A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud.

What is it about?

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.

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Why is it important?

The development of software is an enormous sector of the global economy, with its own phase of evolution and a significant influence on the digital economy as a whole. The IT sector is a real engine of growth in the world economy, which means that its success is important. The escalation of computing services has been enhanced recently. Its attractiveness mainly stems from the release of IT resources, such as the transformation of capital IT expenditure into economic resources. A machine learning model that is replicated on the multi-core cloud heterogeneous resources to execute different input data concurrently to accomplish distributive learning.

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