Summary: We consider the online machine minimization problem in which jobs with hard deadlines arrive online over time at their release dates. The task is to determine a feasible preemptive schedule on a minimum number of machines. Our main result is a general \(\mathcal O(\log m)\)-competitive algorithm for the online problem, where \(m\) is the optimal number of machines used in an offline solution. This is the first improvement to an intriguing problem in nearly two decades. To date, the best known result is a \(\mathcal O(\log (p_{\max}/p_{\min}))\)-competitive algorithm by C. A. Phillips et al. [in: Proceedings of the 29th annual ACM symposium on theory of computing, STOC’97. New York, NY: ACM, Association for Computing Machinery. 140–149 (1999; Zbl 0962.68010)] that depends on the ratio of maximum and minimum job sizes, \(p_{\max}\) and \(p_{\min}\). Even for \(m=2\) no better algorithm was known. Our algorithm is in this case constant-competitive. When applied to laminar or agreeable instances, our algorithm achieves a competitive ratio of \(\mathcal O(1)\) even independently of \(m\). The following two key components lead to our new result. First, we derive a new lower bound on the optimum value that relates the laxity and the number of jobs with intersecting time windows. Then, we design a new algorithm that is tailored to this lower bound and balances the delay of jobs by taking the number of currently running jobs into account.