Resource allocation with Non-deterministic demands and profits

Nan Hu, Diego Pizzocaro, Matthew P. Johnson, Thomas Laporta, Alun D. Preece

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Support for intelligent and autonomous resource management is one key factor to the success of modern sensor network systems. The limited resources, such as exhaustible battery life, moderate processing ability and finite bandwidth, restrict the system's ability to serve multiple users simultaneously. It always happens that only a subset of tasks is selected with the goal of maximizing total profit. Besides, because of uncertain factors like unreliable wireless medium or variable quality of sensor outputs, it is not practical to assume that both demands and profits of tasks are deterministic and known a priori, both of which may be stochastic following certain distributions. In this paper, we model this resource allocation challenge as a stochastic knapsack problem. We study a specific case in which both demands and profits follow normal distributions, which are then extended to Poisson and Binomial variables. A couple of tunable parameters are introduced to configure two probabilities: one limits the capacity overflow rate with which the combined demand is allowed to exceed the available supply, and the other sets the minimum chance at which expected profit is required to be achieved. We define relative values for random variables in given conditions, and utilize them to search for the best resource allocation solutions. We propose heuristics with different optimality/ efficiency tradeoffs, and find that our algorithms run relatively fast and provide results considerably close to the optimum.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013
Pages145-153
Number of pages9
DOIs
StatePublished - Dec 1 2013
Event10th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013 - Hangzhou, China
Duration: Oct 14 2013Oct 16 2013

Publication series

NameProceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013

Other

Other10th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013
CountryChina
CityHangzhou
Period10/14/1310/16/13

Fingerprint

Resource allocation
Profitability
Normal distribution
Random variables
Sensor networks
Bandwidth
Sensors
Processing

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Hu, N., Pizzocaro, D., Johnson, M. P., Laporta, T., & Preece, A. D. (2013). Resource allocation with Non-deterministic demands and profits. In Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013 (pp. 145-153). [6680234] (Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013). https://doi.org/10.1109/MASS.2013.61
Hu, Nan ; Pizzocaro, Diego ; Johnson, Matthew P. ; Laporta, Thomas ; Preece, Alun D. / Resource allocation with Non-deterministic demands and profits. Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013. 2013. pp. 145-153 (Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013).
@inproceedings{a0a4f756f1a643cf9fe952330d1cd0fd,
title = "Resource allocation with Non-deterministic demands and profits",
abstract = "Support for intelligent and autonomous resource management is one key factor to the success of modern sensor network systems. The limited resources, such as exhaustible battery life, moderate processing ability and finite bandwidth, restrict the system's ability to serve multiple users simultaneously. It always happens that only a subset of tasks is selected with the goal of maximizing total profit. Besides, because of uncertain factors like unreliable wireless medium or variable quality of sensor outputs, it is not practical to assume that both demands and profits of tasks are deterministic and known a priori, both of which may be stochastic following certain distributions. In this paper, we model this resource allocation challenge as a stochastic knapsack problem. We study a specific case in which both demands and profits follow normal distributions, which are then extended to Poisson and Binomial variables. A couple of tunable parameters are introduced to configure two probabilities: one limits the capacity overflow rate with which the combined demand is allowed to exceed the available supply, and the other sets the minimum chance at which expected profit is required to be achieved. We define relative values for random variables in given conditions, and utilize them to search for the best resource allocation solutions. We propose heuristics with different optimality/ efficiency tradeoffs, and find that our algorithms run relatively fast and provide results considerably close to the optimum.",
author = "Nan Hu and Diego Pizzocaro and Johnson, {Matthew P.} and Thomas Laporta and Preece, {Alun D.}",
year = "2013",
month = "12",
day = "1",
doi = "10.1109/MASS.2013.61",
language = "English (US)",
isbn = "9780768551043",
series = "Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013",
pages = "145--153",
booktitle = "Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013",

}

Hu, N, Pizzocaro, D, Johnson, MP, Laporta, T & Preece, AD 2013, Resource allocation with Non-deterministic demands and profits. in Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013., 6680234, Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013, pp. 145-153, 10th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013, Hangzhou, China, 10/14/13. https://doi.org/10.1109/MASS.2013.61

Resource allocation with Non-deterministic demands and profits. / Hu, Nan; Pizzocaro, Diego; Johnson, Matthew P.; Laporta, Thomas; Preece, Alun D.

Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013. 2013. p. 145-153 6680234 (Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Resource allocation with Non-deterministic demands and profits

AU - Hu, Nan

AU - Pizzocaro, Diego

AU - Johnson, Matthew P.

AU - Laporta, Thomas

AU - Preece, Alun D.

PY - 2013/12/1

Y1 - 2013/12/1

N2 - Support for intelligent and autonomous resource management is one key factor to the success of modern sensor network systems. The limited resources, such as exhaustible battery life, moderate processing ability and finite bandwidth, restrict the system's ability to serve multiple users simultaneously. It always happens that only a subset of tasks is selected with the goal of maximizing total profit. Besides, because of uncertain factors like unreliable wireless medium or variable quality of sensor outputs, it is not practical to assume that both demands and profits of tasks are deterministic and known a priori, both of which may be stochastic following certain distributions. In this paper, we model this resource allocation challenge as a stochastic knapsack problem. We study a specific case in which both demands and profits follow normal distributions, which are then extended to Poisson and Binomial variables. A couple of tunable parameters are introduced to configure two probabilities: one limits the capacity overflow rate with which the combined demand is allowed to exceed the available supply, and the other sets the minimum chance at which expected profit is required to be achieved. We define relative values for random variables in given conditions, and utilize them to search for the best resource allocation solutions. We propose heuristics with different optimality/ efficiency tradeoffs, and find that our algorithms run relatively fast and provide results considerably close to the optimum.

AB - Support for intelligent and autonomous resource management is one key factor to the success of modern sensor network systems. The limited resources, such as exhaustible battery life, moderate processing ability and finite bandwidth, restrict the system's ability to serve multiple users simultaneously. It always happens that only a subset of tasks is selected with the goal of maximizing total profit. Besides, because of uncertain factors like unreliable wireless medium or variable quality of sensor outputs, it is not practical to assume that both demands and profits of tasks are deterministic and known a priori, both of which may be stochastic following certain distributions. In this paper, we model this resource allocation challenge as a stochastic knapsack problem. We study a specific case in which both demands and profits follow normal distributions, which are then extended to Poisson and Binomial variables. A couple of tunable parameters are introduced to configure two probabilities: one limits the capacity overflow rate with which the combined demand is allowed to exceed the available supply, and the other sets the minimum chance at which expected profit is required to be achieved. We define relative values for random variables in given conditions, and utilize them to search for the best resource allocation solutions. We propose heuristics with different optimality/ efficiency tradeoffs, and find that our algorithms run relatively fast and provide results considerably close to the optimum.

UR - http://www.scopus.com/inward/record.url?scp=84893259521&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893259521&partnerID=8YFLogxK

U2 - 10.1109/MASS.2013.61

DO - 10.1109/MASS.2013.61

M3 - Conference contribution

AN - SCOPUS:84893259521

SN - 9780768551043

T3 - Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013

SP - 145

EP - 153

BT - Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013

ER -

Hu N, Pizzocaro D, Johnson MP, Laporta T, Preece AD. Resource allocation with Non-deterministic demands and profits. In Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013. 2013. p. 145-153. 6680234. (Proceedings - IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems, MASS 2013). https://doi.org/10.1109/MASS.2013.61