Abstract
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.
| Original language | English (US) |
|---|---|
| Article number | 8351912 |
| Pages (from-to) | 889-898 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Cloud Computing |
| Volume | 8 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jul 1 2020 |
Keywords
- Cloud computing
- auto-scaling
- data centers
- dynamic stochastic bin packing
- online algorithms
- virtual machine
ASJC Scopus subject areas
- Software
- Information Systems
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications