Rising energy consumption of IT infrastructure concerns have spurred the development of more power efficient networking equipment and algorithms. When old equipment just drew an almost constant amount of power regardless of the traffic load, there were some efforts to minimize the total energy usage by modifying routing decisions to aggregate traffic in a minimal set of links, creating the opportunity to power off some unused equipment during low traffic periods. New equipment, with power profile functions depending on the offered load, presents new challenges for optimal routing. The goal now is not just to power some links down, but to aggregate and or spread the traffic so that devices operate in their sweet spot in regards to network usage. In this paper we present an algorithm that, making use of the ant colonization algorithm, computes, in a decentralized manner, the routing tables so as to minimize global energy consumption. Moreover, the resulting algorithm is also able to track changes in the offered loadand react to them in real time. HighlightsNew network links show load dependant energy consumption.Energy saving routing algorithm for load dependant links.Simply powering down links can increase energy consumption.Obtained power savings in the 10-20 interval for real networks.
New proposals that take into account the different power profiles are also known in the literature. For instance, @cite_10 considers super-linear energy costs functions in the analysis of the maximum power savings attainable by powering down part of the network. @cite_5 the authors formulate a minimization problem considering the links formed by IEEE 802.3az links. Similarly, the authors of @cite_8 address a similar problem and compare the results obtained with both super and sub-linear power profiles. The same problem is also studied in @cite_22 @cite_6 , this time considering bundle links between adjacent routers. The authors find out in @cite_6 that traffic consolidation does not always lead to energy savings.
@cite_22 @cite_8 @cite_6 @cite_5 @cite_10
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The paper copes with the reduction of network power consumption by the definition of new routing algorithms, able to take into account the energy consumed by the network devices. In particular, based on the power consumption characterization of the network devices obtained using the Energy Profile (EP) concept, the paper presents the analysis of the exact solution of the Energy Aware Routing (EAR) problem solved with a Mixed Integer Programming solver. The analysis is aimed at evaluating the impact on the performance of three relevant aspects of the problem: the approximation of the actual EP, the traffic load and the topology of the network. Furthermore, the paper proposes a heuristic solution of the EAR, denoted as Dijkstra-based Power Aware Routing Algorithm (DPRA), defined in order to cope with the complexity of the exact solution. 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. In this paper we study the behavior of a general optimization model for reducing the power consumption of core networks employing energy-aware network equipment. Specifically, we assess how the energy profiles of the devices affect the outcome of the optimization model and hence determine the general power saving policy. The computational analysis performed on several real topologies shows that the widespread traffic consolidation strategy does not always provide the best results. In fact, for devices presenting a cubic (convex in general) energy profile, the highest energy savings are achieved by spreading the traffic on the network. Energy Efficient Ethernet, as defined by the IEEE 802.3az standard, has shown not to be as efficient as originally expected given the large values of the transition times between the active and sleep power modes. In fact, EEE performs nearly optimal only when the link load is either very low or very high, but never at medium loads. So, in order to achieve large power savings, then it is necessary to design a flow allocation algorithm that allocates traffic demands on links that avoid medium traffic loads on links, since these are far from optimal. This work defines EEE-FA, an energy-aware flow allocation algorithm that computes the best possible route in terms of energy consumption for a given network load condition. Essentially, EEE-FA computes the K-shortest paths for a given traffic demand and evaluates the consumption impact of allocating the traffic demand on each of them, in order to further select that route with minimum energy consumption impact for a given network status. This algorithm is compared with shortest path routing and it is shown that important energy savings may be achieved, however at the expense of increasing the global network traffic load and the average number of hops per demand as a consequence of using sub-optimal (in terms of distance) routes. Nowadays two main approaches are being pursued to reduce energy consumption of networks: the use of sleep modes in which devices enter a low-power state during inactivity periods, and the adoption of energy proportional mechanisms where the device architecture is designed to make energy consumption proportional to the actual load. Common to all the proposals is the evaluation of energy saving performance by means of simulation or experimental evidence, which typically consider a limited set of benchmarking scenarios.In this paper, we do not focus on a particular algorithm or procedure to offer energy saving capabilities in networks, but rather we formulate a theoretical model based on random graph theory that allows to estimate the potential gains achievable by adopting sleep modes in networks where energy proportional devices are deployed. Intuitively, when some devices enter sleep modes some energy is saved. However, this saving could vanish because of the additional load (and power consumption) induced onto the active devices. The impact of this effect changes based on the degree of load proportionality. As such, it is not simple to foresee which are the scenarios that make sleep mode or energy proportionality more convenient.Instead of conducting detailed simulations, we consider simple models of networks in which devices (i.e., nodes and links) consume energy proportionally to the handled traffic, and in which a given fraction of nodes are put into sleep mode. Our model allows to predict how much energy can be saved in different scenarios. The results show that sleep modes can be successfully combined with load proportional solutions. However, if the static power consumption component is one order of magnitude less than the load proportional component, then sleep modes become not convenient anymore. Thanks to random graph theory, our model gauges the impact of different properties of the network topology. For instance, highly connected networks tend to make the use of sleep modes more convenient.