%P 2022
%L uneatlantico3009
%R doi:10.3390/electronics11132022
%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.
%A N. R. Rajalakshmi
%A Ankur Dumka
%A Manoj Kumar
%A Rajesh Singh
%A Anita Gehlot
%A Shaik Vaseem Akram
%A Divya Anand
%A Dalia H. Elkamchouchi
%A Irene Delgado Noya
%J Electronics
%K data parallel task; virtual machine; cloud data center; cost optimization model; concurrent computation
%N 13
%D 2022
%T A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud
%V 11