Research Area:  Cloud Computing
We consider the auto-scaling problem for application hosting in a cloud, where applications are elastic and the number of requests changes over time. The application requests are serviced by Virtual Machines (VMs), which reside on Physical Machines (PMs) in a cloud. We aim to minimize the number of hosting PMs by intelligently packing VMs into PMs, while the VMs are auto-scaled, i.e., dynamically acquired and released, to accommodate varying application needs. We consider a shadow routing based approach for this problem. The proposed shadow algorithm employs a specially constructed virtual queueing system to dynamically produce an optimal solution that guides the VM auto-scaling and the VM-to-PM packing. The proposed algorithm runs continuously without the need to re-solve the underlying optimization problem “from scratch”, and adapts automatically to the changes in the application demands. We prove the asymptotic optimality of the shadow algorithm. The simulation experiments further demonstrate the algorithms good performance and high adaptivity.
Author(s) Name:  Yang Guo,Alexander L. Stolyar and Anwar Walid
Journal name:  IEEE Transactions on Cloud Computing
Publisher name:  IEEE
Volume Information:  July-Sept. 2020, pp. 889-898, vol. 8
Paper Link:   https://www.computer.org/csdl/journal/cc/2020/03/08351912/13rRUxCRFPR