Multi-constrained Path Problem Research Articles

A Multi Constrained Path Quality of Service Routing Algorithm

Providing Quality-of-Service (QoS) in packet switched networks has many challenging problems. One of the m is how to determine a feasible path that satisfies additive multiple constraints. A difficulty of multi constrained path problem is that it is intractable. In this context Multi Constraint path Problem version 3 (MCPv3) is presented where all constraints are approximated and return a feasible path in much quicker time. This problem is modeled by using a graph with K additive QoS constraints associated with each edge for any constant K≥2.MCpv3 is an approximation algorithm that finds a path between a given source and destination pair such that the path weight is not more than (1+α)W where α is approximation factor and W is constraint bound. By properly selecting α and constraint bounds MCPv3 performs well in terms of better quality of paths, less running time and improved scalability irrespective of nature of constraints. A variety of experimental validations are carried out on different scenario to analyze the performance of the proposed schemes.

Performance analysis of genetic algorithm (GA)-based multi-constrained path routing algorithm

To support networked multimedia applications, it is important for the network to provide guaranteed quality-of-service (QoS). One way to provide such services is for the network to perform QoS routing, where the path taken must fulfill a given set of constraints. Multi-constrained path (MCP) problem refers to the problem of finding a path through a network subject to multiple additive constraints. It has been proven that this problem is Non-deterministic Polynomial time (NP)-complete and therefore no exact algorithm can be found. As such, various heuristics and approximation algorithms have been proposed to solve the MCP problem. This paper proposed a solution to the MCP problem using genetic algorithm (GA). The effectiveness of the proposed algorithm is evaluated through simulation. The performance of the algorithm is then compared with an exact algorithm called the depth first search and a common shortest path algorithm called the Dijkstra’s algorithm. The result of the simulation shows that the performance of the proposed algorithm is almost comparable to an exact algorithm, while at the same time can execute much faster. The proposed algorithm has also been shown to have good network link utilization and is able to scale well with network size. Key words: Genetic algorithm, multi-constrained path problem, quality-of-service (QoS), routing.

A Robust GA-based QoS Routing Algorithm for Solving Multi-constrained Path Problem

To support networked multimedia applications, it is important for a network to provide guaranteed quality-of-service (QoS). One way to provide such services is for the network to perform QoS routing, where the path taken must fulfill certain constraints. Multi-constrained path (MCP) problem refers to the problem of finding a path through a network subject to multiple additive constraints. It has been proven that this problem is NP-complete and finding an exact solution can be difficult. As such, various heuristics and approximation algorithms have been proposed to solve the MCP problem. However, the actual link metrics in a QoS-aware network is dynamic and may continuously change over time and since the path given by the routing algorithm is computed using the state information available to the router, which may or may not be up-to-date, it is possible that a feasible path returned by the algorithm may turn out to be no longer valid. This paper presents a GA-based QoS routing algorithm for solving the general k -constrained problem which has the capability to return multiple feasible paths in a single run. This makes the algorithm more robust in the case that the rate of change of state information in the network is higher than the rate of state information received by the router. Simulation results show that this algorithm consistently achieve higher feasibility ratio relative to existing well-known MCP routing algorithms when state information in the router lags behind the network.

A new distributed QoS routing algorithm using mean field approximation method

AbstractToday various real‐time services, such as audio and video conferencing and telemedicine are being deployed over the Internet. This requires the network to provide guarantees of the service provided to the receiver. The needs of applications are specified in terms of Quality of Service (QoS) metrics such as the desired bandwidth, response time, loss rate, expected reliability, etc. Support for QoS must be provided at each of the layers of the protocol stack for overall efficiency in network utilization. Routing algorithms supporting QoS differentiation differ from traditional routing algorithms in that, in QoS routing, the path from the source to the destination must satisfy multiple constraints simultaneously, while in conventional routing, routing decisions are made only on a single metric, such as cost or delay. However, it is well known that the problem of path selection subject to two or more independent additive metrics is NP‐complete which means that there is no efficient (polynomial) exact solution for the general multi‐constrained path (MCP) selection problems. In this paper, we present an outline of distributed QoS routing problems and propose a mean field annealing (MFA) heuristic algorithm to solve the MCP problem. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(1): 1– 10, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10019

