Subru Krishnan

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The continuous shift towards data-driven approaches to business, and a growing attention to improving return on investments (ROI) for cluster infrastructures is generating new challenges for big-data frameworks. Systems originally designed for big batch jobs now handle an increasingly complex mix of computations. Moreover, they are expected to guarantee(More)
Modern resource management frameworks for largescale analytics leave unresolved the problematic tension between high cluster utilization and job’s performance predictability—respectively coveted by operators and users. We address this in Morpheus, a new system that: 1) codifies implicit user expectations as explicit Service Level Objectives (SLOs), inferred(More)
Query Optimization focuses on finding the best query execution plan, given fixed hardware resources. In BigData settings, both pay-as-you-go clouds and on-prem shared clusters, a complementary challenge emerges: Resource Optimization: find the best hardware resources, given an execution plan. In this world, provisioning is almost instantaneous and(More)
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