Biologically-inspired resource management for cloud computing.
Ching, Mark Chuen Teck.
Date of Issue2010
School of Computer Engineering
Cloud computing allows the users to efficiently and dynamically provision computing resources to meet their IT needs. Most cloud providers offer two types of payment plans to the user, i.e., reservation and on-demand. The reservation plan is typically cheaper than the on-demand plan but reservation plan has to be provisioned in advance. Reserving the resources would be straightforward if the actual computing demand (e.g., job processing) is known in advance. However, in reality, the actual computing demand is known only at the point of actual usage. This makes it difficult to reserve the correct amount of resources during the reservation to meet the computing demands of the users. In this project, we propose an evolutionary optimal virtual machine placement (EOVMP) algorithm with a demand forecaster. First, a demand forecaster predicts the computing demand from user’s data. Then, EOVMP uses this predicted demand to allocate the virtual machine using reservation and on-demand plans for job processing. The performance of the proposed schemes is evaluated by simulations and numerical studies. The evaluation result shows that the EOVMP algorithm can provide the solution close to the optimal solution of stochastic integer programming (SIP) and the prediction of the demand forecaster is of reasonable accuracy.
DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks
Final Year Project (FYP)
Nanyang Technological University