Increasing scale and the need for rapid response to changing requirements are hard to meet
with current monolithic cluster scheduler architectures. This restricts the rate at which new …

We found that interactive services at Bing have highly variable datacenter-side processing
latencies because their processing consists of many sequential stages, parallelization …

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 (ie …

This paper presents Natjam, a system that supports arbitrary job priorities, hard real-time
scheduling, and efficient preemption for Mapreduce clusters that are resource-constrained …

Map-Reduce frameworks such as Hadoop have built-in fault-tolerance mechanisms that
allow jobs to run to completion even in the presence of certain faults. However, these jobs …

The popularity of cloud-based interactive computing services (eg, virtual desktops) brings
new management challenges. Each interactive user leaves abundant but fluctuating …

MapReduce has been widely used as a Big Data processing platform. As it gets popular, its
scheduling becomes increasingly important. In particular, since many MapReduce …

Existing cloud provisioning schemes allocate resources to batch processing systems at
deployment time and only change this allocation at run-time due to unexpected events such …

As a popular programming model in cloud-based data processing environment, MapReduce
and its open source implementation Hadoop, are widely applied both in industry and …

Tetrisched is a new scheduler that explicitly considers both job-specific preferences and
estimated job runtimes in its allocation of resources. Combined, this information allows …