An Optimal Flow Admission and Routing Control Policy for Resource Constrained Networks
Overloaded network devices are becoming an increasing problem especially in resource limited networks with the continuous and rapid increase of wireless devices and the huge volume of data generated. Admission and routing control policy at a network device can be used to balance the goals of maximiz...
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doaj-9e23841d046b4a31a42cb2f81ce195d22020-11-25T04:07:29ZengMDPI AGSensors1424-82202020-11-01206566656610.3390/s20226566An Optimal Flow Admission and Routing Control Policy for Resource Constrained NetworksEssia Hamouda0J. H. Brown College of Business & Public Administration, California State University, San Bernardino, CA 92407, USAOverloaded network devices are becoming an increasing problem especially in resource limited networks with the continuous and rapid increase of wireless devices and the huge volume of data generated. Admission and routing control policy at a network device can be used to balance the goals of maximizing throughput and ensuring sufficient resources for high priority flows. In this paper we formulate the admission and routing control problem of two types of flows where one has a higher priority than the other as a Markov decision problem. We characterize the optimal admission and routing policy, and show that it is a state-dependent threshold type policy. Furthermore, we conduct extensive numerical experiments to gain more insight into the behavior of the optimal policy under different systems’ parameters. While dynamic programming can be used to solve such problems, the large size of the state space makes it untractable and too resource intensive to run on wireless devices. Therefore, we propose a fast heuristic that exploits the structure of the optimal policy. We empirically show that the heuristic performs very well with an average reward deviation of 1.4% from the optimal while being orders of magnitude faster than the optimal policy. We further generalize the heuristic for the general case of a system with <i>n</i> (<inline-formula><math display="inline"><semantics><mrow><mi>n</mi><mo>></mo><mn>2</mn></mrow></semantics></math></inline-formula>) types of flows.https://www.mdpi.com/1424-8220/20/22/6566wireless communicationperformance optimizationmarkov decision processenergy efficiencythreshold routingsensor networks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Essia Hamouda |
spellingShingle |
Essia Hamouda An Optimal Flow Admission and Routing Control Policy for Resource Constrained Networks Sensors wireless communication performance optimization markov decision process energy efficiency threshold routing sensor networks |
author_facet |
Essia Hamouda |
author_sort |
Essia Hamouda |
title |
An Optimal Flow Admission and Routing Control Policy for Resource Constrained Networks |
title_short |
An Optimal Flow Admission and Routing Control Policy for Resource Constrained Networks |
title_full |
An Optimal Flow Admission and Routing Control Policy for Resource Constrained Networks |
title_fullStr |
An Optimal Flow Admission and Routing Control Policy for Resource Constrained Networks |
title_full_unstemmed |
An Optimal Flow Admission and Routing Control Policy for Resource Constrained Networks |
title_sort |
optimal flow admission and routing control policy for resource constrained networks |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-11-01 |
description |
Overloaded network devices are becoming an increasing problem especially in resource limited networks with the continuous and rapid increase of wireless devices and the huge volume of data generated. Admission and routing control policy at a network device can be used to balance the goals of maximizing throughput and ensuring sufficient resources for high priority flows. In this paper we formulate the admission and routing control problem of two types of flows where one has a higher priority than the other as a Markov decision problem. We characterize the optimal admission and routing policy, and show that it is a state-dependent threshold type policy. Furthermore, we conduct extensive numerical experiments to gain more insight into the behavior of the optimal policy under different systems’ parameters. While dynamic programming can be used to solve such problems, the large size of the state space makes it untractable and too resource intensive to run on wireless devices. Therefore, we propose a fast heuristic that exploits the structure of the optimal policy. We empirically show that the heuristic performs very well with an average reward deviation of 1.4% from the optimal while being orders of magnitude faster than the optimal policy. We further generalize the heuristic for the general case of a system with <i>n</i> (<inline-formula><math display="inline"><semantics><mrow><mi>n</mi><mo>></mo><mn>2</mn></mrow></semantics></math></inline-formula>) types of flows. |
topic |
wireless communication performance optimization markov decision process energy efficiency threshold routing sensor networks |
url |
https://www.mdpi.com/1424-8220/20/22/6566 |
work_keys_str_mv |
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