Multi-constrained Path Research Articles

Size-fixed group discovery via multi-constrained graph pattern matching

Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.

Research on Complex Multiconstraint Path Planning Based on ACA Hybrid Algorithm

Aiming at the multiconstraint complex problem of path planning, a hybrid algorithm with fast convergence and fast obstacle avoidance is proposed. According to the actual aircraft, a multiconstrained path cost model with seven components is constructed and defined the parameters and penalty factors of each part. To improve the sensitivity of each part of the trace planning cost function, according to the inconsistency of the cost index unit and measurement range, the expert scoring method is used to configure and normalize the index with weight. Make full advantage of the ACO algorithm and the artificial potential field algorithm to form the ACA hybrid algorithm, a pheromone heuristic function improvement method is proposed, and a control factor is introduced. The proposed model and planning algorithm are simulated based on the MATLAB platform environment, and it is shown by simulations that the hybrid algorithm has obvious advantages in terms of motion trajectory smoothness, number of iterations, and minimum path cost .It well solved the practical application problem of fast path planning convergence and fast avoiding obstacles in the complex environment.

Multi-constrained cooperative path planning of multiple drones for persistent surveillance in urban environments

Different from the usual surveillance task in which the goal is to achieve complete coverage of the specified area, the cooperative path planning problem of drones for persistent surveillance task is studied in this paper considering multiple constraints of the covered area. The goal is to maximize the combinational coverage area of drones while giving preference to the area that hasn’t been visited beyond a certain time interval. The influence of shooting resolution and blocking of buildings are considered, and the state information of each grid is defined to record the visit information of the ground area. Considering the characteristic of the established model, the multi-constrained cooperative path planning (MCCPP) algorithm is developed. The grids which have not been visited for a long time are received special attentions, and the drone is led to reducing the flight height to cover the gird which has a special requirement on the shooting resolution. The cooperation mechanism among drones is also set to ensure that all the drones can determine the next path point synchronously. An emergency path planning algorithm with the continuous checking strategy is designed for a drone to fly to the specified area and finish a complete coverage of it.

在占空比无线传感器网络中寻找多约束路径的算法

在占空比无线传感器网络中寻找多约束路径的算法

An effective method for service components selection based on micro-canonical annealing considering dependability assurance

Distributed virtualization changes the pattern of building software systems. However, it brings some problems on dependability assurance owing to the complex social relationships and interactions between service components. The best way to solve the problems in a distributed virtualized environment is dependable service components selection. Dependable service components selection can be modeled as finding a dependable service path, which is a multiconstrained optimal path problem. In this paper, a service components selection method that searches for the dependable service path in a distributed virtualized environment is proposed from the perspective of dependability assurance. The concept of Quality of Dependability is introduced to describe and constrain software system dependability during dynamic composition. Then, we model the dependable service components selection as a multiconstrained optimal path problem, and apply the Adaptive Bonus-Penalty Microcanonical Annealing algorithm to find the optimal dependable service path. The experimental results show that the proposed algorithm has high search success rate and quick converges.

Risk-energy aware service level agreement assessment for computing quickest path in computer networks

In this paper, a new variant of the Quickest Path Problem (QPP) is addressed with additional factors of risk and energy which leads to the evaluation of Risk-Energy Constrained Quickest Path Problem (REQPP) computation. In REQPP, risk is based on lag-time or delay and energy necessary for the transmission between two adjacent nodes. The formulation of this problem presents a realistic path computation for critical applications. The success of REQPP is measured by defining a Service Level Agreement (SLA). The proposed algorithm is able to solve the risk-energy constrained REQPP for the continuity of communication. SLA fulfills the needs of multi-constrained path whereas its time-complexity is of the order of Dijkstra's algorithm. Finally, in the end, variation of penalty time and risk variation helps to find the high-performance value of the allowed delay time toward success of SLA.

VNF-EQ: dynamic placement of virtual network functions for energy efficiency and QoS guarantee in NFV

With the advances of network function virtualization and cloud computing technologies, a number of network services are implemented across data centers by creating a service chain using different virtual network functions (VNFs) running on virtual machines. Due to the complexity of network infrastructure, creating a service chain requires high operational cost especially in carrier-grade network service providers and supporting stringent QoS requirements from users is also a complicated task. There have been various research efforts to address these problems that only focus on one aspect of optimization goal either from users such as latency minimization and QoS based optimization, or from service providers such as resource optimization and cost minimization. However, meeting the requirements both from users and service providers efficiently is still a challenging issue. This paper proposes a VNF placement algorithm called VNF-EQ that allows users to meet their service latency requirements, while minimizing the energy consumption at the same time. The proposed algorithm is dynamic in a sense that the locations or the service chains of VNFs are reconfigured to minimize the energy consumption when the traffic passing through the chain falls below a pre-defined threshold. We use genetic algorithm to formulate this problem because it is a variation of the multi-constrained path selection problem known as NP-complete. The benchmarking results show that the proposed approach outperforms other heuristic algorithms by as much as 49% and reduces the energy consumptions by rearranging VNFs.

