Cloud Computing: Survey on Energy Efficiency <s> Energy Efficiency <s> Most Ethernet interfaces available for deployment in switches and hosts today can operate in a variety of different low power modes. However, currently these modes have very limited usage models. They do not take advantage of periods of inactivity, when the links remain idle or under-utilized. In this study, we propose methods that allow for detection of such periods to obtain energy savings with little impact on loss or delay. We evaluate our methods on a wide range of real-time traffic traces collected at a high-speed backbone switch within our campus LAN. Our results show that Ethernet interfaces at both ends can be put in extremely low power modes anywhere from 40%-98% of the time observed. In addition, we found that approximately 37% of interfaces studied (on the same switch) can be put in low power modes simultaneously which opens the potential for further energy savings in the switching fabric within the switch. <s> BIB001 </s> Cloud Computing: Survey on Energy Efficiency <s> Energy Efficiency <s> We present the design and evaluation of two forms of power management schemes that reduce the energy consumption of networks. The first is based on putting network components to sleep during idle times, reducing energy consumed in the absence of packets. The second is based on adapting the rate of network operation to the offered workload, reducing the energy consumed when actively processing packets. ::: ::: For real-world traffic workloads and topologies and using power constants drawn from existing network equipment, we show that even simple schemes for sleeping or rate-adaptation can offer substantial savings. For instance, our practical algorithms stand to halve energy consumption for lightly utilized networks (10-20%). We show that these savings approach the maximum achievable by any algorithms using the same power management primitives. Moreover this energy can be saved without noticeably increasing loss and with a small and controlled increase in latency (<10ms). Finally, we show that both sleeping and rate adaptation are valuable depending (primarily) on the power profile of network equipment and the utilization of the network itself. <s> BIB002 </s> Cloud Computing: Survey on Energy Efficiency <s> Energy Efficiency <s> Current network infrastructures exhibit poor power efficiency, running network devices at full capacity all the time regardless of the traffic demand and distribution over the network. Most research on router power management are at component level or link level, treating routers as isolated devices. A complementary approach is to facilitate power management at network level by routing traffic through different paths to adjust the workload on individual routers or links. Given the high path redundancy and low link utilization in today's large networks, this approach can potentially allow more network devices or components to go into power saving mode. This paper proposes an intra-domain traffic engineering mechanism, GreenTE, which maximizes the number of links that can be put into sleep under given performance constraints such as link utilization and packet delay. Using network topologies and traffic data from several wide-area networks, our evaluation shows that GreenTE can reduce line-cards' power consumption by 27% to 42% under constraints that the maximum link utilization is below 50% and the network diameter remains the same as in shortest path routing. <s> BIB003 </s> Cloud Computing: Survey on Energy Efficiency <s> Energy Efficiency <s> Energy consumption of the Internet is already substantial and it is likely to increase as operators deploy faster equipment to handle popular bandwidth-intensive services, such as streaming and video-on-demand. Existing work on energy saving considers local adaptation relying primarily on hardware-based techniques, such as sleeping and rate adaptation. We argue that a complete solution requires a network-wide approach that works in conjunction with local measures. However, traditional traffic engineering objectives do not include energy. This paper presents Energy-Aware Traffic engineering (EATe), a technique that takes energy consumption into account while achieving the same traffic rates as the energy-oblivious approaches. EATe uses a scalable, online technique to spread the load among multiple paths so as to increase energy savings. Our extensive ns-2 simulations over realistic topologies show that EATe succeeds in moving 21% of the links to the sleep state, while keeping the same sending rates and being close to the optimal energy-aware solution. Further, we demonstrate that EATe successfully handles changes in traffic load and quickly restores a low overall energy state. Alternatively, EATe can move links to lower energy levels, resulting in energy savings of 8%. Finally, EATe can succeed in making 16% of active routers sleep. <s> BIB004 </s> Cloud Computing: Survey on Energy Efficiency <s> Energy Efficiency <s> The power consumption of the Internet and datacenter networks is already significant, and threatens to shortly hit the power delivery limits while the hardware is trying to sustain ever-increasing traffic requirements. Existing energy-reduction approaches in this domain