@article{uneatlantico3009,
           title = {A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud},
         journal = {Electronics},
            year = {2022},
          number = {13},
           pages = {2022},
          author = {N. R. Rajalakshmi and Ankur Dumka and Manoj Kumar and Rajesh Singh and Anita Gehlot and Shaik Vaseem Akram and Divya Anand and Dalia H. Elkamchouchi and Irene Delgado Noya},
          volume = {11},
           month = {Junio},
        keywords = {data parallel task; virtual machine; cloud data center; cost optimization model; concurrent computation},
             url = {http://repositorio.uneatlantico.es/id/eprint/3009/},
        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.}
}