Open Access. Powered by Scholars. Published by Universities.®

Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Dr Jun Shen

2012

Data

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

Towards Bio-Inspired Cost Minimisation For Data-Intensive Service Provision, Lijuan Wang, Jun Shen Dec 2012

Towards Bio-Inspired Cost Minimisation For Data-Intensive Service Provision, Lijuan Wang, Jun Shen

Dr Jun Shen

The world is filled with an unimaginably vast amount of digital information which is getting even vaster and even growing more rapidly. The enormous new data is impacting every area of our society. The real strategic value of the data can determine what will happen and what can be discovered in the future. To better use the so called “Big Data”, automatic business process or workflow is needed to process large quantity of data. Biological systems present fascinating features, such as autonomy, scalability, adaptability, and robustness. The bio-inspired concepts and mechanisms have been successfully applied to service oriented systems. In …


An Effective Data Aggregation Based Adaptive Long Term Cpu Load Predictions Mechanism On Computational Grid, Fang Dong, Junzhou Luo, Aibo Song, Jiuxin Cao, Jun Shen Dec 2012

An Effective Data Aggregation Based Adaptive Long Term Cpu Load Predictions Mechanism On Computational Grid, Fang Dong, Junzhou Luo, Aibo Song, Jiuxin Cao, Jun Shen

Dr Jun Shen

With the development of Internet-based technologies and the rapid growth of scientific computing applications, Grid computing becomes more and more attractive. Generally, the execution time of a CPU-intensive task on a certain resource is tightly related to the CPU load on this resource. In order to estimate the task execution time more accurately to achieve an effective task scheduling, it is significant to make an effective long-term load prediction in dynamic Grid environments. Nevertheless, as the prediction errors will be gradually accumulated while the best values of prediction parameters may vary vigorously, the existing prediction algorithms usually fail to achieve …