Generative and multi-phase learning for computer systems optimization
Machine learning and artificial intelligence are invaluable for computer systems
optimization: as computer systems expose more resources for management, ML/AI is …
optimization: as computer systems expose more resources for management, ML/AI is …
A probabilistic graphical model-based approach for minimizing energy under performance constraints
In many deployments, computer systems are underutilized--meaning that applications have
performance requirements that demand less than full system capacity. Ideally, we would …
performance requirements that demand less than full system capacity. Ideally, we would …
Caloree: Learning control for predictable latency and low energy
Many modern computing systems must provide reliable latency with minimal energy. Two
central challenges arise when allocating system resources to meet these conflicting …
central challenges arise when allocating system resources to meet these conflicting …
Learning transfer-based adaptive energy minimization in embedded systems
Embedded systems execute applications with varying performance requirements. These
applications exercise the hardware differently depending on the computation task …
applications exercise the hardware differently depending on the computation task …
Machine learning for power, energy, and thermal management on multicore processors: A survey
Due to the high integration density and roadblock of voltage scaling, modern multicore
processors experience higher power densities than previous technology scaling nodes …
processors experience higher power densities than previous technology scaling nodes …
Managing power consumption and performance of computing systems using reinforcement learning
Electrical power management in large-scale IT systems such as commercial datacenters is
an application area of rapidly growing interest from both an economic and ecological …
an application area of rapidly growing interest from both an economic and ecological …
Energy-efficient application resource scheduling using machine learning classifiers
Resource scheduling in high performance computing (HPC) usually aims to minimize
application runtime rather than optimize for energy efficiency. Most existing research on …
application runtime rather than optimize for energy efficiency. Most existing research on …
STAFF: Online learning with stabilized adaptive forgetting factor and feature selection algorithm
Dynamic resource management techniques rely on power consumption and performance
models to optimize the operating frequency and utilization of processing elements, such as …
models to optimize the operating frequency and utilization of processing elements, such as …
Runtime workload behavior prediction using statistical metric modeling with application to dynamic power management
R Sarikaya, C Isci… - … symposium on workload …, 2010 - ieeexplore.ieee.org
Adaptive computing systems rely on accurate predictions of workload behavior to
understand and respond to the dynamically-varying application characteristics. In this study …
understand and respond to the dynamically-varying application characteristics. In this study …
Machine learning basics
The idea of making intelligent, sentient, and self-aware machines is not something that
suddenly came into existence in the last few years. In fact a lot of lore from Greek mythology …
suddenly came into existence in the last few years. In fact a lot of lore from Greek mythology …