Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

Citation

BibTex format

@article{Guo:2014,
author = {Guo, Y and He, S and Guo, L},
title = {Enhancing Cloud Resource Utilisation using Statistical Analysis},
url = {http://iaesjournal.com/online/index.php/IJ-CLOSER/article/view/5791},
year = {2014}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Resource provisioning based on virtual machine (VM) has been widely accepted and adopted in cloud computing environments. A key problem resulting from using static scheduling approaches for allocating VMs on different physical machines (PMs) is that resources tend to be not fully utilised. Although some existing cloud reconfiguration algorithms have been developed to address the problem, they normally result in high migration costs and low resource utilisation due to ignoring the multi-dimensional characteristics of VMs and PMs. In this paper we present and evaluate a new algorithm for improving resource utilisation for cloud providers. By using a multivariate probabilistic model, our algorithm selects suitable PMs for VM re-allocation which are then used to generate a reconfiguration plan. We also describe two heuristics metrics which can be used in the algorithm to capture the multi-dimensional characteristics of VMs and PMs. By combining these two heuristics metrics in our experiments, we observed that our approach improves the resource utilisation level by around 8% for cloud providers, such as IC Cloud, which accept user-defined VM configurations and 14% for providers, such as Amazon EC2, which only provide limited types of VM configurations.
AU - Guo,Y
AU - He,S
AU - Guo,L
PY - 2014///
TI - Enhancing Cloud Resource Utilisation using Statistical Analysis
UR - http://iaesjournal.com/online/index.php/IJ-CLOSER/article/view/5791
ER -