Abstract 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.
Numerous works address the switch energy efficiency and energy-aware routing strategies in SDNs NFVs @cite_20 @cite_34 @cite_23 @cite_27 @cite_16 . In detail, the authors in @cite_20 present a network-wide energy-aware routing method using OF maximizing aggregate network utilization and optimized load balancing in SDN. Their practical solution has problem with scalability and does not even support the FRFP SFC aspects that this paper also targets.
@cite_16 @cite_27 @cite_23 @cite_34 @cite_20
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Abstract The energy efficiency of wired networks has received considerable attention over the past decade due to its economic and environmental impacts. However, because of the vertical integration of the control and data planes in conventional networks, optimizing energy consumption in such networks is challenging. Software-defined networking (SDN) is an emerging networking paradigm that decouples the control plane from the data plane and introduces network programmability for the development of network applications. In this work, we propose an energy-aware integral flow-routing solution to improve the energy efficiency of the SDN routing application. We consider discreteness of link rates and pose the routing problem as a mixed integer linear programming (MILP) problem, which is known to be NP complete. The proposed solution is a heuristic implementation of the Benders decomposition method that routes additional single and multiple flows without resolving the routing problem. Performance evaluations demonstrate that the proposed solution achieves a close-to-optimal performance (within 3.27 error) compared to CPLEX on various topologies with less than 0.056 of CPLEX average computation time. Furthermore, our solution outperforms the shortest path algorithm by 24.12 to 54.35 in power savings. The inefficiency of energy usage on the Internet has become a critical problem with its rapid growth, as all network devices operate at full capacity in spite of the real traffic load. Existing studies try to develop energy efficient routings by aggregating traffic and switching underutilized devices into sleep mode. However, most existing approaches do not address the problem of routing convergence well. Since traffic changes frequently in a network, routing convergence may be triggered frequently for an energy efficient routing, which may induce routing loops and black holes, resulting in severe packet loss. In this paper, we present a fast rerouting-based (FRR-based) energy efficient routing scheme, namely GreenFRR, which leverages the technique of fast rerouting to reduce the convergence time. We first study typical fast rerouting techniques and address the challenge of guaranteeing loop-free routing. Then, we formalize the FRR-based energy efficient routing problem and prove that the problem is NP-hard. In order to solve this problem, we design heuristic algorithms to maximize the number of sleeping links. In particular, we consider link utilization ratio and path stretch in our algorithms. We evaluate our scheme by simulations on real and synthetic topologies with real and synthetic traffic traces. The results show that the power consumed by line cards achieves a saving of 40 and the convergence time can be reduced by 95 . The energy-efficiency usage is relative to each layer of wireless sensor networks. In order to improve the ZigBee mesh routing protocol for energy-efficiency usage, we propose a routing algorithm combining AODVjr with the node residual energy. To reduce the energy consumption of bottleneck nodes and extend network lifetime, the improved routing algorithm aims to build a new balancing method between the energy-aware routing metric and the shortest path. Based on the energy model of CC2430 RF transceiver, the implemented routing protocol is simulated using NS2 (Network Simulator version 2). Simulation result shows that this improved protocol can extend the network lifetime very efficiently. In this paper, we address the problem of energy efficient localized routing in wireless ad hoc networks. Numerous energy aware routing protocols were proposed to seek the power efficiency of routes. Among them, several geographical localized routing protocols were proposed to help making smarter routing decision using only local information and reduce the routing overhead. However, most of the proposed localized routing methods cannot theoretically guarantee the power efficiency of their routes. In this paper, we give the first localized routing algorithm, called localized energy aware restricted neighborhood routing (LEARN), which can guarantee the power efficiency of its route asymptotically almost sure. Given destination node t, an intermediate node v will only select a certain neighbor v such that pi alpha, our LEARN routing protocol will find the route for any pair of nodes asymptotically almost sure. When the transmission range rn = radicbet alnl pin for some beta < pi alpha, the LEARN routing protocol will not be able to find the route for any pair of nodes asymptotically almost sure. We also conducted simulations to study the performance of LEARN and compare it with a typical localized routing protocol (GPSR) and a global ad hoc routing protocol (DSR)