In order to reduce power and energy costs, giant cloud providers now mix online and batch
jobs on the same cluster. Although the co-allocation of such jobs improves machine …

Toward ML-centric cloud platforms Page 1 50 COMMUNICATIONS OF THE ACM | FEBRUARY
2020 | VOL. 63 | NO. 2 contributed articles IMA GE B Y MARCEL CLEMENS resource …

Periodicity detection is a crucial step in time series tasks, including monitoring and
forecasting of metrics in many areas, such as IoT applications and self-driving database …

While the current literature typically focuses on load-balancing among multiple servers, in
this paper, we demonstrate the importance of load-balancing within a single machine …

Considering its unique operational and power consumption characteristics, internet data
center (IDC) has been intensively investigated as a promising candidate to provide flexibility …

Cluster schedulers routinely face data-parallel jobs with complex task dependencies
expressed as DAGs (directed acyclic graphs). Understanding DAG structures and runtime …

To increase utilization, datacenter schedulers often overcommit resources where the sum of
resources allocated to the tasks on a machine exceeds its physical capacity. Setting the right …

To mitigate climate change, we must reduce carbon emissions from hyperscale cloud
computing. We find that cloud compute servers cause the majority of emissions in a …

HPC workload analysis and resource consumption characteristics are the key to driving
better operation practices, system procurement decisions, and designing effective resource …

Datacenters are the principal electricity consumers for cloud computing that provide an IT
backbone for today's business and economy. Numerous studies suggest that most of the …