Exploring the Potential of using Power as a First Class Parameter for Resource Allocation in Apache Mesos Managed Clouds
Pradyumna Kaushik, Srinidhi Raghavendra, Madhusudhan Govindaraju, Devesh Tiwari · 2020
We propose a resource allocation policy that uses (a) Power as a first class parameter as an indicator of the computational intensity of a task and its potential impact on peak power draw, and (b) Power Tolerance as an indicator of a task's sensitivity towards degradation of performance as a result of resource contention. Through experimentation and analysis, we present coarse-grained and fine-grained Power Tolerance assignment methods that can be employed to make smarter peak power performance trade-offs. Our experiments show that (a) cloud operators can benefit from a uniform workload-wide setting of Power Tolerance to achieve significant reduction in peak power consumption, (b) fine-grained Power Tolerance assignment methods show potential in making smarter peak power and performance trade-offs.