@article{bouterse_perros_2019, title={Performance analysis of the reserve capacity policy for dynamic VM allocation in a SaaS environment}, volume={93}, ISSN={["1878-1462"]}, DOI={10.1016/j.simpat.2018.07.002}, abstractNote={We consider a periodic-review provision scheme with constant inspection intervals for allocating dynamically virtual machines (VMs) in a Software-as-a Service (SaaS) environment. At each interval, we determine how many virtual machines (VMs) to provisioned or de-provision using a simple heuristic referred to as the reserve capacity policy, since it maintains a fixed reserve capacity of VMs. We analyze the performance of the reserve capacity policy within the context of a periodic-review provision scheme using a Markov Chain embedded at the inspection intervals. We assume a single stream of jobs with each job requiring a single VM. Jobs arrive in a Poisson fashion and the execution time of a job in a VM is exponentially distributed. We calculate the probability distribution of the number of customers in the system, the number of in-service VMs, the utilization, and the queue-length distribution of the waiting customers. The embedded Markov Chain is solved numerically. For cases where the underlying transition matrix is very large, we have proposed approximations and showed that they have a root mean square error (RMSE) of less than 2%.}, journal={SIMULATION MODELLING PRACTICE AND THEORY}, author={Bouterse, Brian and Perros, Harry}, year={2019}, month={May}, pages={293–304} }
 @inproceedings{bouterse_perros_2012, title={Scheduling cloud capacity for time- varying customer demand}, DOI={10.1109/cloudnet.2012.6483668}, abstractNote={As utility computing resources become more ubiquitous, service providers increasingly look to the cloud for an in-full or in-part infrastructure to serve utility computing customers on demand. Given the costs associated with cloud infrastructure, dynamic scheduling of cloud resources can significantly lower costs while providing an acceptable service level. We investigated several methods for predicting the required cloud capacity in the presence of time-varying customer demand of application environments. We evaluated and compared their performance, using historical data of the Virtual Computing Laboratory (VCL) at North Carolina State University. We show that a simple heuristic, whereby we continuously maintain a fixed reserve capacity, performs better than the other methods.}, booktitle={2012 IEEE 1st International Conference on Cloud Networking (cloudnet)}, author={Bouterse, B. and Perros, H.}, year={2012} }