Optimizing Auto-scaling Virtual Machines for a Cloud-based VoD Data Center

MPhil Thesis Defence


Title: "Optimizing Auto-scaling Virtual Machines for a Cloud-based VoD Data 
Center"

By

Mr. Ka Wai AU


Abstract

We consider a Netflix-like video-on-demand (VoD) system, where the video 
popularity remains stable over a day or week while the request traffic may 
vary significantly within a day (e.g., by orders of magnitude). To respond 
to dynamic user traffic in a timely manner, we study an auto-scaling 
cloud-based VoD data center, where the virtual machines (VMs) can be 
turned on and off according to user traffic to elastically scale system 
resources. Movies are stored in persistent storage as standby unit, which 
is attached to VMs on-demand at any time. Due to limited VM streaming 
capacities and persistent storage size, a movie request is dispatched 
either to an operating VM or to a remote repository (the so-called remote 
traffic). We are interested in minimizing the number of operating VMs 
given a certain remote traffic constraint, by jointly optimizing which 
movies to store in each persistent storage unit, which storage units to be 
attached for online VMs, and which VMs to dispatch user requests.

We formulate the problem and show that it is NP-hard. We propose AMATO 
(Auto-scaling Movie Allocation and Traffic Optimization), an efficient 
approximation algorithm which achieves 2-approximation at peak traffic. 
Both experiments on cloud platform (Amazon EC2) and trace-driven 
simulation based on large-scale real-world data show that AMATO is closely 
optimal. It achieves significantly lower number of operating VMs as 
compared with the other state-of-the-art and traditional schemes.


Date:			Friday, 5 August 2016

Time:			3:00pm - 5:00pm

Venue:			Room 3494
 			Lifts 25/26

Committee Members:	Prof. Gary Chan (Supervisor)
 			Dr. Sunghun Kim (Chairperson)
 			Prof. Cunsheng Ding


**** ALL are Welcome ****