Neeraja J. Yadwadkar

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Straggler tasks continue to be a major hurdle in achieving faster completion of data intensive applications running on modern data-processing frameworks. Existing straggler mitigation techniques are inefficient due to their reactive and replicative nature -- they rely on a wait-speculate-re-execute mechanism, thus leading to delayed straggler detection and(More)
An understanding of application I/O access patterns is useful in several situations. First, gaining insight into what applications are doing with their data at a semantic level helps in designing efficient storage systems. Second, it helps create benchmarks that mimic realistic application behavior closely. Third, it enables autonomic systems as the(More)
Users of cloud services are presented with a bewildering choice of VM types and the choice of VM can have significant implications on performance and cost. In this paper we address the fundamental problem of <i>accurately</i> and <i>economically</i> choosing the <i>best VM</i> for a given <i>workload</i> and <i>user goals.</i> To address the problem of(More)
Parallel processing frameworks (Dean and Ghemawat, 2004) accelerate jobs by breaking them into tasks that execute in parallel. However, slow running or straggler tasks can run up to 8 times slower than the median task on a production cluster (Ananthanarayanan et al., 2013), leading to delayed job completion and inefficient use of resources. Existing(More)
Slow running or straggler tasks in distributed processing frameworks [1, 2] can be 6 to 8 times slower than the median task in a job on a production cluster [3], despite existing mitigation techniques. This leads to extended job completion times, inefficient use of resources, and increased costs. Recently, proactive straggler avoidance techniques [4] have(More)
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