Abstract
Data centers are now used as the underlying infrastructure of many modern commercial operations, powering both large Internet services and a growing number of data-intensive scientific applications. The tasks in these applications always consist of rich and complex flows which require different resources at different time slots. The existing data center scheduling frameworks are however base on either task- or flow-level metrics. This simplifies the design and deployment but hardly unleashes the potentials of obtaining low task completion time for delay-sensitive applications.
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