Multi-constrained Path Selection Research Articles

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.

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.

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.

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

Normal Measure Based Multi-Constrained Path Selection

Normal Measure Based Multi-Constrained Path Selection

Algorithm for multi-constrained path selection based on experimental analysis

It is a challenging problem to provide quality-of-service (QoS) guarantees in next generation high-speed network, and the QoS routing is one of the key issues of the problem. For the problem of multi-constrained QoS routing in high-speed network, especially under the inaccurate link state information, the success ratio of the different constraint combination is analyzed statistically, and a constraint analysis method based on the computer simulation is proposed. Furthermore, the approximately equal loose-tight order relation between each two constraints is constructed, and then an algorithm based on the experimental analysis is presented. Finally, the simulation result demonstrates that the algorithm has the higher success ratio, and the theoretical analysis proves its correctness and universality.

An efficient QoS routing algorithm for solving MCP in ad hoc networks

Providing guaranteed quality of service (QoS) in wireless networks is a key issue for deploying multimedia applications. To support such a QoS, an arduous problem concerning how to find a feasible end to end path to satisfy multiple QoS constraints should be studied. In general, multi-constrained path selection, with or without optimization, is an NP-complete problem that cannot be exactly solved in polynomial time. Approximation algorithms and heuristics 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 multimedia applications in ad hoc networks characterized by mobility and performance constraints (e.g., limited energy, wireless medium, etc.). Recently a promising heuristic algorithm H_MCOP using a non linear Lagrange relaxation path functions has demonstrated an improvement in its success rate and in finding feasible paths. However, the H_MCOP is not suitable for ad hoc networks and has not exploited the full capability that a Lagrange relaxation could offer. In this paper, we propose an efficient multi-constrained path heuristic called E_MCP, which exploits efficiently the Lagrange relaxation and enhances the path search process to be adequate to mobile ad hoc networks. Using extensive simulations on random mobile network with correlated and uncorrelated link weights, we show that the same level of computational complexity, E_MCP can achieve a higher success ratio of finding feasible paths.

Highly responsive and efficient QoS routing using pre- and on-demand computations along with a new normal measure

Multi-constrained path (MCP) selection is one of the great challenges that QoS routing (QoSR) faces. To address it in an efficient and highly responsive manner, we propose a new QoSR algorithm, namely NM_MCP (normal measure-based multiple constrained path). Using the Dijkstra's algorithm with respect to each link metric, NM_MCP pre-computes k primary paths in advance, where k is the number of link weights. When a routing request arrives, NM_MCP executes a modified version of the Dijkstra's algorithm using a newly proposed, normal-measure-based nonlinear cost function. Extensive simulations show that NM_MCP achieves higher success rate in finding feasible paths with less computational cost than existing algorithms. To further improve the performance, we incorporate Pareto and nonlinear look-ahead mechanisms into the algorithm.

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.

Routing multimedia traffic with qos guarantees

One of the challenging issues in exchanging multimedia information over a network is how to determine a feasible path that satisfies all the quality-of-service (QoS) requirements of multimedia applications 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, multiconstrained 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/spl I.bar/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/spl I.bar/MCOP guarantees at least the performance of GLA and often improves upon it. H/spl I.bar/MCOP has the same order of complexity as Dijkstra's algorithm. Using extensive simulations on random graphs and realistic network topologies with correlated and uncorrelated link weights from several distributions including uniform, normal, and exponential, we show the efficiency of H/spl I.bar/MCOP over its (less general) contenders in terms of finding feasible paths and minimizing their costs under the same level of computational complexity.

An efficient QoS routing algorithm for multi-constrained path selection

An efficient QoS routing algorithm was proposed for multiple constrained path selection. Making use of efficient pruning policy, the algorithm reduces greatly the size of search space and the computing time. Although the proposed algorithm has exponential time complexity in the worst case, it can get the running results quickly in practical application. When the scale of network increases, the algorithm can efficiently control the size of search space by constraint conditions and prior queue. The results of simulation show that successful request ratio (r) of efficient algorithm for multi-constrained optimal path (EAMCOP) is better than that of heuristic algorithm for multi-constrained optimal path (H-MCOP), but average computing time (t) of EAMCOP is far less than that of H-MCOP. And it can be seen that the computing time of EAMCOP is only one fourth of that of H-MCOP in Advanced Research Projects Agency Network (ARPANet) topology.