My research on cloud computing systems focuses on how to improve resource utilization in data centers using economic optimization models.
VM Splitting and Assignment
Using multiple smaller virtual machines (SVM) reduces the granulairty in VM placement and may further increase the utilization of physical resources at data centers. However, a major challenge to overcome when deploying multiple SVMs for one application is to preserve the performance of the application in terms of response delay. We show through theoretical analysis and experiments that in order to preserve the performance of the application, one needs to allocate sufficient resources to each SVM, and the total amount of resources required by all the SVMs will exceed that required by the LVM. Nevertheless we also show that by using the proposed heuristic algorithm called VM splitting and assignment (VMSA), we can substantially improve the utilization and reduce the number of physical servers.
>>Download Liu, L., J. Xu, C. Qiao, H. Yu, L. Li. 2015. VMSA: A Performance Preserving Online VM Splitting and Placement Algorithm in Dynamic Cloud Environments. Journal of Supercomputing, published online, 72 3169-3193.
Joint Admission Control and Provisioning for Virtual Machines
We propose a joint admission control and VM provisioning scheme based on a Markov Decision Process (MDP) framework to optimize the VM admission and placement decision to maximize the cloud provider's operating revenue. Because of the large number of physical servers in data centers, the MDP problem is computationally intractable to standard dynamic programming techniques. We propose a simulation-based approximate dynamic programming (ADP) algorithm to effectively solve the problem. Simulation results show that the new joint admission control and VM provisioning algorithm can substantially increase the revenue. >>Download Liu L., J. Xu, H. Yu, X. Wei. 2015. Joint admissions control and provisioning for virtual machines. Proceedings of 2015 IEEE International Conference on Communications, IEEE, 332-337.
Optimal Pricing and Capacity Planning of a New Economy Cloud Computing Service Class
This paper presents an analytic study on the optimal pricing and capacity planning of a new Economy cloud computing service class that supports long-term SLO using reclaimed computing resources. We show that depending on the terms of the service level agreements and the characteristics of the cloud computing workloads, a cloud service provider may either choose a penalty averse or penalty preference strategy when allocating reclaimed computing resources to the Economy class cloud computing service.We also derive conditions under which the new Economy class will be profitable. >>Download Xu, J. and C. Zhu. 2015. Optimal Pricing and Capacity Planning of a New Economy Cloud Computing Service Class. In Proceedings of 2015 IEEE International Conference on Cloud and Autonomic Computing (ICCAC), 149-157.