While recent deep learning workload schedulers exhibit excellent performance, it is arduous
to deploy them in practice due to some substantial defects, including inflexible intrusive …

Traditional systems for allocating finite cluster resources among competing jobs have either
aimed at providing fairness, relied on users to specify their resource requirements, or have …

Modern cloud platforms allow customers to flexibly allocate or release computing resources.
One crucial scenario is how to drive existing VMs to a specific state by a given deadline in a …

Many data analytic systems have adopted a newly emerging compute resource, serverless
(SL), to handle data analytics queries in a timely and cost-efficient manner, ie, serverless …

Big data frameworks such as Storm, Spark and Hadoop are widely deployed in commercial
and research applications, the energy consumption of cloud data centers that support big …

In the cloud environment, different kinds of jobs (Flink, PyTorch, TensorFlow, AI-Serving) are
running in the same cluster with different service-level agreements (SLA). To manage large …

Resource provisioning is vital in large-scale, geographically distributed, and hierarchically
organized infrastructures, and, at the same time, it represents one of the stiffest challenges in …

For decades, database research has focused on optimizing performance under fixed
resources. As more and more database applications move to the public cloud, we argue that …

Given the wide variety of cloud computing resources for creating high‐performance
computer clusters and their complex performance relationship with applications, finding the …

Abstract Directed Acyclic Graph (DAG) workflows are widely used for large-scale data
analytics in cluster-based distributed computing systems. The performance model for a DAG …