Deadline based scheduling for data-intensive applications in clouds

Xiong, Fei; Yeliang, Cang; Zhu, Lipeng; Hu, Bin; Deng, Song; Wang, Dong

doi:10.1016/s1005-8885(16)60064-x

Cited by 5 publications

(1 citation statement)

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“…The experiment is performed on the assignment of the task to the multi-core virtual resources. In [36,37], the authors assigned the data-intensive job to a set of processors or clusters based on their speed and capacity to complete the task within the deadline. They considered the communication delay and data access latency to calculate the completion time of the task.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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

et al. 2022

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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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Section: Experimental Evaluationmentioning

confidence: 99%