Map-reduce process algebra: a formalism to describe directed acyclic graph task-based jobs in parallel environments

Enrico Barbierato, Marco Gribaudo, Mauro Iacono

Risultato della ricerca: Contributo in libroContributo a convegno

Abstract

Cloud Computing has made possible flexible resources provisioning from an almost unlimited pool. This has created the opportunity to broaden the horizon of data that can be analyzed, allowing to support the so called Big Data Analytics applications. New programming paradigms, such as NoSQL queries and Map-Reduce applications, have emerged within frameworks such as Microsoft Azure, Hadoop and Apache Spark. In many cases, applications execute jobs that are split into stages, each one composed of tasks that can be run in parallel on many computational nodes. Directed acyclic graphs describe the precedence between stages, defining the execution rules and controlling the degree of parallelism. This work presents a Process Algebra dialect aimed at describing both jobs and execution environments. The proposed framework is then used to model and study standard parallel programming benchmarks, to demonstrate its applicability.
Lingua originaleEnglish
Titolo della pubblicazione ospiteLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pagine85-99
Numero di pagine15
Volume12023
DOI
Stato di pubblicazionePubblicato - 2020
Evento25th International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2019 - Moscow
Durata: 21 ott 201925 ott 2019

Convegno

Convegno25th International Conference on Analytical and Stochastic Modelling Techniques and Applications, ASMTA 2019
CittàMoscow
Periodo21/10/1925/10/19

Keywords

  • dagSim
  • Process algebra
  • Map-Reduce
  • Fork-join

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