AN EFFICIENT MULTI-CONSTRAINED FEASIBLE PATH SELECTION FOR MANET

Quality-of-service (QoS) routing in an Ad-Hoc network is difficult because the network topology may change constantly and the available state information for routing is inherently imprecise. This paper proposes a method for multiconstrained feasible path selection for MANET. We introduce the composite function which allows OLSR to find the feasible path in case of multiple routes are available to reach the same destination. Combinations of additive and non additive QoS metrics are considered to find the best path. Our simulation results show that the QoS version of the OLSR do improve the Packet Delivery Ratio, Throughput, Normalized control overhead, End-to-End delay and Packet loss but with the increased time complexity.

Generalized Zeon Algebras: Theory and Application to Multi-Constrained Path Problems

Classical approaches to routing problems often employ construction of trees and the use of heuristics to prevent combinatorial explosion. The algebraic approach presented herein, however, allows such explicit tree constructions to be avoided. Introduced here is the notion of generalized zeon algebras. Their inherent combinatorial properties make them useful for routing problems by implicitly pruning the underlying tree structures. Through the use of generalized idempotent algebras, max-min operators can be implemented for non-additive weights. Moreover, these algebras occur as subalgebras of Clifford algebras, lending them a natural connection to quantum probability and (by extension) to quantum computing.

A service path selection and adaptation algorithm for QoS assurance and load balancing in context-aware service overlay networks

As services are becoming more complicated, service providers face with tremendous difficulties in developing their services. In this environment, a lot of component functions are provided by multiple service providers, and the providers also desire to reduce costs for providing their services. A context-aware service overlay network (CSON) is a promising architecture to meet those demands by dynamically composing service paths and reconfiguring them based on various contexts. This paper proposes a service path selection and adaptation algorithm that assures quality of services (QoS) and balances the loads at the same time. The algorithm also dynamically adapts to an optimal service path when it detects a large amount of unexpected service loads over the service path. Since our algorithm is to solve a variation of multi-constrained path selection problem, which is known to be NP-complete, we formulate the problem using ant colony optimisation algorithm.

To converge more quickly and effectively—Mean field annealing based optimal path selection in WMN

Multi-constrained path selection with optimization aims at finding an optimal path that satisfies a set of QoS parameters, as an NP-hard problem, which is also a big challenge for wireless mesh networks. Heuristic algorithms with polynomial and pseudo-polynomial-time complexities are often used to deal with this problem. However, existing methods either have analyses in specific network models or cannot support the change of network topology well. Thus, multi-constrained QoSR in emerging wireless mesh networking is worth investigating further. In this paper, we propose a novel Routing Algorithm based on Mean Field Annealing (MFA_RA) to solve this problem. MFA_RA first uses a function of two QoS parameters, wireless link’s delay and transmission success rate, as the cost function, and then seeks to find a feasible path by mean field annealing. Moreover, a set of deterministic equations are utilized to replace the stochastic process in simulated annealing (SA), and a saddle point approximation is adopted for the calculation of the stationary probability distribution at equilibrium. Extensive simulation results demonstrate that MFA_RA is an effective algorithm and can outperform other heuristic algorithms.

Multi-constrained path computation for inter-domain QoS-capable services

Computing inter-domain multiprotocol label switching traffic engineering label switched path MPLS-TE LSP through a pre-determined sequence of domains is quite straight as each path computation element PCE, using the backward recursive PCE-based computation BRPC, knows who is the next to be contacted in order to continue the computation. The optimality of the inter-domain MPLS-TE LSP path depends strongly on the choice of the pre-determined sequence of domains on which the calculation works. In this paper, we propose a novel procedure allowing a forward discovery of multiple inter-domain sequences and the computation of constrained inter-domain paths for MPLS-TE LSPs over these domains sequences. Other issues around the inter-domain path computation, such as route discovery and inter-domain loop avoidance, are investigated. Experimental evaluation shows that our solution is effective in terms of protocol and algorithmic efficiency and provides satisfiable performance with high success rate, reasonable message overhead and runtime.

