Jonathan Yaniv

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We consider a market-based resource allocation model for batch jobs in cloud computing clusters. In our model, we incorporate the importance of the due date of a job rather than the number of servers allocated to it at any given time. Each batch job is characterized by the work volume of total computing units (e.g., CPU hours) along with a bound on maximum(More)
We introduce a novel pricing and resource allocation approach for batch jobs on cloud systems. In our economic model, users submit jobs with a value function that specifies willingness to pay as a function of job due dates. The cloud provider in response allocates a subset of these jobs, taking into advantage the flexibility of allocating resources to jobs(More)
We study online mechanisms for preemptive scheduling with deadlines, with the goal of maximizing the total value of completed jobs. This problem is fundamental to deadline-aware cloud scheduling, but there are strong lower bounds even for the algorithmic problem without incentive constraints. However, these lower bounds can be circumvented under the natural(More)
We consider mechanisms for online deadline-aware scheduling in large computing clusters. Batch jobs that run on such clusters often require guarantees on their completion time (i.e., deadlines). However, most existing scheduling systems implement fair-share resource allocation between users, an approach that ignores heterogeneity in job requirements and may(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)
This paper presents a novel algorithm for scheduling big data jobs on large compute clusters. In our model, each job is represented by a DAG consisting of several stages linked by precedence constraints. The resource allocation per stage is malleable, in the sense that the processing time of a stage depends on the resources allocated to it (the dependency(More)
We consider mechanisms for online deadline-aware scheduling in large computing clusters. Batch jobs that run on such clusters often require guarantees on their completion time (i.e., deadlines). However, most existing scheduling systems implement fair-share resource allocation between users, an approach that ignores heterogeneity in job requirements and may(More)
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