We revisit a classical scheduling model to incorporate modern trends in data center networks and cloud services. Addressing some key challenges in the allocation of shared resources to user requests (jobs) in such settings, we consider the following variants of the classic {\em resource allocation problem} (\textsf{RAP}). The input to our problems is a set $J$ of jobs and a set $M$ of homogeneous hosts, each has an available amount of some resource. A job is associated with a release time, a due date, a weight, and a given length, as well as its resource requirement. A \emph{feasible} schedule is an allocation of the resource to a subset of the jobs, satisfying the job release times/due dates as well as the resource constraints. A crucial distinction between classic {\textsf{RAP}} and our problems is that we allow preemption and migration of jobs, motivated by virtualization techniques. We consider two natural objectives: {\em throughput maximization} (\textsf{MaxT}), which seeks a maximum weight subset of the jobs that can be feasibly scheduled on the hosts in $M$, and {\em resource minimization} (\textsf{MinR}), that is finding the minimum number of (homogeneous) hosts needed to feasibly schedule all jobs. Both problems are known to be NP-hard. We first present a $Ω(1)$-approximation algorithm for \textsf{MaxT} instances where time-windows form a laminar family of intervals. We then extend the algorithm to handle instances with arbitrary time-windows, assuming there is sufficient slack for each job to be completed. For \textsf{MinR} we study a more general setting with $d$ resources and derive an $O(\log d)$-approximation for any fixed $d \geq 1$, under the assumption that time-windows are not too small. This assumption can be removed leading to a slightly worse ratio of $O(\log d\log^* T)$, where $T$ is the maximum due date of any job.