slight changes to algorithms

U-COE\mhempstead
1 parent 6b5ef29c
Showing 1 changed file with 2 additions and 2 deletions
algorithm_implications.tex
@@ -24,14 +24,14 @@ intervals. We propose two ways in which this can be achieved.
 \item \textit{Learning:} Algorithms can use learning based approaches to predict when to run again.
 Isci. et. al~\cite{ieeemicro05-isci} propose simple ways in which algorithms can detect how long the
 current application phase is going to be stable and only choose to tune at the end of predicted
-phase for CPU performance.  Similar approaches could be developed that extend this methodology to
+phase for CPU performance. Similar approaches could be developed that extend this methodology to
 detect stable regions of clusters containing both memory and CPU settings.
  
 \item \textit{Offline Analysis:} Another approach that can be taken to reduce
 the number of tuning events is offline analysis of the applications. An
 application can be profiled once offline to identify regions in which the
 performance cluster is stable. The profiled information of the stable region
-lengths, positions and available settings can then be used at run time to enable
+lengths, positions, and available settings can then be used at run time to enable
 the system to predict how long it can go without tuning. Algorithms can also
 extend the usage of the profiled information to new applications that may have
 phases that match with already profiled data. Previous work has already proposed