Generalized Multi-Constrained Path (G_MCP) QoS Routing Algorithm for Mobile Ad hoc Networks

In mobile ad hoc network, efficient routing protocol is required to perform route discovery and maintenance. These protocols can be classified into two main types which are proactive and reactive routing protocols. Most of them usually use the suboptimal path to reach destination without considering QoS parameters. This results in network congestion during high traffic load situation. Hence, many algorithms have been proposed to offer QoS routing to these protocols. However, most of them find the feasible path by using only one or two QoS metrics. This is not enough to support many applications with QoS guaranteed, especially multimedia applications since they have more stringent various QoS requirements. To provide QoS routing in ad hoc network based on such environment, we propose the effective algorithm called Generalized MCP (G_MCP) to find the feasible path based on proposed weighted Connectivity Index (combination of link connectivity and capacity) and non-linear cost (combination of multiple additive QoS metrics using non-linear function). We adopt the fall-back approach. That is, G_MCP will find the path from source to destination by considering weighted Connectivity Index first. If there is a tie, the path with least non-linear cost will be chosen. Based on this approach, G_MCP has the comparable time complexity with the Shortest-Widest Path algorithm. We construct the simulation in a number of scenarios based on proactive protocol called OLSR. According to the simulation results, it is obvious that G_MCP performances are superior than OLSR and Shortest-Widest Path algorithms in terms of throughput, packet delivery ratio, delay and success ratio. Therefore, it can be concluded that G_MCP is able to support various applications and be operated well in highly dynamic mobility environment

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.

An evolutionary programming algorithm for finding constrained optimal disjoint paths for multihop communication networks

We present an evolutionary programming algorithm (EP) for finding hop count and bandwidth constrained cost optimal disjoint paths in multihop communication networks. In general, multi-constrained path selection is an NP complete problem. Our proposed algorithm can be used for real-time online network applications, which can initiate and process a number of calls simultaneously on disjoint paths, without overloading the network. This algorithm also requires a small memory space and low execution time. The proposed algorithm, which maintains a balance between finding an optimal shortest path and CPU mean execution time, also generates parallel suboptimal paths during the process of generating the best path. One of these suboptimal paths can be used as a backup path if it is link disjoint with all the primary paths (best paths) of the concurrent requests. Thus, the proposed algorithm provides a limited degree of reliability for routing of packets as well.

Multi-constrained Shortest Disjoint Paths for Reliable QoS Routing

Finding link-disjoint or node-disjoint paths under multiple constraints is an effective way to improve network QoS ability, reliability, and so on. However, existing algorithms for such scheme cannot ensure a feasible solution for arbitrary networks. We propose design principles of an algorithm to fill this gap, which we arrive at by analyzing the properties of optimal solutions for the multi-constrained link-disjoint path pair problem. Based on this, we propose the link-disjoint optimal multi-constrained paths algorithm (LIDOMPA), to find the shortest link-disjoint path pair for any network. Three concepts, namely, the candidate optimal solution, the contractive constraint vector, and structure-aware non-dominance, are introduced to reduce its search space without loss of exactness. Extensive simulations show that LIDOMPA outperforms existing schemes and achieves acceptable complexity. Moreover, LIDOMPA is extended to the node-disjoint optimal multi-constrained paths algorithm (NODOMPA) for the multi-constrained node-disjoint path pair problem.

Design and performance analysis of an inductive QoS routing algorithm

Routing mechanism is key to the success of large-scale, distributed communication and heterogeneous networks. Consequently, computing constrained shortest paths is fundamental to some important network functions such as QoS routing and traffic engineering. The problem of QoS routing with multiple additive constraints is known to be NP-complete but researchers have been designing heuristics and approximation algorithms for multi-constrained paths algorithms to propose pseudo-polynomial time algorithms. This paper introduces a polynomial time approximation quality of service (QoS) routing algorithm and constructs dynamic state-dependent routing policies. The proposed algorithm uses an inductive approach based on trial/error paradigm combined with swarm adaptive approaches to optimize lexicographically various QoS criteria. The originality of our approach is based on the fact that our system is capable to take into account the dynamics of the network where no model of the network dynamics is assumed initially. Our approach samples, estimates, and builds the model of pertinent aspects of the environment which is very important in heterogeneous networks. The algorithm uses a model that combines both a stochastic planned pre-navigation for the exploration phase and a deterministic approach for the backward phase. Multiple paths are searched in parallel to find the K best qualified ones. To improve the overall network performance, a load adaptive balancing policy is defined and depends on a dynamic traffic path probability distribution function. We conducted a performance analysis of the proposed QoS routing algorithm using OPNET based on a platform simulated network. The obtained results demonstrate substantial performance improvements as well as the benefits of learning approaches over networks with dynamically changing traffic.

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