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Joint failure recovery, fault prevention, and energy-efficient resource management for real-time SFC in fog-supported SDN

机译:雾支持SDN中针对实时SFC的联合故障恢复,故障预防和节能资源管理

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Middleboxes have become a vital part of modern networks by providing services such as load balancing, optimization of network traffic, and content filtering. A sequence of middleboxes comprising a logical service is called a Service Function Chain (SFC). In this context, the main issues are to maintain an acceptable level of network path survivability and a fair allocation of the resource between different demands in the event of faults or failures. In this paper, we focus on the problems of traffic engineering, failure recovery, fault prevention, and SFC with reliability and energy consumption constraints in Software Defined Networks (SDN). These types of deployments use Fog computing as an emerging paradigm to manage the distributed small-size traffic flows passing through the SDN-enabled switches (possibly Fog Nodes). The main aim of this integration is to support service delivery in real-time, failure recovery, and fault-awareness in an SFC context. Firstly, we present an architecture for Failure Recovery and Fault Prevention called FRFP; this is a multi-tier structure in which the real-time traffic flows pass through SDN-enabled switches to jointly decrease the network side-effects of flow rerouting and energy consumption of the Fog Nodes. We then mathematically formulate an optimization problem called the Optimal Fog-Supported Energy-Aware SFC rerouting algorithm (OFES) and propose a near-optimal heuristic called Heuristic OFES (HFES) to solve the corresponding problem in polynomial time. In this way, the energy consumption and the reliability of the selected paths are optimized, while the Quality of Service (QoS) constraints are met and the network congestion is minimized. In a reliability context, the focus of this work is on fault prevention; however, since we use a reallocation technique, the proposed scheme can be used as a failure recovery scheme. We compare the performance of HFES and OFES in terms of energy consumption, average path length, fault probability, network side-effects, link utilization, and Fog Node utilization. Additionally, we analyze the computational complexity of HFES. We use a real-world network topology to evaluate our algorithm. The simulation results show that the heuristic algorithm is applicable to large-scale networks. (C) 2019 Elsevier B.V. All rights reserved.
机译:中间盒通过提供诸如负载平衡,网络流量优化和内容过滤之类的服务,已成为现代网络的重要组成部分。包含逻辑服务的中间盒序列称为服务功能链(SFC)。在这种情况下,主要问题是要保持可接受的网络路径生存能力水平,并在发生故障或故障时在不同需求之间合理分配资源。在本文中,我们重点关注软件定义网络(SDN)中的流量工程,故障恢复,故障预防以及具有可靠性和能耗限制的SFC问题。这些类型的部署使用Fog计算作为新兴范例来管理通过启用SDN的交换机(可能是Fog节点)的分布式小型流量。这种集成的主要目的是在SFC上下文中支持实时的服务交付,故障恢复和故障意识。首先,我们提出了一种用于故障恢复和故障预防的架构,称为FRFP。这是一个多层结构,其中实时流量流通过启用SDN的交换机,以共同减少流量重新路由的网络副作用以及雾节点的能耗。然后,我们用数学公式表达一个优化问题,称为优化雾支持能量感知SFC重路由算法(OFES),并提出一种近似最优的启发式算法,称为启发式OFES(HFES),以解决多项式时间内的相应问题。这样,可以优化所选路径的能耗和可靠性,同时满足服务质量(QoS)约束,并最大程度地减少网络拥塞。在可靠性方面,这项工作的重点是故障预防。但是,由于我们使用了重新分配技术,因此所提出的方案可以用作故障恢复方案。我们在能耗,平均路径长度,故障概率,网络副作用,链路利用率和雾节点利用率方面比较了HFES和OFES的性能。此外,我们分析了HFES的计算复杂性。我们使用现实世界的网络拓扑来评估我们的算法。仿真结果表明,该启发式算法适用于大规模网络。 (C)2019 Elsevier B.V.保留所有权利。

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