  Due to its optimality on a single machine for the problem of minimizing
average flow time, Shortest-Remaining-Processing-Time (\srpt) appears to be the
most natural algorithm to consider for the problem of minimizing average flow
time on multiple identical machines. It is known that $\srpt$ achieves the best
possible competitive ratio on multiple machines up to a constant factor. Using
resource augmentation, $\srpt$ is known to achieve total flow time at most that
of the optimal solution when given machines of speed $2- \frac{1}{m}$. Further,
it is known that $\srpt$'s competitive ratio improves as the speed increases;
$\srpt$ is $s$-speed $\frac{1}{s}$-competitive when $s \geq 2- \frac{1}{m}$.
  However, a gap has persisted in our understanding of $\srpt$. Before this
work, the performance of $\srpt$ was not known when $\srpt$ is given
$(1+\eps)$-speed when $0 < \eps < 1-\frac{1}{m}$, even though it has been
thought that $\srpt$ is $(1+\eps)$-speed $O(1)$-competitive for over a decade.
Resolving this question was suggested in Open Problem 2.9 from the survey
"Online Scheduling" by Pruhs, Sgall, and Torng \cite{PruhsST}, and we answer
the question in this paper. We show that $\srpt$ is \emph{scalable} on $m$
identical machines. That is, we show $\srpt$ is $(1+\eps)$-speed
$O(\frac{1}{\eps})$-competitive for $\eps >0$. We complement this by showing
that $\srpt$ is $(1+\eps)$-speed $O(\frac{1}{\eps^2})$-competitive for the
objective of minimizing the $\ell_k$-norms of flow time on $m$ identical
machines. Both of our results rely on new potential functions that capture the
structure of \srpt. Our results, combined with previous work, show that $\srpt$
is the best possible online algorithm in essentially every aspect when
migration is permissible.
