The Experts below are selected from a list of 26070 Experts worldwide ranked by ideXlab platform

Athanasios V Vasilakos - One of the best experts on this subject based on the ideXlab platform.

  • low latency and resource efficient service function chaining orchestration in Network function virtualization
    IEEE Internet of Things Journal, 2020
    Co-Authors: Gang Sun, Victor Chang, Xi Chen, Athanasios V Vasilakos
    Abstract:

    Recently, Network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional Networks and to improve Network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual Network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this article, we propose an SFC deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS-based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the Load rate of the nodes to achieve Network Load Balancing.

Santos Dorabella - One of the best experts on this subject based on the ideXlab platform.

  • Optimizing Network Load Balancing: an hybridization approach of metaheuristics with column generation
    'Springer Science and Business Media LLC', 2026
    Co-Authors: Santos Dorabella, Amaro Fernandes De ,sousa, Alvelos Filipe, Pióro Michal
    Abstract:

    Given a capacitated telecommunications Network with single path routing and an estimated traffic demand matrix, we aim to determine the routing path of each traffic commodity such that the whole set of paths provide an optimal Network Load Balancing. In a recent paper, we have proposed a column generation based heuristic where, in the first step, we use column generation to solve a linear programming relaxation of the original problem (obtaining, in this way, a lower bound and a set of paths for each commodity) and, in the second step, we apply a multi-start local search with path relinking heuristic on the solution space defined by the paths of the first step. Here, we propose a hybridization approach of the metaheuristic with column generation that can be seen as an enhanced version of the previous approach: we run column generation not only at the beginning (to define the initial search space) but also during the search. These additional column generation steps consist in solving a perturbed problem defined by the incumbent solution. In the previous paper, we have shown that the first approach is efficient in obtaining near optimal routing solutions within short running times. With the enhanced version, we show through computational results that the additional paths, introduced by the additional column generation steps, either improve the efficiency of the algorithm or show similar efficiency in the cases where the original algorithm is already very efficient

  • A hybrid column generation with GRASP and path relinking for the Network Load Balancing problem
    'Elsevier BV', 2026
    Co-Authors: Santos Dorabella, Amaro Fernandes De ,sousa, Alvelos Filipe
    Abstract:

    In this paper, a hybrid meta-heuristic is proposed which combines the GRASP with path relinking method and Column Generation. The key idea of this method is to run a GRASP with path relinking search on a restricted search space, defined by Column Generation, instead of running the search on the complete search space of the problem. Moreover, column generation is used not only to compute the initial restricted search space but also to modify it during the whole algorithm. The proposed heuristic is used to solve the Network Load Balancing problem: given a capacitated telecommunications Network with single path routing and an estimated traffic demand matrix, the Network Load Balancing problem is the determination of a routing path for each traffic commodity such that the Network Load Balancing is optimized, i.e., the worst link Load is minimized, among all such solutions, the second worst link Load is minimized, and continuing in this way until all link Loads are minimized. The computational results presented in this paper show that, for the Network Load Balancing problem, the proposed heuristic is effective in obtaining better quality solutions in shorter running times

  • Load Balancing optimization of telecommunication Networks with two differentiated services
    'Elsevier BV', 2026
    Co-Authors: Amaro Fernandes De ,sousa, Lopes Carlos, Santos Dorabella
    Abstract:

    The Differentiated Services architecture is a scalable solution to provide differentiated Quality of Service. In this paper, we address the Network Load Balancing optimization of such Networks based on bandwidth differentiation between two services. We define the optimization problem as an Integer Programming model and propose a heuristic algorithm based on GRASP with Path Relinking. We present computational results showing that (i) good quality solutions can be computed and (ii) proper Load Balancing can efficiently obtain service differentiation

Lihong Zheng - One of the best experts on this subject based on the ideXlab platform.

  • an effective hybrid routing algorithm in wsn ant colony optimization in combination with hop count minimization
    Sensors, 2018
    Co-Authors: Ailian Jiang, Lihong Zheng
    Abstract:

    Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor Networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, Network Load Balancing and dynamic Network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, Balancing the Network Load and the Network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the Network lifetime.

Jong Hyuk Park - One of the best experts on this subject based on the ideXlab platform.

  • machine learning based Network sub slicing framework in a sustainable 5g environment
    Sustainability, 2020
    Co-Authors: Sushil Kumar Singh, Mikail Mohammed Salim, Jeonghun Cha, Y Pan, Jong Hyuk Park
    Abstract:

    Nowadays, 5G Network infrastructures are being developed for various industrial IoT (Internet of Things) applications worldwide, emerging with the IoT. As such, it is possible to deploy power-optimized technology in a way that promotes the long-term sustainability of Networks. Network slicing is a fundamental technology that is implemented to handle Load Balancing issues within a multi-tenant Network system. Separate Network slices are formed to process applications having different requirements, such as low latency, high reliability, and high spectral efficiency. Modern IoT applications have dynamic needs, and various systems prioritize assorted types of Network resources accordingly. In this paper, we present a new framework for the optimum performance of device applications with optimized Network slice resources. Specifically, we propose a Machine Learning-based Network Sub-slicing Framework in a Sustainable 5G Environment in order to optimize Network Load Balancing problems, where each logical slice is divided into a virtualized sub-slice of resources. Each sub-slice provides the application system with different prioritized resources as necessary. One sub-slice focuses on spectral efficiency, whereas the other focuses on providing low latency with reduced power consumption. We identify different connected device application requirements through feature selection using the Support Vector Machine (SVM) algorithm. The K-means algorithm is used to create clusters of sub-slices for the similar grouping of types of application services such as application-based, platform-based, and infrastructure-based services. Latency, Load Balancing, heterogeneity, and power efficiency are the four primary key considerations for the proposed framework. We evaluate and present a comparative analysis of the proposed framework, which outperforms existing studies based on experimental evaluation.

Rui Wang - One of the best experts on this subject based on the ideXlab platform.

  • a clustering tree topology control based on the energy forecast for heterogeneous wireless sensor Networks
    IEEE CAA Journal of Automatica Sinica, 2016
    Co-Authors: Zhen Hong, Rui Wang
    Abstract:

    How to design an energy-efficient algorithm to maximize the Network lifetime in complicated scenarios is a critical problem for heterogeneous wireless sensor Networks (HWSN). In this paper, a clustering-tree topology control algorithm based on the energy forecast (CTEF) is proposed for saving energy and ensuring Network Load Balancing, while considering the link quality, packet loss rate, etc. In CTEF, the average energy of the Network is accurately predicted per round (the lifetime of the Network is denoted by rounds) in terms of the difference between the ideal and actual average residual energy using central limit theorem and normal distribution mechanism, simultaneously. On this basis, cluster heads are selected by cost function (including the energy, link quality and packet loss rate) and their distance. The non-cluster heads are determined to join the cluster through the energy, distance and link quality. Furthermore, several non-cluster heads in each cluster are chosen as the relay nodes for transmitting data through multi-hop communication to decrease the Load of each cluster-head and prolong the lifetime of the Network. The simulation results show the efficiency of CTEF. Compared with low-energy adaptive clustering hierarchy (LEACH), energy dissipation forecast and clustering management (EDFCM) and efficient and dynamic clustering scheme (EDCS) protocols, CTEF has longer Network lifetime and receives more data packets at base station.