  Let $X_1,\dots,X_n$ be independent nonnegative random variables (r.v.'s),
with $S_n:=X_1+\dots+X_n$ and finite values of $s_i:=E X_i^2$ and $m_i:=E
X_i>0$. Exact upper bounds on $E f(S_n)$ for all functions $f$ in a certain
class $\mathcal{F}$ of nonincreasing functions are obtained, in each of the
following settings: (i) $n,m_1,\dots,m_n,s_1,\dots,s_n$ are fixed; (ii) $n$,
$m:=m_1+\dots+m_n$, and $s:=s_1+\dots+s_n$ are fixed; (iii)~only $m$ and $s$
are fixed. These upper bounds are of the form $E f(\eta)$ for a certain r.v.
$\eta$. The r.v. $\eta$ and the class $\mathcal{F}$ depend on the choice of one
of the three settings. In particular, $(m/s)\eta$ has the binomial distribution
with parameters $n$ and $p:=m^2/(ns)$ in setting (ii) and the Poisson
distribution with parameter $\lambda:=m^2/s$ in setting (iii). One can also let
$\eta$ have the normal distribution with mean $m$ and variance $s$ in any of
these three settings. In each of the settings, the class $\mathcal{F}$
contains, and is much wider than, the class of all decreasing exponential
functions. As corollaries of these results, optimal in a certain sense upper
bounds on the left-tail probabilities $P(S_n\le x)$ are presented, for any real
$x$. In fact, more general settings than the ones described above are
considered. Exact upper bounds on the exponential moments $E\exp\{hS_n\}$ for
$h<0$, as well as the corresponding exponential bounds on the left-tail
probabilities, were previously obtained by Pinelis and Utev. It is shown that
the new bounds on the tails are substantially better.
