Formulation of Control Strategies for IoT Task Scheduling
Prasantha Rao A1, G. Sekhar Reddy2, CH N Santhosh Kumar3, K.S. Reddy4
1Prasantha Rao A, Professor IT Dept., Anurag Group of Institutions, Hyderabad, India.
2G. Sekhar Reddy, Asst. Professor IT Dept., Anurag Group of Institutions, Hyderabad, India.
3CH.N.Santhosh Kumar, CSE Department, Anurag Engg. College, Kodada, India.
4K. S. Reddy, Hyderabad, India.
Manuscript received on 06 August 2019. | Revised Manuscript received on 11 August 2019. | Manuscript published on 30 September 2019. | PP: 7886-7890 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6560098319/2019©BEIESP | DOI: 10.35940/ijrte.C6560.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The various Internets of Things (IoT) application tasks are difficult to schedule due to heterogeneity properties of IoT. So an efficient algorithm is required that forms < task, processor> pair appropriately. This paper presents a more sensible model for varying execution times of tasks and deviation in task parameters for building a schedule is allowed. The system provides an adaptive learning mechanism called Expected Time Matrix ETM (i, j). When the environment of the system changes dynamically, the system learns and adapts itself to the new changes automatically, since the learning mechanism has been incorporated in the system. ETM (i, j) concepts allows the system to learn from past instances as well. The work is supported by simulations that highlight the viability of concepts proposed. The key objective of this paper is to present the developed scheduling algorithm that is self-configurable and dynamic
Keywords: Load Balancer, Task Model, Task Cluster, Self-Configurable, Cluster, Control Strategy, Dynamic Scheduler, Heterogeneity.
Scope of the Article: IoT