advocate recomputing network configuration with each substantial change in demand. Unfortunately, computing the minimum network subset is computationally hard and does not scale. Thus, the network is forced to operate with diminished performance during the recomputation periods. In this paper, we propose REsPoNse, a framework which overcomes the optimality-scalability trade-off. The insight in REsPoNse is to identify a few energy-critical paths off-line, install them into network elements, and use a simple online element to redirect the traffic in a way that enables large parts of the network to enter a low-power state. We evaluate REsPoNse with real network data and demonstrate that it achieves the same energy savings as the existing approaches, with marginal impact on network scalability and application performance. <s> BIB005 </s> Cloud Computing: Survey on Energy Efficiency <s> Energy Efficiency <s> 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. <s> BIB006
Energy efficiency refers to a reduction of energy used for a given service or level of activity, as defined by the World Energy Council  . However, defining the energy efficiency of data center equipment is extremely difficult [Fanara 2007 ] because it represents a complex system with a large number of components from various research areas such as computing, networking, management, and the like. The provided service of such a system is too diverse to be covered in detail. On the one hand, surveys such as that found in define an energy model through static and dynamic power consumption, which deals only with energy waste while running idle. On the other hand,  define a difference between energy used by ICT and auxiliary equipment in order to measure energy losses by the latter. However, we are interested in energy efficiency in general, and thus we combine these two in order to define energy efficiency from a more general perspective. Figure 2 shows an arbitrary system as a set of interconnected components, in which each component can be observed as a different (sub)system. Therefore, every (sub)system can be optimized for itself, which can affect the energy efficiency of other related systems. Furthermore, each system requires an input energy for performing a certain task, where a task is an abstract assignment that the system has to perform to fulfil its purpose. To improve the energy efficiency of a system, first it is necessary to identify problems that degrade efficiency. Therefore, we identify two critical points where energy is not used in an efficient way but is instead lost or wasted. Both terms define inefficient use of energy from an agnostic point of view, where energy loss refers to energy brought to the system but not consumed for its main task (e.g., energy lost due to transport and conversion). This also includes energy used by supporting subsystems, such as cooling or lighting within a data center whose main task is the provision of cloud services. Energy waste refers to energy used by the system's main task but without useful output (e.g., energy used while running in idle mode). Additionally, useless work by the system is also considered energy waste; for example, for a cooling subsystem, this would mean keeping the cooling at maximum during the night when temperatures are lower. Both critical points are shown in Figure 3 . Based on these definitions, two goals are defined for reducing energy loss and two goals for reducing energy waste, thus improving the energy efficiency: -L1. The first goal is minimizing a percentage of input energy that is not consumed by a subsystem. This can be done by implementing more efficient components (e.g., using more efficient power supply units for servers that leak less energy). -L2. The second goal is to reduce the overhead of supporting systems (i.e., systems that do not perform the main task of the system), for example, by implementing a single cooling unit for the entire cabinet instead of cooling each rack server separately. -W1. The third goal is to reduce idle run of the system and increase utilization or achieve zero energy consumption when no output is produced (i.e., during idle time). This also implies achieving a proportional increase of energy consumption with system output (e.g., to provide twice as much bandwidth, a network router requires twice the amount of energy or less). -W2. The fourth goal is to minimize energy consumption where the system performs redundant operations. This can be done by implementing smart functions and subsystems, such as implementing an optimized algorithm that does not require redundant steps to perform the same task. The listed goals are taken as a basis for the literature review in our search to find current as well as future research directions that focus on improving the energy efficiency of cloud computing infrastructure. Figure 4 shows data center domains and their energy cascades as they are covered in this article, starting with Network and BIB001 BIB002 Traffic engineering and routing BIB003 ] BIB004 BIB005 ] BIB006 Server domains and moving on to the CMS and Appliance domains. Finally, relevant papers are summarized for each domain in Tables I, II, III, IV and V. The following section covers the Network, the first hardware domain in this article.