Paper Info
Title
WebIDE Cloud Server Resource Allocation with Task Pre-Scheduling in IOT Application Development
Abstract
WebIDE is leveraged for IOT applications development, which could adapt to the rapid growth of IOT applications and meanwhile facilitate the rapid development. Resource allocation is of vital significance in the WebIDE cloud service system. Existing resource allocation approaches may encounter issues such as unbalanced resource assignments, which could lead to the reduced system resource utilization or extended system response time. The fundamental reason behind it lies in the fact that existing ones are typically on the basis of predetermined resource demands for each task, and not applicable to the case with dynamic and unknown resource demands. Thus, in this paper, we propose a novel approach based on task pre-scheduling, which schedules beforehand each task into a virtual machine local task queue by means of demand prediction. Firstly, all tasks are classified, based on the execution state, execution operations and cloud server resource requirements. Secondly, the grouped tasks are mapped to different system states, with the Markov state transition probability matrix leveraged to model the transition probability between tasks, followed by the prediction model constructed. Finally, integrating both task pre-scheduling and ant colony algorithm, cloud server resource allocation is carried out. Simulation results show that the task prediction model could significantly not only reduce the task response time, but also improve the cloud server resource utilization.
Year
DOI
Venue
2019
10.1109/ICII.2019.00054
2019 IEEE International Conference on Industrial Internet (ICII)
Keywords
DocType
ISBN
Cloud server resource allocation, Task pre scheduling, Markov state transition probability matrix, IOT Application development
Conference
978-1-7281-2978-5
Citations 
PageRank 
References 
0
0.34
15
Authors
4
Name
Order
Citations
PageRank
Huaijun Wang12013.02
Junhuai Li23916.44
Jubo Tian300.34
Kan Wang400.34