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Online VM Auto-Scaling Algorithms for Application Hosting in a Cloud



Yang Guo, Alexander Stolyar, Anwar Walid


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 algorithm’s good performance and high adaptivity.
IEEE Transactions on Cloud Computing


Cloud, VM choice and placement, Shadow routing


Guo, Y. , Stolyar, A. and Walid, A. (2020), Online VM Auto-Scaling Algorithms for Application Hosting in a Cloud, IEEE Transactions on Cloud Computing, [online], (Accessed May 22, 2024)


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Created August 31, 2020, Updated September 15, 2020