SIAM Journal on Computing
A randomized algorithm for finding a path subject to multiple QoS requirements
Computer Networks: The International Journal of Computer and Telecommunications Networking
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Networking with Cognitive Packets
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Ant Colony Optimization
Conditions that impact the complexity of QoS routing
IEEE/ACM Transactions on Networking (TON)
Approximation Algorithms for Multiconstrained Quality-of-Service Routing
IEEE Transactions on Computers
Data Structures, Algorithms, And Applications In C++
Data Structures, Algorithms, And Applications In C++
International Journal of Communication Systems
Quality of Service Mechanisms in Next Generation Heterogeneous Networks
Quality of Service Mechanisms in Next Generation Heterogeneous Networks
Research challenges in QoS routing
Computer Communications
Performance evaluation of constraint-based path selection algorithms
IEEE Network: The Magazine of Global Internetworking
User to user QoE routing system
WWIC'11 Proceedings of the 9th IFIP TC 6 international conference on Wired/wireless internet communications
Delay coerced multi constrained quality of service routing algorithm
WSEAS Transactions on Information Science and Applications
A state-dependent time evolving multi-constraint routing algorithm
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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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.