A neural network framework for optimizing parallel computing in cloud servers

doi:10.1016/j.sysarc.2024.103131

A neural network framework for optimizing parallel computing in cloud servers

2024-05-01

2

PRE

AI

F

Fábio Diniz Rossi

M

Marcelo Caggiani Luizelli

DOI：10.1016/j.sysarc.2024.103131

Original

Original request for help

Save

Abstract

En 中文

Energy efficiency has become a major focus in optimizing hardware resource usage for Cloud servers. One approach widely employed to enhance the execution of parallel applications is thread-level parallelism (TLP) exploitation. This technique leverages multiple threads to improve computational efficiency and performance. However, the increasing heterogeneity of resources in cloud environments and the complexity of selecting the optimal configuration for each application pose a significant challenge to cloud users due to the massive number of possible configurations and the need to effectively harness TLP in diverse hardware setups to achieve optimal energy efficiency and performance. To address this challenge, we propose TLP-Allocator, an artificial neural network (ANN) optimization strategy that uses hardware and software metrics to build and train an ANN model. It predicts worker node and thread count combinations that provide optimal energy-delay product (EDP) results. In experiments using ten well-known applications on a private cloud with heterogeneous resources, we show that TLP-Allocator predicts combinations that yield EDP values close to the best achieved by an exhaustive search. It also improves the overall EDP by 38.2% compared to state-of-the-art workloads scheduling on cloud environments.

Keywords:

Parallel computing

Energy efficiency

Cloud computing

Artificial neural network

A neural network framework for optimizing parallel computing in cloud servers

Abstract

Journal

Organization