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) The recent interest in sensor networks has led to a number of routing schemes that use the limited resources available at sensor nodes more efficiently. These schemes typically try to find the minimum energy path to optimize energy usage at a node. In this paper we take the view that always using lowest energy paths may not be optimal from the point of view of network lifetime and long-term connectivity. To optimize these measures, we propose a new scheme called energy aware routing that uses sub-optimal paths occasionally to provide substantial gains. Simulation results are also presented that show increase in network lifetimes of up to 40 over comparable schemes like directed diffusion routing. Nodes also burn energy in a more equitable way across the network ensuring a more graceful degradation of service with time. Elastic scaling and load balancing with efficient switch migration are critical to enable the elasticity of software-defined networking (SDN) controllers, but learning how to improve migration efficiency remains a difficult problem. To address this issue, a switch migration-based decision-making (SMDM) scheme is put forward that could be made aware of the load imbalance by a switch migration trigger metric; the migration efficiency model for this scheme is built to make a tradeoff between migration costs and the load balance rate. An efficiency-aware switch migration algorithm based on greedy method is designed to utilize the migration efficiency model and thus guide the choice of possible migration actions. We implement a proof of the scheme and present a numerical evaluation using Mininet emulator to demonstrate the effectiveness of our proposal. The goal of data center network is to interconnect the massive number of data center servers, and provide efficient and fault-tolerant routing service to upper-layer applications. To overcome the problem of tree architecture in current practice, many new network architectures are proposed, represented by Fat-Tree, BCube, and etc. A consistent theme in these new architectures is that a large number of network devices are used to achieve 1:1 oversubscription ratio. However, at most time, data center traffic is far below the peak value. The idle network devices will waste significant amount of energy, which is now a headache for many data center owners. In this paper, we discuss how to save energy consumption in high-density data center networks in a routing perspective. We call this kind of routing energy-aware routing. The key idea is to use as few network devices to provide the routing service as possible, with no little sacrifice on the network performance. Meanwhile, the idle network devices can be shutdown or put into sleep mode for energy saving. We establish the model of energy-aware routing in data center network, and design a heuristic algorithm to achieve the idea. Our simulation in typical data center networks shows that energy-aware routing can effectively save power consumed by network devices. This paper analyzes the performance of a duty-cycled polling-based access mechanism that exploits the Transmission Opportunity Power Save Mode (TXOP PSM) defined in the IEEE 802.11ac to improve the energy efficiency of Wireless Local Area Networks (WLANs) based on the IEEE 802.11. The basic idea behind the proposed approach, named GreenPoll, is to enable contention free periods, based on polling with beacons, during which wireless stations can save energy by turning off their radio transceivers after exchanging data with the access point. The closed expression of energy efficiency of GreenPoll is formulated in this paper and is used to evaluate the performance of GreenPoll considering important parameters like the traffic load, packet length, data rate, and number of stations in the network. Both analytical and simulation results show the high energy efficiency of GreenPoll with gains of up to 330 and 110 when compared to the legacy Distributed Coordination Function (DCF) and the Point Coordination Function (PCF) defined in the IEEE 802.11, respectively. In this paper, we develop an energy efficient routing scheme that takes into account the interference created by existing flows in the network. Unlike previous works, we explicitly study the impact of routing a new flow on the energy consumption of the network. Under certain assumptions on how links are scheduled, we can show that our proposed algorithm is asymptotically (in time) optimal in terms of minimizing the average energy consumption. We also develop a distributed version of the algorithm. Our algorithm automatically detours around a congested area in the network, which helps mitigate network congestion and improve overall network performance. Using simulations, we show that the routes chosen by our algorithm (centralized and distributed) are more energy efficient than the state of the art. This paper deals with an energy saving routing solution, called Energy Saving IP Routing (ESIR), to be applied in an IP network. ESIR operation is integrated with Open Shorthest Path First (OSPF) protocol and allows the selection of the links to be switched off so that the negative effects of the IP topology reconfiguration procedures are avoided. The basic mechanisms which ESIR is based on are the concepts of SPT exportation and move. These mechanisms allow to share a Shortest Path Tree (SPT) between neighbor routers, so that the overall set of active network links can be reduced. Properties of moves are defined and the energy saving problem in an IP network is formulated as the problem of finding the Maximum Set of Compatible Moves (MSCM). The MSCM problem is investigated in two steps: firstly, a relaxed version of the problem, named basic MSCM problem, is considered in which QoS requirements are neglected; in the second step, the solution of the full problem, named QoS-aware MSCM problem, is faced. We prove that the basic MSCM problem can be formulated as the well-known Maximum Clique Problem in a graph; instead the QoS-aware MSCM introduces a condition equivalent to the Knapsack problem. ILP formulations to solve both the problems are given and heuristics to solve them in practical cases are proposed. The performance evaluation shows that in a real ISP network scenario ESIR is able to switch off up to 30 of network links by exploiting over-provisioning adopted by operators in the network resource planning phase and typical daily traffic trend. Energy efficiency is a key issue in wireless ad hoc and sensor networks. Several directions have been explored to maximize