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In cloud computing, an important concern is to allocate the available resources of service nodes to the requested tasks on demand and to make the objective function optimum, i.e., maximizing resource utilization, payoffs and available bandwidth. This paper proposes a hierarchical multi-agent optimization (HMAO) algorithm in order to maximize the resource utilization and make the bandwidth cost minimum for cloud computing. The proposed HMAO algorithm is a combination of the genetic algorithm (GA) and the multi-agent optimization (MAO) algorithm. With maximizing the resource utilization, an improved GA is implemented to find a set of service nodes that are used to deploy the requested tasks. A decentralized-based MAO algorithm is presented to minimize the bandwidth cost. We study the effect of key parameters of the HMAO algorithm by the Taguchi method and evaluate the performance results. The results demonstrate that the HMAO algorithm is more effective than two baseline algorithms of genetic algorithm (GA) and fast elitist non-dominated sorting genetic algorithm (NSGA-II) in solving the large-scale optimization problem of resource allocation. Furthermore, we provide the performance comparison of the HMAO algorithm with two heuristic Greedy and Viterbi algorithms in on-line resource allocation.

By introducing software-defined networking (SDN) and network function virtualization (NFV), low-earth-orbit (LEO) satellite networks can facilitate virtual network function (VNF) placement, which will provide computing services for satellite applications on-demand. In this paper, we study the VNF placement problem in a decentralized LEO satellite network due to the requirements for real-time processing and network resilience, where our aim is to jointly optimize end-toend service delay and network bandwidth cost in a dynamic environment. To this end, a decentralized LEO satellite network architecture is first implemented for resource management by establishing the neighboring sub-network for each satellite. Then we formulate the VNF placement problem as an integer non-linear programming problem with multiple constraints of network resources and service requirements. A neighbor-based VNF placement (N-VNFP) approach is proposed to address the optimization problem. Finally, we conduct the experiments to evaluate the performance of the proposed N-VNFP approach in a Walker constellation with 66 LEO satellites. The simulation results show that the proposed N-VNFP approach provides an effective solution for resource management in a decentralized LEO satellite network and also outperforms the two centralized baselines, i.e., Viterbi and Greedy, in terms of end-to-end service delay and network bandwidth cost.

This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version.

As per the increase in satellite number and variety, satellite ground station should be required to offer user services in a flexible and efficient manner. Network function virtualization (NFV) can provide a new paradigm to allocate network resources on demand for user services over the underlying network. In this paper, we investigate the virtualized network function (VNF) placement and routing traffic problem in satellite ground station networks. We formulate the problem of resource allocation as an integer linear programming (ILP) model and the objective is to minimize the link resource utilization and the number of servers used. Considering the information about satellite orbit fixation and mission planning, we propose location-aware resource allocation (LARA) algorithms based on Greedy and IBM CPLEX 12.10, respectively. The proposed LARA algorithm can assist in deploying VNFs and routing traffic flows by predicting the running conditions of user services. We evaluate the performance of our proposed LARA algorithm in three networks of Fat-Tree, BCube and VL2. Simulation results show that our proposed LARA algorithm performs better than that without prediction, and can effectively decrease the average resource utilization of satellite ground station networks.