number of computing iterations (2000 in our case).
Note that this convergence detection was implemented in a centralized manner.
-This is easy to do within the simulator, but it's obviously not realistic. In
-a real application we would have chosen a decentralized convergence detection algorithm, like the one described in \cite{10.1109/TPDS.2005.2}.
+This is easy to do within the simulator, but it's obviously not realistic. In a
+real application we would have chosen a decentralized convergence detection
+algorithm, like the one described in \cite{10.1109/TPDS.2005.2}.
\paragraph{Platforms}
that can be compared between simulations of different sizes.
This metric is expected to give an idea of the ability of the strategy to
- diffuse the load quickly, lesser is better.
+ diffuse the load quickly. A smaller value is better.
\item[\textbf{average convergence date:}] that's the average of the dates when
all nodes reached the convergence state. The dates are measured as a number
reached the convergence state.
These two dates give an idea of the time needed by the strategy to reach the
- equilibrium state, lesser is doubtlessly better.
+ equilibrium state. A smaller value is better.
-\item[\textbf{data transfer amount:}] that's the sum of the amount of data of
- all transfers during the simulation. This sum is then normalized by dividing
- it by the total amount of data present in the system.
+\item[\textbf{data transfer amount:}] that's the sum of the amount of all data
+ transfers during the simulation. This sum is then normalized by dividing it
+ by the total amount of data present in the system.
This metric is expected to give an idea of the efficiency of the strategy in
terms of data movements, i.e. its ability to reach the equilibrium with fewer
- transfers.
+ transfers. Again, a smaller value is better.
\end{description}
