%0 Journal Article
%@ 2079-9292
%A Rajalakshmi, N. R.
%A Dumka, Ankur
%A Kumar, Manoj
%A Singh, Rajesh
%A Gehlot, Anita
%A Akram, Shaik Vaseem
%A Anand, Divya
%A Elkamchouchi, Dalia H.
%A Delgado Noya, Irene
%D 2022
%F uneatlantico:3009
%J Electronics
%K data parallel task; virtual machine; cloud data center; cost optimization model; concurrent computation
%N 13
%P 2022
%T A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud
%U http://repositorio.uneatlantico.es/id/eprint/3009/
%V 11
%X 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.