ARED: automata-based runtime estimation for distributed systems using deep learning

Autor:	Yo-Sub Han, Hyunjoon Cheon, Chan Yeol Park, Jinseung Ryu, Jae-Cheol Ryou
Rok vydání:	2021
Předmět:	Schedule Source code Finite-state machine Artificial neural network Computer Networks and Communications Computer science business.industry media_common.quotation_subject Deep learning Distributed computing Automaton Task (computing) Software Artificial intelligence business media_common
Zdroj:	Cluster Computing.
ISSN:	1573-7543 1386-7857
Popis:	High-performance computers are used for computation-intensive tasks. It is essential for these systems to simultaneously execute several computation-intensive tasks for efficient and timely system utilization. Since typical tasks have a longer runtime, it is essential to determine the runtime of each task prior to execution and schedule them accordingly. We propose a method for predicting the runtime of MPI-based software. Initially, we analyze the source code of the software by translating the code to finite automata and measuring the state complexity. Next, the runtime of software is trained using a deep neural network (DNN) along with its state complexity. Herein, we propose three models based on DNN, statistics and their hybrid. DNN model is superior in comparison. Additionally, the adaptability of our method is demonstrated by showing that our method can adapt on new environment with 90% accuracy on various software.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5092810af150dc4f37424a6f37d0bb5b https://doi.org/10.1007/s10586-021-03272-w Zobrazit plný text záznamu Full text from SpringerLink