Partially Predictable Load Prediction for Best Effort Real Time Peter A. Dinda Execution Model Execution Time and Host Load Integration In BBN QuO Delegate with Hysteresis LoadPred Syscond Load Prediction Engine Host A Other Jobs Server Replica Host B Client Application code BBN QuO System CMU Load Prediction (Peter A. Dinda) Delegate chooses server replica based on load predictions CMU load prediction software uses linear time series models to predict load on each host. QuO delegate choses server replica based on load predictions Integration by Peter A. Dinda and Xiaoming Liu Execution Model and Integration into BBN QuO [tmin,tmax] ? Interactive Application Short tasks with deadlines Unmodified COTS Distributed System Choose host based on predicted load Load is Highly Variable Load is Self Similar Load Exhibits Epochal Behavior Statistical Properties of Host Load Linear Time Series Models Realizable Pole-zero Models Real World Benefits of Prediction sa is the confidence interval for t+1 predictions Map work that would take 100 ms at zero load axp0: sz=0.54, m=1.0, sa(ARMA(4,4))= 0.109 sa(ARFIMA(4,d,4))= 0.108 no model: 1.0 +/- 1.06 (95%) => 100 to 306 ms ARMA: 1.0 +/- 0.22 (95%) => 178 to 222 ms ARFIMA: 1.0 +/- 0.21 (95%) => 179 to 221 ms axp7: sz=0.14, m=0.12, sa(ARMA(4,4))= 0.041 sa(ARFIMA(4,d,4))= 0.025 no model: 0.12 +/- 0.27 (95%) => 100 to 139 ms ARMA: 0.12 +/- 0.08 (95%) => 104 to 120 ms ARFIMA: 0.12 +/- 0.05 (95%) => 107 to 117 ms ARFIMA(p,d,q) ARIMA(p,d,q) ARMA(p,q) AR(p) MA(q) Infinite # roots d roots Load Prediction With Linear Time Series Models Unpredictable Random Sequence Fixed Linear Filter Partially Predictable Load Sequence ARFIMA Models Capture Long-range Dependence of Self-Similar Signals