A statistical approach to risk mitigation in computational markets
Thomas E. Sandholm, Kevin Lai · 2007
We study stochastic models to mitigate the risk of poor Quality-of-Service (QoS) in computational markets. Consumers whopurchase services expect both price and performance guarantees. They need to predict future demand to budget for sustained performance despite price fluctuations. Conversely, providers need to estimate demand to price future usage. The skewed and bursty nature of demand in large-scale computer networks challenges the common statistical assumptions of symmetry, independence, and stationarity. This discrepancy leads to under estimation of investment risk. We confirm this non-normal distribution behavior in our study of demand in computational markets.