### Abstract

Providing quality-of-service (QoS) guarantees in packet networks gives rise to several challenging issues. One of them is how to determine a feasible path that satisfies a set of constraints while maintaining high utilization of network resources. The latter objective implies the need to impose an additional optimality requirement on the feasibility problem. This can be done through a primary cost function (e.g., administrative weight, hop-count) according to which the selected feasible path is optimal. In general, multi-constrained path selection, with or without optimization, is an NP-complete problem that cannot be exactly solved in polynomial time. Heuristics and approximation algorithms with polynomial-and pseudo-polynomial-time complexities are often used to deal with this problem. However, existing solutions suffer either from excessive computational complexities that cannot be used for online network operation or from low performance. Moreover, they only deal with special cases of the problem (e.g., two constraints without optimization, one constraint with optimization, etc.). For the feasibility problem under multiple constraints, some researchers have recently proposed a nonlinear cost function whose minimization provides a continuous spectrum of solutions ranging from a generalized linear approximation (GLA) to an asymptotically exact solution. In this paper, we propose an efficient heuristic algorithm for the most general form of the problem. We first formalize the theoretical properties of the above nonlinear cost function. We then introduce our heuristic algorithm (H_MCOP), which attempts to minimize both the nonlinear cost function (for the feasibility part) and the primary cost function (for the optimality part). We prove that H_MCOP guarantees at least the performance of GLA and often improves upon it. H_MCOP has the same order of complexity as Dijkstra's algorithm. Using extensive simulations on random graphs with correlated and uncorrelated link weights, we show that under the same level of computational complexity, H_MCOP outperforms its (less general) contenders in its success rate in finding feasible paths and in the cost of such paths.

Original language | English (US) |
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Title of host publication | Proceedings - IEEE INFOCOM |

Pages | 834-843 |

Number of pages | 10 |

Volume | 2 |

State | Published - 2001 |

Event | 20th Annual Joint Conference of the IEEE Computer and Communications Societies - Anchorage, AK, United States Duration: Apr 24 2001 → Apr 26 2001 |

### Other

Other | 20th Annual Joint Conference of the IEEE Computer and Communications Societies |
---|---|

Country | United States |

City | Anchorage, AK |

Period | 4/24/01 → 4/26/01 |

### Fingerprint

### Keywords

- k-shortest paths
- Multiple constraints
- Path selection
- QoS routing
- Scalable routing

### ASJC Scopus subject areas

- Hardware and Architecture
- Electrical and Electronic Engineering

### Cite this

*Proceedings - IEEE INFOCOM*(Vol. 2, pp. 834-843)

**Multi-constrained optimal path selection.** / Korkmaz, T.; Krunz, Marwan M.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings - IEEE INFOCOM.*vol. 2, pp. 834-843, 20th Annual Joint Conference of the IEEE Computer and Communications Societies, Anchorage, AK, United States, 4/24/01.

}

TY - GEN

T1 - Multi-constrained optimal path selection

AU - Korkmaz, T.

AU - Krunz, Marwan M

PY - 2001

Y1 - 2001

N2 - Providing quality-of-service (QoS) guarantees in packet networks gives rise to several challenging issues. One of them is how to determine a feasible path that satisfies a set of constraints while maintaining high utilization of network resources. The latter objective implies the need to impose an additional optimality requirement on the feasibility problem. This can be done through a primary cost function (e.g., administrative weight, hop-count) according to which the selected feasible path is optimal. In general, multi-constrained path selection, with or without optimization, is an NP-complete problem that cannot be exactly solved in polynomial time. Heuristics and approximation algorithms with polynomial-and pseudo-polynomial-time complexities are often used to deal with this problem. However, existing solutions suffer either from excessive computational complexities that cannot be used for online network operation or from low performance. Moreover, they only deal with special cases of the problem (e.g., two constraints without optimization, one constraint with optimization, etc.). For the feasibility problem under multiple constraints, some researchers have recently proposed a nonlinear cost function whose minimization provides a continuous spectrum of solutions ranging from a generalized linear approximation (GLA) to an asymptotically exact solution. In this paper, we propose an efficient heuristic algorithm for the most general form of the problem. We first formalize the theoretical properties of the above nonlinear cost function. We then introduce our heuristic algorithm (H_MCOP), which attempts to minimize both the nonlinear cost function (for the feasibility part) and the primary cost function (for the optimality part). We prove that H_MCOP guarantees at least the performance of GLA and often improves upon it. H_MCOP has the same order of complexity as Dijkstra's algorithm. Using extensive simulations on random graphs with correlated and uncorrelated link weights, we show that under the same level of computational complexity, H_MCOP outperforms its (less general) contenders in its success rate in finding feasible paths and in the cost of such paths.

AB - Providing quality-of-service (QoS) guarantees in packet networks gives rise to several challenging issues. One of them is how to determine a feasible path that satisfies a set of constraints while maintaining high utilization of network resources. The latter objective implies the need to impose an additional optimality requirement on the feasibility problem. This can be done through a primary cost function (e.g., administrative weight, hop-count) according to which the selected feasible path is optimal. In general, multi-constrained path selection, with or without optimization, is an NP-complete problem that cannot be exactly solved in polynomial time. Heuristics and approximation algorithms with polynomial-and pseudo-polynomial-time complexities are often used to deal with this problem. However, existing solutions suffer either from excessive computational complexities that cannot be used for online network operation or from low performance. Moreover, they only deal with special cases of the problem (e.g., two constraints without optimization, one constraint with optimization, etc.). For the feasibility problem under multiple constraints, some researchers have recently proposed a nonlinear cost function whose minimization provides a continuous spectrum of solutions ranging from a generalized linear approximation (GLA) to an asymptotically exact solution. In this paper, we propose an efficient heuristic algorithm for the most general form of the problem. We first formalize the theoretical properties of the above nonlinear cost function. We then introduce our heuristic algorithm (H_MCOP), which attempts to minimize both the nonlinear cost function (for the feasibility part) and the primary cost function (for the optimality part). We prove that H_MCOP guarantees at least the performance of GLA and often improves upon it. H_MCOP has the same order of complexity as Dijkstra's algorithm. Using extensive simulations on random graphs with correlated and uncorrelated link weights, we show that under the same level of computational complexity, H_MCOP outperforms its (less general) contenders in its success rate in finding feasible paths and in the cost of such paths.

KW - k-shortest paths

KW - Multiple constraints

KW - Path selection

KW - QoS routing

KW - Scalable routing

UR - http://www.scopus.com/inward/record.url?scp=0035019901&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035019901&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0035019901

VL - 2

SP - 834

EP - 843

BT - Proceedings - IEEE INFOCOM

ER -