Reducing Makespan via Optimizing Service Applications Scheduling Without Runtime Estimation

Efficient scheduling of service applications is critical for improving cluster resource utilization while minimizing makespan and application completion time. However, existing schedulers often struggle with coordinating task placement on worker machines due to the lack of runtime estimations. This limitation leads to two major performance issues: the non-synchronization problem and the contention-oblivious problem, both of which result in suboptimal application completion times. To address these challenges, Morbius is proposed, a scheduler that explicitly leverages the spatial structure of service applications to enhance scheduling decisions. Morbius adopts an all-or-nothing scheduling policy, ensuring that all tasks of an application are scheduled to run simultaneously, thereby effectively mitigating the non-synchronization problem. Within each priority queue, Morbius follows a shortest total time first policy, which facilitates contention-aware scheduling. Moreover, Morbius incorporates work conservation and starvation avoidance policies to better handle execution uncertainties and further improve application completion times. A prototype of Morbius is implemented on Yarn and evaluated in two environments: a homogeneous cluster with 36 machines and a heterogeneous cluster with 122 machines. Experimental results show that Morbius significantly outperforms existing approaches, improving average application completion time by up to 10.41× and reducing makespan by over 32.80%.