1. 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))= sa(ARFIMA(4,d,4))= 0.108
no model: / (95%) => 100 to 306 ms
ARMA: 1.0 +/ (95%) => to 222 ms
ARFIMA: 1.0 +/ (95%) => 179 to 221 ms
axp7: sz=0.14, m=0.12, sa(ARMA(4,4))= sa(ARFIMA(4,d,4))= 0.025
no model: / (95%) => 100 to 139 ms
ARMA: / (95%) => 104 to 120 ms
ARFIMA: / (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