network lifetime, among them energy efficient routing. In this paper, we show how to extend the standardized OLSR routing protocol, in order to make it energy efficient. To take into account residual node energy, three new selection algorithms of multipoint relays, based on the minimum residual energy are evaluated, the best one is chosen. This OLSR extension selects the path minimizing the energy consumed in the end-to-end transmission of a flow packet and avoids nodes with low residual energy. We compare this extension with a two-path source routing strategy (with different links or different nodes). An extensive performance evaluation shows that this energy efficient extension maximizes both network lifetime and user data delivered. Service function chaining (SFC) allows the forwarding of traffic flows along a chain of virtual network functions (VNFs). Software defined networking (SDN) solutions can be used to support SFC to reduce both the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impacting the Quality of Service. In this paper, we propose a novel resource allocation architecture which enables energy-aware SFC for SDN-based networks, considering also constraints on delay, link utilization, server utilization. To this end, we formulate the problems of VNF placement, allocation of VNFs to flows, and flow routing as integer linear programming (ILP) optimization problems. Since the formulated problems cannot be solved (using ILP solvers) in acceptable timescales for realistic problem dimensions, we design a set of heuristic to find near-optimal solutions in timescales suitable for practical applications. We numerically evaluate the performance of the proposed algorithms over a real-world topology under various network traffic patterns. Our results confirm that the proposed heuristic algorithms provide near-optimal solutions (at most 14 optimality-gap) while their execution time makes them usable for real-life networks. We consider a dense fading multi-user network with multiple active multi-antenna source-destination pair terminals communicating simultaneously through a large common set of K multi-antenna relay terminals in the full spatial multiplexing mode. We use Shannon-theoretic tools to analyze the tradeoff between energy efficiency and spectral efficiency (known as the power-bandwidth tradeoff) in meaningful asymptotic regimes of signal-to-noise ratio (SNR) and network size. We design linear distributed multi-antenna relay beamforming (LDMRB) schemes that exploit the spatial signature of multi-user interference and characterize their power-bandwidth tradeoff under a system-wide power constraint on source and relay transmissions. The impact of multiple users, multiple relays and multiple antennas on the key performance measures of the high and low SNR regimes is investigated in order to shed new light on the possible reduction in power and bandwidth requirements through the usage of such practical relay cooperation techniques. Our results indicate that point-to-point coded multi-user networks supported by distributed relay beamforming techniques yield enhanced energy efficiency and spectral efficiency, and with appropriate signaling and sufficient antenna degrees of freedom, can achieve asymptotically optimal power-bandwidth tradeoff with the best possible (i.e., as in the cutset bound) energy scaling of K-1 and the best possible spectral efficiency slope at any SNR for large number of relay terminals. Furthermore, our results help to identify the role of interference cancellation capability at the relay terminals on realizing the optimal power- bandwidth tradeoff; and show how relaying schemes that do not attempt to mitigate multi-user interference, despite their optimal capacity scaling performance, could yield a poor power- bandwidth tradeoff. According to recent research, the current Internet wastes energy due to an un-optimized network design, which does not consider the energy consumption of network elements such as routers and switches. Looking toward energy saving networks, a generalized problem called the energy consumption minimized network (EMN) had been proposed. However, due to the NP-completeness of this problem, it requires a considerable amount of time to obtain the solution, making it practically intractable for large-scale networks.In this paper, we re-formulate the NP-complete EMN problem into a simpler one using a newly defined concept called 'traffic centrality'. We then propose a new ant colony-based self-adaptive energy saving routing scheme, referred to as A-ESR, which exploits the ant colony optimization (ACO) method to make the Internet more energy efficient. The proposed A-ESR algorithm heuristically solves the re-formulated problem without any supervised control by allowing the incoming flows to be autonomously aggregated on specific heavily-loaded links and switching off the other lightly-loaded links. Additionally, the A-ESR algorithm adjusts the energy consumption by tuning the aggregation parameter s, which can dramatically reduce the energy consumption during nighttime hours (at the expense of tolerable network delay performance). Another promising capability of this algorithm is that it provides a high degree of self-organizing capabilities due to the amazing advantages of the swarm intelligence of artificial ants. The simulation results in real IP networks show that the proposed A-ESR algorithm performs better than previous algorithms in terms of its energy efficiency. The results also show that this efficiency can be adjusted by tuning s. Data centers are a cost-effective infrastructure for hosting Cloud and Grid applications, but they do incur tremendous energy cost and CO2 emissions. Today's data center network architectures such as Fat-tree and BCube are over-provisioned to guarantee large network capacity and meet peak performance requirement. Networks suffer from inefficient power usage when data center traffic is not high. A solution to this problem is the adoption of network management platform such as OpenNaaS, which can be augmented with energy-aware capabilities. We developed a component for energy monitoring and routing in OpenNaaS. Energy-aware OpenNaaS can support different types of OpenFlow controller; it inherits and enhances network management capabilities, e.g. dynamically obtaining power and topology.In this paper we also discuss the evaluation and selection of energy-aware routing strategies based on an initial prototype of energy-aware OpenNaaS. The target strategies are fine-grained as they combine flow routing algorithms that make routing decisions for