Download now Free registration required
Bandwidth usage in large-scale Video on Demand (VoD) systems varies rapidly over time, due to unpredictable dynamics in user demand and network conditions. Such bandwidth volatility makes it hard to provision the exact amount of server resources that matches the demand in each video channel, posing significant challenges to achieving quality assurance and efficient resource allocation at the same time. In this paper, the seek to statistically model time-varying traffic volatility in VoD servers, leveraging heteroscedastic models first used to interpret economic time series, with the goal of forecasting not only traffic patterns but also traffic volatility.
- Format: PDF
- Size: 1181 KB