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In the past few years, we have envisioned an increasing number of businessesstart driving by big data analytics, such as Amazon recommendations and GoogleAdvertisements. At the back-end side, the businesses are powered by big dataprocessing platforms to quickly extract information and make decisions. Runningon top of a computing cluster, those platforms utilize scheduling algorithms toallocate resources. An efficient scheduler is crucial to the system performancedue to limited resources, e.g. CPU and Memory, and a large number of userdemands. However, besides requests from clients and current status of thesystem, it has limited knowledge about execution length of the running jobs,and incoming jobs' resource demands, which make assigning resources achallenging task. If most of the resources are occupied by a long-running job,other jobs will have to keep waiting until it releases them. This paperpresents a new scheduling strategy, named DRESS that particularly aims tooptimize the allocation among jobs with various demands. Specifically, itclassifies the jobs into two categories based on their requests, reserves aportion of resources for each of category, and dynamically adjusts the reservedratio by monitoring the pending requests and estimating release patterns ofrunning jobs. The results demonstrate DRESS significantly reduces thecompletion time for one category, up to 76.1% in our experiments, and in themeanwhile, maintains a stable overall system performance.

DRESS: Dynamic RESource-reservation Scheme for Congested Data-intensive Computing Platforms