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 recently approved Energy Efficient Ethernet standard IEEE 802.3az achieves energy savings by using a low power mode when the link is idle. However, those savings heavily depend on the traffic patterns, due to the overhead inherent in transitions between active and low power modes. This makes it impractical to estimate energy savings through measurements or simulations in all relevant scenarios. In this letter we present an analytical model to estimate the energy consumption of an Energy Efficient Ethernet link, based on simple traffic parameters. The model is validated through simulation and experimental data. 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. We study network optimization that considers power minimization as an objective. Studies have shown that mechanisms such as speed scaling can significantly reduce the power consumption of telecommunication networks by matching the consumption of each network element to the amount of processing required for its carried traffic. Most existing research on speed scaling focuses on a single network element in isolation. We aim for a network-wide optimization. Specifically, we study a routing problem with the objective of provisioning guaranteed speed bandwidth for a given demand matrix while minimizing power consumption. Optimizing the routes critically relies on the characteristic of the speed-power curve f(s), which is how power is consumed as a function of the processing speed s. If f is superadditive, we show that there is no bounded approximation in general for integral routing, i.e., each traffic demand follows a single path. This contrasts with the well-known logarithmic approximation for subadditive functions. However, for common speed-power curves such as polynomials f(s) = μsα, we are able to show a constant approximation via a simple scheme of randomized rounding. We also generalize this rounding approach to handle the case in which a nonzero startup cost σ appears in the speed-power curve, i.e., f(s) = σ + μsα, if s >; 0; 0, if s = 0. We present an O((σ μ)1 α)-approximation, and we discuss why coming up with an approximation ratio independent of the startup cost may be hard. Finally, we provide simulation results to validate our algorithmic approaches.