  The problem of online scheduling of multi-server jobs is considered, where
there are a total of $K$ servers, and each job requires concurrent service from
multiple servers for it to be processed. Each job on its arrival reveals its
processing time, the number of servers from which it needs concurrent service
and an online algorithm has to make scheduling decisions using only causal
information, with the goal of minimizing the response/flow time. The worst case
input model is considered and the performance metric is the competitive ratio.
For the case, when all job processing time (sizes) are the same, we show that
the competitive ratio of any deterministic/randomized algorithm is at least
$\Omega(K)$ and propose an online algorithm whose competitive ratio is at most
$K+1$. With equal job sizes, we also consider the resource augmentation regime
where an online algorithm has access to more servers than an optimal offline
algorithm. With resource augmentation, we propose a simple algorithm and show
that it has a competitive ratio of $1$ when provided with $2K$ servers with
respect to an optimal offline algorithm with $K$ servers. With unequal job
sizes, we propose an online algorithm whose competitive ratio is at most $2K
\log (K w_{\max})$, where $w_{\max}$ is the maximum size of any job.
