADS 2012

T2 - 18th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2012

Y2 - 17 December 2012 through 19 December 2012

ER -