Let $X\_1, X\_2, \ldots$ be a sequence of positive iid random variables with mean $\mu$ whose distribution admits a moment generating function in a neighborhood of zero. 


Let $N\_t$ be the associated renewal process given by
$$N\_t = \sup \left\{ m \geq 0: \sum\_{i=1}^m X\_i \leq t \right\}.$$


I am looking for DKW type inequalities that would give an exponentially decreasing upper bound to the following probability:


$$
P \left( \sup\_{0 \leq t \leq T} \left| \dfrac{N\_{nt}}{n} - \dfrac{t}{\mu} \right| \geq \epsilon \right).
$$


Are there any results which give an upper bound which is up to a constant equal to $\exp(-n\epsilon^2)$?



DKW type inequality for renewal processes
$\newcommand{\si}{\sigma}
\newcommand{\ep}{\varepsilon}
$
Without loss of generality, $\ep\ge4/n$, because otherwise the bound $Ke^{-n\ep^2}$ on a probability (with an unspecified constant $K$) is trivial. 
Let $S\_m:=\sum\_1^m X\_i$. Then for $t\ge0$
\begin{equation}
 S\_{N\_{nt}}\le nt<S\_{N\_{nt}+1}. 
\end{equation}
If the event $\sup\_{0\le t\le T}|\frac{N\_{nt}}n-\frac t\mu|>\ep$ occurs, then for some $t\in[0,T]$ we have 
\begin{equation}
 \text{ either (i) $N\_{nt}>k:=\lfloor n(\tfrac t\mu+\ep)\rfloor$ or (ii) $N\_{nt}+1<l:=\lceil n(\tfrac t\mu-\ep)\rceil+1$ }, 
\end{equation}
whence either 
$$S\_k\le S\_{N\_{nt}}\le nt\le(k+1-n\ep)\mu\le(k-n\ep/2)\mu$$
or 
$$S\_l\ge S\_{N\_{nt}+1}\ge nt\ge(l-2+n\ep)\mu\ge(l+n\ep/2)\mu;$$
note also that here 
\begin{equation}
k\le N:=\lfloor n(\tfrac T\mu+\ep)\rfloor, \quad 
l\le\lfloor n(\tfrac T\mu-\ep)+2\rfloor\le\lfloor n\tfrac T\mu\rfloor\le N. 
\end{equation}


So, 
\begin{equation}
 P \left( \sup\_{0 \leq t \leq T} \left| \dfrac{N\_{nt}}{n} - \dfrac{t}{\mu} \right| \geq \ep \right)
 \le Q\_1+Q\_2, 
\end{equation}
where 
\begin{equation}
 Q\_1:=P(\min\_{k\le N}T\_k\le-n\ep\mu/2),\quad
 Q\_2:=P(\max\_{l\le N}T\_l\ge n\ep\mu/2), 
\end{equation}
\begin{equation}
 T\_k:=\sum\_1^k Y\_i,\quad Y\_i:=X\_i-\mu, 
\end{equation}
so that $EY\_i=0$. 
For positive $h$ close enough to $0$, we have $Ee^{h|Y\_i|}<2$, whence 
\begin{equation}
 Ee^{hY\_i}\le E(1+hY\_i+h^2Y\_i^2e^{h|Y\_i|}/2)\le1+h^2\si^2\le e^{h^2\si^2}, 
\end{equation}
where $\si^2:=EY\_i^2=Var\,X\_i$. 
Using now Doob's maximal inequality for the submartingale $(e^{hT\_k})\_{k\ge1}$, with small enough $\ep$ and 
\begin{equation}
 h=\frac nN\,\frac{\ep\mu}{4\si^2}
\end{equation}
we have 
\begin{multline}
 Q\_2\le\exp\{-hn\ep\mu/2\}Ee^{hT\_N} \\ 
 \le\exp\{-hn\ep\mu/2+Nh^2\si^2\}
 =\exp\Big\{-\frac{n^2\ep^2\mu^2}{16\si^2 N}\Big\} \\ 
 \le\exp\Big\{-\frac{n\ep^2\mu^2}{16\si^2(T/\mu+\ep)}\Big\}
 \le e^{-cn\ep^2}, 
\end{multline}
where $c:=\mu^3/(32\si^2 T)$. 
Similarly, $Q\_1\le e^{-cn\ep^2}$. So, the probability in question is upper-bounded by $2e^{-cn\ep^2}$, for small enough $\ep$ (depending on the distribution of $X\_1$ and $T$). This bound is similar to what you wanted, $Ke^{-n\ep^2}$, except that the factor in the exponent has the extra constant factor $c$, depending on $\mu,\si,T$ -- as it of course should be. 


