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.

/pdf/low-latency-and-resource-efficient-service-function-chaining-mo4vwermbq.pdf

Low-Latency and Resource-Efficient Service Function Chaining Orchestration in Network Function Virtualization

Video big data in smart city: Background construction and optimization for surveillance video processing

Location and trajectory privacy preservation in 5G-Enabled vehicle social network services

As the size and service requirements of today’s networks gradually increase, large numbers of proprietary devices are deployed, which leads to network complexity, information security crises and makes network service and service provider management increasingly difficult. Network function virtualization (NFV) technology is one solution to this problem. NFV separates network functions from hardware and deploys them as software on a common server. NFV can be used to improve service flexibility and isolate the services provided for each user, thus guaranteeing the security of user data. Therefore, the use of NFV technology includes many problems worth studying. For example, when there is a free choice of network path, one problem is how to choose a service function chain (SFC) that both meets the requirements and offers the service provider maximum profit. Most existing solutions are heuristic algorithms with high time efficiency, or integer linear programming (ILP) algorithms with high accuracy. It’s necessary to design an algorithm that symmetrically considers both time efficiency and accuracy. In this paper, we propose the Q-learning Framework Hybrid Module algorithm (QLFHM), which includes reinforcement learning to solve this SFC deployment problem in dynamic networks. The reinforcement learning module in QLFHM is responsible for the output of alternative paths, while the load balancing module in QLFHM is responsible for picking the optimal solution from them. The results of a comparison simulation experiment on a dynamic network topology show that the proposed algorithm can output the approximate optimal solution in a relatively short time while also considering the network load balance. Thus, it achieves the goal of maximizing the benefit to the service provider.

/pdf/a-q-learning-based-approach-for-deploying-dynamic-service-49cefg9q8k.pdf

A Q-learning-based approach for deploying dynamic service function chains

Towards provisioning hybrid virtual networks in federated cloud data centers

The efficient framework and algorithm for provisioning evolving VDC in federated data centers

Cloud computing has become a cost-effective paradigm for deploying online service applications in large data centers in recent years. Virtualization technology enables flexible and efficient management of physical resources in cloud data centers and improves the resource utilization. A request for resources to a data center can be abstracted as a virtual data center (VDC) request. Due to the use of a large number of resources and at various locations, reliability is an important issue that should be addressed in large and multiple data centers. However, most research focuses on the problem of reliable VDC embedding in a single data center. In this paper, we study the problem of reliable VDC embedding across multiple data centers, such that the total bandwidth consumption in the inter-data center backbone network is minimized, while satisfying the reliability requirement of each VDC request. We model the problem by using mixed integer linear programming (MILP) and propose a heuristic algorithm to address this NP-hard problem efficiently. Simulation results show that the proposed algorithm performs better in terms of lowering physical resource consumption and VDC request blocking ratio compared with existing solution.

Reliable Virtual Data Center Embedding Across Multiple Data Centers

The invention discloses a vehicle congestion degree acquiring method based on Hadoop. Parallel processing on statistical GPS messages is carried out through multiple MapReduce tasks, parallel calculation of the statistical congestion degree and the linearity congestion degree of each area at one time is carried out through a congestion degree calculation function, so a problem of long calculation time existing in a mobile cluster algorithm is overcome, and calculation efficiency is greatly improved. The vehicle congestion degree acquiring method based on Hadoop can simultaneously realize automatic storing of data copy and automatic expansion of a computer cluster and further has properties of high expansibility and high fault tolerance performance.

Vehicle congestion degree acquiring method based on Hadoop

The invention provides a method for reliability perceptive cross-domain mapping of a virtual data center, which belongs to the technical field of cross-domain mapping of the virtual data center. The cross-domain mapping of the VDC (Virtual Data Center) is implemented through dividing levels of a server, grouping virtual machines, mapping working virtual machines in the groups and computing reliability of a current scheme. The mapping method provided by the invention does not utilize excessive resources as backups to guarantee reliability, but the conventional over-supply strategy utilizes more resources than the method provided by the invention. In comparison with the prior art, through the method, the reliability of the VDC is improved, the lower bandwidth cost is implemented, and the blocking rate of the VDC is reduced.

Method for reliability perceptive cross-domain mapping of virtual data center

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Papers

The efficient framework and algorithm for provisioning evolving VDC in federated data centers

Reliable Virtual Data Center Embedding Across Multiple Data Centers

Vehicle congestion degree acquiring method based on Hadoop

Method for reliability perceptive cross-domain mapping of virtual data center