%0 Journal Article %1 7243348 %A Chase, J. %A Niyato, D. %D 2017 %J IEEE Transactions on Services Computing %K cloud scaling %N 3 %P 396-409 %R 10.1109/TSC.2015.2476812 %T Joint Optimization of Resource Provisioning in Cloud Computing %U http://ieeexplore.ieee.org/document/7243348/citations?tabFilter=papers %V 10 %X Cloud computing exploits virtualization to provision resources efficiently. Increasingly, Virtual Machines (VMs) have high bandwidth requirements; however, previous research does not fully address the challenge of both VM and bandwidth provisioning. To efficiently provision resources, a joint approach that combines VMs and bandwidth allocation is required. Furthermore, in practice, demand is uncertain. Service providers allow the reservation of resources. However, due to the dangers of over- and under-provisioning, we employ stochastic programming to account for this risk. To improve the efficiency of the stochastic optimization, we reduce the problem space with a scenario tree reduction algorithm, that significantly increases tractability, whilst remaining a good heuristic. Further we perform a sensitivity analysis that finds the tolerance of our solution to parameter changes. Based on historical demand data, we use a deterministic equivalent formulation to find that our solution is optimal and responds well to changes in parameter values. We also show that sensitivity analysis of prices can be useful for both users and providers in maximizing cost efficiency.

@article{7243348, abstract = {Cloud computing exploits virtualization to provision resources efficiently. Increasingly, Virtual Machines (VMs) have high bandwidth requirements; however, previous research does not fully address the challenge of both VM and bandwidth provisioning. To efficiently provision resources, a joint approach that combines VMs and bandwidth allocation is required. Furthermore, in practice, demand is uncertain. Service providers allow the reservation of resources. However, due to the dangers of over- and under-provisioning, we employ stochastic programming to account for this risk. To improve the efficiency of the stochastic optimization, we reduce the problem space with a scenario tree reduction algorithm, that significantly increases tractability, whilst remaining a good heuristic. Further we perform a sensitivity analysis that finds the tolerance of our solution to parameter changes. Based on historical demand data, we use a deterministic equivalent formulation to find that our solution is optimal and responds well to changes in parameter values. We also show that sensitivity analysis of prices can be useful for both users and providers in maximizing cost efficiency.}, added-at = {2017-06-12T11:48:41.000+0200}, author = {Chase, J. and Niyato, D.}, biburl = {https://www.bibsonomy.org/bibtex/270c5d190215d40b3ef95f2cd350ec13c/jose.a.pascual}, description = {Joint Optimization of Resource Provisioning in Cloud Computing - IEEE Xplore Document}, doi = {10.1109/TSC.2015.2476812}, interhash = {0e45a0d58665befca21e55207425bcce}, intrahash = {70c5d190215d40b3ef95f2cd350ec13c}, issn = {1939-1374}, journal = {IEEE Transactions on Services Computing}, keywords = {cloud scaling}, month = may, number = 3, pages = {396-409}, timestamp = {2017-06-12T11:53:01.000+0200}, title = {Joint Optimization of Resource Provisioning in Cloud Computing}, url = {http://ieeexplore.ieee.org/document/7243348/citations?tabFilter=papers}, volume = 10, year = 2017 }