Network operators and researchers constantly search for platforms to evaluate future
deployments and test new research ideas. When experimenting, they usually face …

Coarse-grained reconfigurable architecture (CGRA) has become a promising candidate for
data-intensive computing due to its flexibility and high energy efficiency. CGRA compilers …

Co-location, which deploys long running applications and batch-processing applications in
the same computing cluster, has become a promising way to improve resource utility for …

Task scheduling algorithms based on reinforce learning (RL) have been important methods
with which to improve the performance of cloud platforms; however, due to the dynamics and …

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 …

Multi-Chiplet integrated systems for high-performance computing with dedicated CNN
accelerators are highly demanded due to ever-increasing AI-related training and inferencing …

We provide a highly-efficient solution to the classical problem of scheduling task graphs
corresponding to complex applications on distributed computing systems. A number of …

The significant energy consumption within data centers is an essential contributor to global
energy consumption and carbon emissions. Therefore, reducing energy consumption and …

To address the limitation of a DRAM-based single-tier in satisfying the comprehensive
demands of main memory, multi-tiered memory systems are gaining widespread adoption …

Server load levels affect the performance of cloud task execution, which is rooted in the
impact of server performance on cloud task execution. Traditional cloud task scheduling …