Deep learning workloads are common in today's production clusters due to the proliferation
of deep learning driven AI services (eg, speech recognition, machine translation). A deep …

Estimates of task runtime, disk space usage, and memory consumption, are commonly used
by scheduling and resource provisioning algorithms to support efficient and reliable …

Queueing delays are ubiquitous in many domains, including computer systems, service
systems, communication networks, supply chains, and transportation. Queueing and …

Size-based scheduling with aging has been recognized as an effective approach to
guarantee fairness and near-optimal system response times. We present HFSP, a scheduler …

Size-based schedulers have very desirable performance properties: optimal or near-optimal
response time can be coupled with strong fairness. Despite this, however, such systems are …

The problem of big data analytics is gaining increasing research interest because of the
rapid growth in the volume of data to be analyzed in various areas of science and …

Job scheduling plays an important role in improving the overall system performance in big
data processing frameworks. Simple job scheduling policies, such as Fair and FIFO …

Utilizing customers' service-time information, we study an easy-to-implement scheduling
policy with two priority classes. By carefully designing the classes, the two-class priority rule …

This work addresses the problem of scheduling user-defined analytic applications, which we
define as high-level compositions of frameworks, their components, and the logic necessary …

When scheduling single server systems, Shortest Remaining Processing Time (SRPT)
minimizes the number of jobs in the system at every point in time. However, a major …