*Result*: Machine covering problem in MapReduce systems with a small number of machines.
*Further Information*
*This study investigates machine covering problems on some uniform machines in the MapReduce system. The aim is to maximize the minimum machine completion time. Each job comprises both map tasks and reduce tasks. The map tasks of a job can be freely partitioned and scheduled to different machines for simultaneous processing, whereas reduce tasks can only proceed after all corresponding map tasks have been finished. The study involves both the preemptive and non-preemptive variants of the reduce tasks for the given problem. For the preemptive version of reduce tasks, we introduce optimal algorithms to scenarios involving two and three machines. For the non-preemptive version of reduce tasks, we design an approximation algorithm that guarantees a worse-case ratio of specifically for the two-machine case, where denotes the speed ratio between the two machines. [ABSTRACT FROM AUTHOR]
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