  We consider the problem of strongly-convex online optimization in presence of
adversarial delays; in a T-iteration online game, the feedback of the player's
query at time t is arbitrarily delayed by an adversary for d_t rounds and
delivered before the game ends, at iteration t+d_t-1. Specifically for
\algo{online-gradient-descent} algorithm we show it has a simple regret bound
of \Oh{\sum_{t=1}^T \log (1+ \frac{d_t}{t})}. This gives a clear and simple
bound without resorting any distributional and limiting assumptions on the
delays. We further show how this result encompasses and generalizes several of
the existing known results in the literature. Specifically it matches the
celebrated logarithmic regret \Oh{\log T} when there are no delays (i.e. d_t =
1) and regret bound of \Oh{\tau \log T} for constant delays d_t = \tau.