Multiconstrained QoS Routing: A Norm Approach

A fundamental problem in quality-of-service (QoS) routing is the multiconstrained path (MCP) problem, where one seeks a source-destination path satisfying K > 2 additive QoS constraints in a network with K additive QoS parameters. The MCP problem is known to be NP-complete. One popular approach is to use the shortest path with respect to a single edge weighting function as an approximate solution to MCP. In a pioneering work, Jaffe showed that the shortest path with respect to a scaled 1-norm of the K edge weights is a 2-approximation to MCP in the sense that the sum of the larger of the path weight and its corresponding constraint is within a factor of 2 from minimum. In a recent paper, Xue et al. showed that the shortest path with respect to a scaled oc-norm of the K edge weights is a K-approximation to MCP, in the sense that the largest ratio of the path weight over its corresponding constraint is within a factor of K from minimum. In this paper, we study the relationship between these two optimization criteria and present a class of provably good approximation algorithms to MCP. We first prove that a good approximation according to the second optimization criterion is also a good approximation according to the first optimization criterion, but not vice versa. We then present a class of very simple K-approximation algorithms according to the second optimization criterion, based on the computation of a shortest path with respect to a single edge weighting function

平均場近似計算法を用いた分散型QoSルーティングアルゴリズム

Today various real-time services, such as audio and video conferencing and telemedicine are being deployed over the Internet. This requires the network to provide guarantees of the service provided to the receiver. The needs of applications are specified in terms of Quality of Service (QoS) metrics such as the desired bandwidth, response time, loss rate, expected reliability, etc. Support for QoS must be provided at each of the layers of the protocol stack for overall efficiency in network utilization. Routing algorithms supporting QoS differentiation differ from traditional routing algorithms in that, in QoS routing, the path from the source to the destination must satisfy multiple constraints simultaneously, while in conventional routing, routing decisions are made only on a single metric, such as cost or delay. However, it is well known that the problem of path selection subject to two or more independent additive metrics is NP-complete which means that there is no efficient (polynomial) exact solution for the general multi-constrained path (MCP) selection problems. In this paper, we present an outline of distributed QoS routing problems and propose a mean field annealing (MFA) heuristic algorithm to solve the MCP problem. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(1): 1– 10, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10019

Finding multi-constrained feasible paths by using depth-first search

An extended depth-first-search (EDFS) algorithm is proposed to solve the multi-constrained path (MCP) problem in Quality-of-Service (QoS) routing, which is NP-Complete when the number of independent routing constraints is more than one. EDFS solves the general k-constrained MCP problem with pseudo-polynomial time complexity O(m 2 · EN + N 2), where m is the maximum number of non-dominated paths maintained for each destination, E and N are the number of links and nodes of a graph, respectively. This is achieved by deducing potential feasible paths from knowledge of previous explorations, without re-exploring finished nodes and their descendants in the process of the DFS search. One unique property of EDFS is that the tighter the constraints are, the better the performance it can achieve, w.r.t. both time complexity and routing success ratio. This is valuable to highly dynamic environment such as wireless ad hoc networks in which network topology and link state keep changing, and real-time or multimedia applications that have stringent service requirements. EDFS is an independent feasible path searching algorithm and decoupled from the underlying routing protocol, and as such can work together with either proactive or on-demand ad hoc routing protocols as long as they can provide sufficient network state information to each source node. Analysis and extensive simulation are conducted to study the performance of EDFS in finding feasible paths that satisfy multiple QoS constraints. The main results show that EDFS is insensitive to the number of constraints, and outperforms other popular MCP algorithms when the routing constraints are tight or moderate. The performance of EDFS is comparable with that of the other algorithms when the constraints are loose.

Optimal and heuristic algorithms for quality-of-service routing with multiple constraints

The multi-constrained path (MCP) selection problem occurs when the quality-of-services (QoS) are supported for point-to-point connections in the distributed multimedia applications deployed on the Internet. This NP-complete problem is concerned about how to determine a feasible path between two given end-points, so that a set of QoS path constraints can be satisfied simultaneously. Based on the branch-and-bound technique and tabu-searching strategy, an optimal algorithm and a tabu-search based heuristic are developed in this paper for solving the MCP problem with multiple constraints. The experimental results show that our tabu-search based heuristic not only outperforms the previous method published in literature, but also demonstrates that it is indeed a highly efficient method for solving the MCP problem in large-scale networks.