Intel Corporation

Subramony Sesha

Mrittika Ganguli

Ananth S Narayan

Archana Patni

Muralidharan Sundararajan

Alok Prakash

Ananth Sankaranarayanan

Arkadiusz Chylinski

Rajneesh Bhardwaj

Sunil Raghavan

Gilbert Hyatt

Technologies for contention-aware cloud compute scheduling include a number of compute nodes in a cloud computing cluster and a cloud controller. Each compute node collects performance data indicative of cache contention on the compute node, for example, cache misses per thousand instructions. Each compute node determines a contention score as a function of the performance data and stores the contention score in a cloud state database. In response to a request for a new virtual machine, the cloud controller receives contention scores for the compute nodes and selects a compute node based on the contention score. The cloud controller schedules the new virtual machine on the selected compute node. The contention score may include a contention metric and a contention score level indicative of the contention metric. The contention score level may be determined by comparing the contention metric to a number of thresholds. Other embodiments are described and claimed.

Cloud compute scheduling using a heuristic contention model

A method comprising is described. The method includes receiving a virtual machine and a service level objective compute consumption metrics (SLO) parameters from a cloud controller, mapping the SLO parameters to resources at the compute node, monitoring utilization and performance of the virtual machine and dynamically adjusting performance of the virtual machine based on monitored performance data.

CPU overprovisioning and cloud compute workload scheduling mechanism

Growing community of inventors

Bengaluru, India

Subramony Sesha