eprintid: 3009 rev_number: 12 eprint_status: archive userid: 2 dir: disk0/00/00/30/09 datestamp: 2022-07-27 23:30:10 lastmod: 2023-07-11 23:30:39 status_changed: 2022-07-27 23:30:10 type: article metadata_visibility: show creators_name: Rajalakshmi, N. R. creators_name: Dumka, Ankur creators_name: Kumar, Manoj creators_name: Singh, Rajesh creators_name: Gehlot, Anita creators_name: Akram, Shaik Vaseem creators_name: Anand, Divya creators_name: Elkamchouchi, Dalia H. creators_name: Delgado Noya, Irene creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: creators_id: divya.anand@uneatlantico.es creators_id: creators_id: irene.delgado@uneatlantico.es title: A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica full_text_status: public keywords: data parallel task; virtual machine; cloud data center; cost optimization model; concurrent computation abstract: Large-scale distributed systems have the advantages of high processing speeds and large communication bandwidths over the network. The processing of huge real-world data within a time constraint becomes tricky, due to the complexity of data parallel task scheduling in a time constrained environment. This paper proposes data parallel task scheduling in cloud to address the minimization of cost and time constraints. By running concurrent executions of tasks on multi-core cloud resources, the number of parallel executions could be increased correspondingly, thereby, finishing the task within the deadline is possible. A mathematical model is developed here to minimize the operational cost of data parallel tasks by feasibly assigning a load to each virtual machine in the cloud data center. This work experiments with a machine learning model that is replicated on the multi-core cloud heterogeneous resources to execute different input data concurrently to accomplish distributive learning. The outcome of concurrent execution of data-intensive tasks on different parts of the input dataset gives better solutions in terms of processing the task by the deadline at optimized cost. date: 2022-06 publication: Electronics volume: 11 number: 13 pagerange: 2022 id_number: doi:10.3390/electronics11132022 refereed: TRUE issn: 2079-9292 official_url: http://doi.org/10.3390/electronics11132022 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Abierto Inglés Large-scale distributed systems have the advantages of high processing speeds and large communication bandwidths over the network. The processing of huge real-world data within a time constraint becomes tricky, due to the complexity of data parallel task scheduling in a time constrained environment. This paper proposes data parallel task scheduling in cloud to address the minimization of cost and time constraints. By running concurrent executions of tasks on multi-core cloud resources, the number of parallel executions could be increased correspondingly, thereby, finishing the task within the deadline is possible. A mathematical model is developed here to minimize the operational cost of data parallel tasks by feasibly assigning a load to each virtual machine in the cloud data center. This work experiments with a machine learning model that is replicated on the multi-core cloud heterogeneous resources to execute different input data concurrently to accomplish distributive learning. The outcome of concurrent execution of data-intensive tasks on different parts of the input dataset gives better solutions in terms of processing the task by the deadline at optimized cost. metadata Rajalakshmi, N. R.; Dumka, Ankur; Kumar, Manoj; Singh, Rajesh; Gehlot, Anita; Akram, Shaik Vaseem; Anand, Divya; Elkamchouchi, Dalia H. y Delgado Noya, Irene mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, irene.delgado@uneatlantico.es (2022) A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud. Electronics, 11 (13). p. 2022. ISSN 2079-9292 document_url: http://repositorio.uneatlantico.es/id/eprint/3009/1/electronics-11-02022-v2%20%281%29.pdf