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Matías Hirsch
Matías Hirsch
ISISTAN-UNCPBA-CONICET
Verified email at isistan.unicen.edu.ar
Title
Cited by
Cited by
Year
Augmenting computing capabilities at the edge by jointly exploiting mobile devices: A survey
M Hirsch, C Mateos, A Zunino
Future Generation Computer Systems 88, 644-662, 2018
472018
A two-phase energy-aware scheduling approach for cpu-intensive jobs in mobile grids
M Hirsch, JM Rodriguez, C Mateos, A Zunino
Journal of Grid Computing 15, 55-80, 2017
412017
Battery-aware centralized schedulers for cpu-bound jobs in mobile grids
M Hirsch, JM Rodriguez, A Zunino, C Mateos
Pervasive and Mobile Computing 29, 73-94, 2016
362016
DewSim: A trace‐driven toolkit for simulating mobile device clusters in Dew computing environments
M Hirsch, C Mateos, JM Rodriguez, A Zunino
Software: Practice and Experience 50 (5), 688-718, 2020
282020
A task execution scheme for dew computing with state-of-the-art smartphones
M Hirsch, C Mateos, A Zunino, TA Majchrzak, TM Grønli, H Kaindl
Electronics 10 (16), 2006, 2021
212021
Evaluating the performance of three popular Web mapping libraries: A case study using Argentina’s life quality index
A Zunino, G Velázquez, JP Celemín, C Mateos, M Hirsch, JM Rodriguez
ISPRS International Journal of Geo-Information 9 (10), 563, 2020
162020
Towards integrating mobile devices into dew computing: a model for hour-wise prediction of energy availability
M Longo, M Hirsch, C Mateos, A Zunino
Information 10 (3), 86, 2019
162019
Principales enfermedades del cultivo de maíz en las últimas campañas y su manejo. 7 p
L Couretot, L Parisi, M Hirsch, ML Suarez, G Magnone, G Ferraris
Disponible en línea: https://inta. gob. ar/sites/default/files/scripttmp …, 2013
152013
EasyFJP: Providing hybrid parallelism as a concern for divide and conquer Java applications
C Mateos, A Zunino, M Hirsch
Computer Science and Information Systems 10 (3), 1129-1163, 2013
132013
Spotting and removing WSDL anti-pattern root causes in code-first web services using NLP techniques: a thorough validation of impact on service discoverability
M Hirsch, A Rodriguez, JM Rodriguez, C Mateos, A Zunino
Computer Standards & Interfaces 56, 116-133, 2018
122018
A simulation-based performance evaluation of heuristics for dew computing
M Hirsch, C Mateos, A Zunino, TA Majchrzak, TM Grønli, H Kaindl
112021
A performance comparison of data-aware heuristics for scheduling jobs in mobile grids
M Hirsch, C Mateos, JM Rodriguez, A Zunino, Y Garí, DA Monge
2017 XLIII Latin American Computer Conference (CLEI), 1-8, 2017
112017
A platform for automating battery-driven batch benchmarking and profiling of Android-based mobile devices
M Hirsch, C Mateos, A Zunino, J Toloza
Simulation Modelling Practice and Theory 109, 102266, 2021
102021
Enhancing the BYG gridification tool with state-of-the-art Grid scheduling mechanisms and explicit tuning support
C Mateos, A Zunino, M Hirsch, M Fernández
Advances in Engineering Software 43 (1), 27-43, 2012
102012
A software tool for semi-automatic gridification of resource-intensive java bytecodes and its application to ray tracing and sequence alignment
C Mateos, A Zunino, M Hirsch, M Fernández, M Campo
Advances in Engineering Software 42 (4), 172-186, 2011
102011
New heuristics for scheduling and distributing jobs under hybrid dew computing environments
P Sanabria, TF Tapia, A Neyem, JI Benedetto, M Hirsch, C Mateos, ...
Wireless Communications and Mobile Computing 2021 (1), 8899660, 2021
82021
Motrol 2.0: A Dew-oriented hardware/software platform for batch-benchmarking smartphones
C Mateos, M Hirsch, J Toloza, A Zunino
2021 IEEE 45th Annual Computers, Software, and Applications Conference …, 2021
42021
Connection-Aware Heuristics for Scheduling and Distributing Jobs under Dynamic Dew Computing Environments
P Sanabria, S Montoya, A Neyem, R Toro Icarte, M Hirsch, C Mateos
Applied Sciences 14 (8), 3206, 2024
32024
Speeding up Smartphone-Based Dew Computing: In Vivo Experiments Setup Via an Evolutionary Algorithm
V Yannibelli, M Hirsch, J Toloza, TA Majchrzak, A Zunino, C Mateos
Sensors 23 (3), 1388, 2023
32023
LiveDewStream: A stream processing platform for running in-lab distributed deep learning inferences on smartphone clusters at the edge
C Mateos, M Hirsch, JM Toloza, A Zunino
SoftwareX 20, 101268, 2022
22022
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