An Approximation Algorithm to Maximize User Capacity for an Auto-Scaling VoD System

In a video-on-demand (VoD) service, blockbuster videos have stable and predictable popularity, but the traffic can vary significantly within short timescale. To efficiently serve the user pool in a geographic region, we consider a regional auto-scaling cloud-based data center consisting of multiple servers. For efficient storage, we partition the videos into fixed-size blocks. To respond to dynamic user traffic in a timely and cost-effective manner, we may activate or deactivate each server according to the traffic while keeping at least one replica for each block in the active servers. We maximize the user capacity of the active servers (and hence minimizing the number of active servers at any time) by jointly optimizing block allocation in the servers, server selection at each traffic level, and request dispatching to a server. We believe that this is the first work to study such problem for an auto-scaling cloud-based VoD data center. We first formulate the problem and show its NP-hardness. We then propose AVARDO (
A
uto-scaling
V
ideo
A
llocation and
R
equest
D
istribution
O
ptimization), a simple but efficient approximation algorithm with proven optimality. AVARDO operates the servers like a stack, with a server being pushed into or popped from the existing active server set according to some optimized traffic thresholds. We prove that AVARDO approaches the theoretical optimum as the block size reduces. Trace-driven experimental results based on large-scale real-world video data further validate that AVARDO is closely optimal. It achieves significantly higher user capacity as compared with other state-of-the-art and traditional schemes, and reduces the optimality gap by multiple times.