the flows and flow scheduling algorithms that schedule the flows on the same link. Differently from previous routing work which focuses on power-minimization problem in data center networks, we aim to optimize energy consumption. Our simulation shows that the combination of priority-based shortest routing and exclusive flow scheduling achieves about 5 -35 higher energy efficiency without performance degradation. Nodes in a mobile ad hoc network are typically battery-powered. Energy consumption is therefore an important metric to consider in designing routing protocols for such networks. In this paper, we present our work on integrating energy-efficiency aspects into a standard MANET routing protocol, OLSR. We study the impact of different protocol modifications that aim to increase node lifetime and network performance, and evaluate them under a range of different scenarios. For static networks, little to no performance improvements are achieved, but significant performance gains of 15 to 30 are possible for more dynamic network topologies. At the same time, not all modifications are equally beneficial. In particular, changing the MPR selection criteria, a key OLSR optimization mechanism, is not promising under any of the studied scenarios. Unbalanced energy consumption is an inherent problem in wireless sensor networks characterized by multihop routing and many-to-one traffic pattern, and this uneven energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of maximizing network lifetime through balancing energy consumption for uniformly deployed data-gathering sensor networks. We formulate the energy consumption balancing problem as an optimal transmitting data distribution problem by combining the ideas of corona-based network division and mixed-routing strategy together with data aggregation. We first propose a localized zone-based routing scheme that guarantees balanced energy consumption among nodes within each corona. We then design an offline centralized algorithm with time complexity O(n) (n is the number of coronas) to solve the transmitting data distribution problem aimed at balancing energy consumption among nodes in different coronas. The approach for computing the optimal number of coronas in terms of maximizing network lifetime is also presented. Based on the mathematical model, an energy-balanced data gathering (EBDG) protocol is designed and the solution for extending EBDG to large-scale data-gathering sensor networks is also presented. Simulation results demonstrate that EBDG significantly outperforms conventional multihop transmission schemes, direct transmission schemes, and cluster-head rotation schemes in terms of network lifetime. Energy efficient communication devices are essential to minimize the operational cost of future networks and to reduce the negative effects of global warming. In this paper we propose a novel energy reduction approach on network level that takes load-dependent energy consumption information of communication equipment into account. Case study calculation results show that energy savings of more than 35 and with it operational cost can be saved by applying energy profile aware routing. Since ad hoc and sensor networks can be composed of a very large number of devices, the scalability of network protocols is a major design concern. Furthermore, network protocols must be designed to prolong the battery lifetime of the devices. However, most existing routing techniques for ad hoc networks are known not to scale well. On the other hand, the so-called geographical routing algorithms are known to be scalable but their energy efficiency has never been extensively and comparatively studied. In a geographical routing algorithm, data packets are forwarded by a node to its neighbor based on their respective positions. The neighborhood of each node is constituted by the nodes that lie within a certain radio range. Thus, from the perspective of a node forwarding a packet, the next hop depends on the width of the neighborhood it perceives. The analytical framework proposed in this paper allows to analyze the relationship between the energy efficiency of the routing tasks and the extension of the range of the topology knowledge for each node. A wider topology knowledge may improve the energy efficiency of the routing tasks but increases the cost of topology information due to signaling packets needed to acquire this information. The problem of determining the optimal topology knowledge range for each node to make energy efficient geographical routing decisions is tackled by integer linear programming. It is shown that the problem is intrinsically localized, i.e., a limited topology knowledge is sufficient to make energy efficient forwarding decisions. The leading forwarding rules for geographical routing are compared in this framework, and the energy efficiency of each of them is studied. Moreover, a new forwarding scheme, partial topology knowledge forwarding (PTKF), is introduced, and shown to outperform other existing schemes in typical application scenarios. A probe-based distributed protocol for knowledge range adjustment (PRADA) is finally introduced that allows each node to efficiently select online its topology knowledge range. PRADA is shown to rapidly converge to a near-optimal solution. We propose a novel localized routing protocol for wireless sensor networks (WSN) that is energy-efficient and guarantees delivery. We prove that it is constant factor of the optimum for dense networks. To forward a packet, a node s in graph G computes the cost of the energy weighted shortest path (SP) between s and each of its neighbors which are closer to the destination than itself. It then selects node x which minimizes the ratio of the cost of the SP to the progress towards the destination. It then sends the message to the first node on the SP from s to x: say node x'. Node x' restarts the same greedy routing process until the destination is reached or the routing fails. To recover from failure, our algorithm invokes face routing that guarantees delivery. This work is the first to optimize energy consumption of face routing. First, we build a connected dominating set from graph G, second we compute its Gabriel graph to obtain the planar graph G'. Face routing is applied on G' only to decide which edges to follow in the recovery process. On each edge, greedy routing is used. This two-phase (greedy-Face) end-to-end routing process (EtE) reiterates until the final destination is reached. Simulation results show that EtE outperforms several existing geographical routing on energy consumption metric.