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首页> 外文期刊>Journal of supercomputing >Learning-based dynamic scalable load-balanced firewall as a service in network function-virtualized cloud computing environments
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Learning-based dynamic scalable load-balanced firewall as a service in network function-virtualized cloud computing environments

机译：网络功能虚拟化云计算环境中基于学习的动态可扩展负载平衡防火墙即服务

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Network function virtualization (NFV) is a network architecture which tries to provide communication services in clouds through virtualization techniques. Actually, NFV combines server and service and replaces a lot of network devices. NFV deploys software applications instead of hardware devices and therefore reduces network provider's financial costs and facilities manageability. One of the services that NFVs present is virtualized firewalls in clouds. As other services in clouds, firewalls should be dynamically scaled to the needs of any business and adapt as demands increase. In this paper, a method is proposed for dynamic auto-scalability of the firewall service in cloud environments. The proposed method also balances incoming load among different virtualized firewalls which are installed as a software on virtual machines and are located in one pool. We consider a queuing model for each virtual machine. The goal here is to determine the number of active virtualized firewalls required in different time steps according to the intensity of incoming load and the proportion of total requests that goes to each firewall. Decisions are made regarding the utilization of firewall virtual machines so that QoS requirements can be met; at the same time, the resources will be saved in order to balance the performance with the cost of allocated firewall virtual machines. To solve the problem, we propose a hybrid genetic algorithm and reinforcement learning-based approach, namely GARLAS (genetic algorithm and reinforcement learning-based autonomic scaling), implemented in a cloud manager. The results of simulation with MATLAB on different realistic workloads demonstrate that the approach is able to find an optimal policy in both scalability and load balancing aspects. Also, it leads to 87.91 and 85.15% of lower average response time and 9.93 and 11.77% of improvement in utilization in comparison with static and threshold-based approaches, respectively.
机译：网络功能虚拟化（NFV）是一种尝试通过虚拟化技术在云中提供通信服务的网络体系结构。实际上，NFV结合了服务器和服务，并取代了许多网络设备。 NFV部署软件应用程序而不是硬件设备，因此降低了网络提供商的财务成本和设施可管理性。 NFV提供的服务之一是云中的虚拟防火墙。与云中的其他服务一样，防火墙应动态扩展以适应任何业务的需求，并随着需求的增长而适应。本文提出了一种在云环境中实现防火墙服务动态自动扩展的方法。所提出的方法还平衡了作为软件安装在虚拟机上并位于一个池中的不同虚拟防火墙之间的传入负载。我们为每个虚拟机考虑一个排队模型。此处的目标是根据传入负载的强度和发送到每个防火墙的总请求的比例，确定不同时间步长所需的活动虚拟防火墙的数量。做出有关使用防火墙虚拟机的决定，以便可以满足QoS要求；同时，将节省资源，以平衡性能与分配的防火墙虚拟机的成本。为了解决该问题，我们提出了一种基于混合遗传算法和基于强化学习的方法，即GARLAS（基于遗传算法和基于强化学习的自主缩放），在云管理器中实现。在不同的实际工作负载上使用MATLAB进行仿真的结果表明，该方法能够在可伸缩性和负载平衡方面找到最佳策略。此外，与静态方法和基于阈值的方法相比，它分别导致平均响应时间降低了87.91％和85.15％，利用率提高了9.93％和11.77％。

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