TY  - JOUR
TI  - A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud
ID  - uneatlantico3009
AV  - public
A1  - Rajalakshmi, N. R.
A1  - Dumka, Ankur
A1  - Kumar, Manoj
A1  - Singh, Rajesh
A1  - Gehlot, Anita
A1  - Akram, Shaik Vaseem
A1  - Anand, Divya
A1  - Elkamchouchi, Dalia H.
A1  - Delgado Noya, Irene
KW  - data parallel task; virtual machine; cloud data center; cost optimization model; concurrent computation
Y1  - 2022/06//
JF  - Electronics
N2  - 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.
IS  - 13
VL  - 11
SN  - 2079-9292
UR  - http://doi.org/10.3390/electronics11132022
ER  -