IEICE TRANSACTIONS on Fundamentals
An Efficient Optimization of Network Resource Allocations under Nonlinear Quality of Service Constraints
Summary :
We present an efficient method to optimize network resource allocations under nonlinear Quality of Service (QoS) constraints. We first propose a suite of generalized proportional allocation schemes that can be obtained by minimizing the information-theoretic function of relative entropy. We then optimize over the allocation parameters, which are usually design variables an engineer can directly vary, either for a particular user or for the worst-case user, under constraints that lower bound the allocated resources for all other users. Despite the nonlinearity in the objective and constraints, we show this suite of resource allocation optimization can be efficiently solved for global optimality through a convex optimization technique called geometric programming. This general method and its extensions are applicable to a wide array of resource allocation problems, including processor sharing, congestion control, admission control, and wireless network power control.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E88-A No.10 pp.2642-2646
- Publication Date
- 2005/10/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietfec/e88-a.10.2642
- Type of Manuscript
- Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
- Category
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Hakim BADIS, "An Efficient Optimization of Network Resource Allocations under Nonlinear Quality of Service Constraints" in IEICE TRANSACTIONS on Fundamentals,
vol. E88-A, no. 10, pp. 2642-2646, October 2005, doi: 10.1093/ietfec/e88-a.10.2642.
Abstract: We present an efficient method to optimize network resource allocations under nonlinear Quality of Service (QoS) constraints. We first propose a suite of generalized proportional allocation schemes that can be obtained by minimizing the information-theoretic function of relative entropy. We then optimize over the allocation parameters, which are usually design variables an engineer can directly vary, either for a particular user or for the worst-case user, under constraints that lower bound the allocated resources for all other users. Despite the nonlinearity in the objective and constraints, we show this suite of resource allocation optimization can be efficiently solved for global optimality through a convex optimization technique called geometric programming. This general method and its extensions are applicable to a wide array of resource allocation problems, including processor sharing, congestion control, admission control, and wireless network power control.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e88-a.10.2642/_p
Copy
@ARTICLE{e88-a_10_2642,
author={Hakim BADIS, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Efficient Optimization of Network Resource Allocations under Nonlinear Quality of Service Constraints},
year={2005},
volume={E88-A},
number={10},
pages={2642-2646},
abstract={We present an efficient method to optimize network resource allocations under nonlinear Quality of Service (QoS) constraints. We first propose a suite of generalized proportional allocation schemes that can be obtained by minimizing the information-theoretic function of relative entropy. We then optimize over the allocation parameters, which are usually design variables an engineer can directly vary, either for a particular user or for the worst-case user, under constraints that lower bound the allocated resources for all other users. Despite the nonlinearity in the objective and constraints, we show this suite of resource allocation optimization can be efficiently solved for global optimality through a convex optimization technique called geometric programming. This general method and its extensions are applicable to a wide array of resource allocation problems, including processor sharing, congestion control, admission control, and wireless network power control.},
keywords={},
doi={10.1093/ietfec/e88-a.10.2642},
ISSN={},
month={October},}
Copy
TY - JOUR
TI - An Efficient Optimization of Network Resource Allocations under Nonlinear Quality of Service Constraints
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2642
EP - 2646
AU - Hakim BADIS
PY - 2005
DO - 10.1093/ietfec/e88-a.10.2642
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E88-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2005
AB - We present an efficient method to optimize network resource allocations under nonlinear Quality of Service (QoS) constraints. We first propose a suite of generalized proportional allocation schemes that can be obtained by minimizing the information-theoretic function of relative entropy. We then optimize over the allocation parameters, which are usually design variables an engineer can directly vary, either for a particular user or for the worst-case user, under constraints that lower bound the allocated resources for all other users. Despite the nonlinearity in the objective and constraints, we show this suite of resource allocation optimization can be efficiently solved for global optimality through a convex optimization technique called geometric programming. This general method and its extensions are applicable to a wide array of resource allocation problems, including processor sharing, congestion control, admission control, and wireless network power control.
ER -
English
