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Meeting Quality of Service (QoS) and network reliability has been the main target for telecommunications operators in the last years. This has been done completely ignoring energy waste introduced to achieve this target. In fact, today’s most telecommunications networks are often provisioned for worst-case or busy-hour load, and this load typically exceeds their long-term utilization by a wide margin; Barford, (2008) shows that current network nodes have a constant power consumption that does not depend on the actual traffic load they face. Therefore, most of the energy consumed in networks today is wasted (Sprint, 2007; Jardosh, 2007).
The increment of the temperature of the places where network devices reside is a non-marginal side effect of high-energy dissipation. It derives a consequent further waste of energy used by cooling machines to maintain the temperature of the local environment below a certain threshold.
The constant rising cost of the energy and the need to reduce the global greenhouse gas emission make this occurrence unsustainable: today, 37% of the total ICT (Information and Communications Technology) emissions are due to telecommunications companies infrastructures and devices (The Climate Group, 2008). For this reason, addressing energy efficiency challenges in both wireline and wireless networks is an important matter and it is receiving considerable attention in the literature today (Gupta, 2003; Nedevschi, 2008; Lombardo, 2010a; Lombardo, 2012a; Chiaraviglio, 2010; Panarello, 2012; Lombardo, 2012b; Lombardo, 2010b); moreover many research projects (Econet website, 2010; Trend website, 2010; Greentouch website, 2011) have been started on this topic. Thus, some novel hardware devices, so-called “green routers”, are expected in the near future to allow entering different power states (Cisco, 2009) according to the input traffic.
The energy aware techniques to be used in a green router depend on a number of factors, including the role of the router in the network, the profile of incoming traffic, the hardware complexity and the related costs with respect to the energy we can potentially save and the QoS we want to guarantee to the users (Hu, 2011).