Datacenter-scale computing for analytics workloads is increasingly common. High
operational costs force heterogeneous applications to share cluster resources for achieving …

Nowadays, there is an increasing number of workloads, ie data serving, analytics, AI, HPC
workloads, etc., executed on the Cloud. Although multi-tenancy has gained a lot of attention …

A centralized scheduler can become a bottleneck for placing the tasks of a many-task
application on heterogeneous cloud resources. We have previously demonstrated that a …

Recent trends show that cloud computing is growing to span more and more globally
distributed data centers. For geo-distributed data centers, there is an increasing need for …

Welcome to the special issue of Future Generation Computer System (FGCS) journal. This
special issue compiles a number of excellent technical contributions that significantly …

Python-written data analytics applications can be modeled as and compiled into a directed
acyclic graph (DAG) based workflow, where the nodes are fine-grained tasks and the edges …

Large-scale data analytics frameworks are shifting towards shorter task durations and larger
degrees of parallelism to provide low latency. Scheduling highly parallel jobs that complete …

In spite of many shuffle-heavy jobs in current commercial data-parallel clusters, few previous
studies have considered the network traffic in the shuffle phase, which contains a large …

Cloud computing aims to give users virtually unlimited pay-per-use computing resources
without the burden of managing the underlying infrastructure. We present a new job …

Multiprocessor scheduling of hard real-time tasks modeled by directed acyclic graphs
(DAGs) exploits the inherent parallelism presented by the model. For DAG tasks, a node …