A critical performance challenge in distributed scientific workflows is coordinating tasks and
data flows on distributed resources. To guide these decisions, this paper introduces data …

The emergence of multiple resource management systems, such as SLURM and
Kubernetes, for different computational purposes has led to a desire to support a single …

As user-interactive applications in the cloud transition from monolithic services to agile
microservice architectures, efficient resource management becomes a key challenge. The …

Interdisciplinary workflows are evolving to accom-modate the growing resource diversity and
parallelism in modern computing systems. The necessity of integrating various components …

The scientific computing paradigm has transitioned from compute-intensive to I/O-intensive
and memory-intensive in the past decade, especially when data-driven science has become …

A variety of hardware architectures, both CPUs and GPUs, are used today to build
supercomputers and parallel clusters. Often times, users can choose which hardware …