Green Data Centers: A Survey, Perspectives, and Future Directions <s> I. INTRODUCTION <s> Power consumption and energy efficiency are important factors in the initial design and day-to-day management of computer systems. Researchers and system designers need benchmarks that characterize energy efficiency to evaluate systems and identify promising new technologies. To predict the effects of new designs and configurations, they also need accurate methods of modeling power consumption. <s> BIB001 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> I. INTRODUCTION <s> The data centers used to create cloud services represent a significant investment in capital outlay and ongoing costs. Accordingly, we first examine the costs of cloud service data centers today. The cost breakdown reveals the importance of optimizing work completed per dollar invested. Unfortunately, the resources inside the data centers often operate at low utilization due to resource stranding and fragmentation. To attack this first problem, we propose (1) increasing network agility, and (2) providing appropriate incentives to shape resource consumption. Second, we note that cloud service providers are building out geo-distributed networks of data centers. Geo-diversity lowers latency to users and increases reliability in the presence of an outage taking out an entire site. However, without appropriate design and management, these geo-diverse data center networks can raise the cost of providing service. Moreover, leveraging geo-diversity requires services be designed to benefit from it. To attack this problem, we propose (1) joint optimization of network and data center resources, and (2) new systems and mechanisms for geo-distributing state. <s> BIB002 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> I. INTRODUCTION <s> Numerous studies have shown that datacenter computers rarely operate at full utilization, leading to a number of proposals for creating servers that are energy proportional with respect to the computation that they are performing. In this paper, we show that as servers themselves become more energy proportional, the datacenter network can become a significant fraction (up to 50%) of cluster power. In this paper we propose several ways to design a high-performance datacenter network whose power consumption is more proportional to the amount of traffic it is moving -- that is, we propose energy proportional datacenter networks. We first show that a flattened butterfly topology itself is inherently more power efficient than the other commonly proposed topology for high-performance datacenter networks. We then exploit the characteristics of modern plesiochronous links to adjust their power and performance envelopes dynamically. Using a network simulator, driven by both synthetic workloads and production datacenter traces, we characterize and understand design tradeoffs, and demonstrate an 85% reduction in power --- which approaches the ideal energy-proportionality of the network. Our results also demonstrate two challenges for the designers of future network switches: 1) We show that there is a significant power advantage to having independent control of each unidirectional channel comprising a network link, since many traffic patterns show very asymmetric use, and 2) system designers should work to optimize the high-speed channel designs to be more energy efficient by choosing optimal data rate and equalization technology. Given these assumptions, we demonstrate that energy proportional datacenter communication is indeed possible. <s> BIB003 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> I. INTRODUCTION <s> Although there is tremendous interest in designing improved networks for data centers, very little is known about the network-level traffic characteristics of data centers today. In this paper, we conduct an empirical study of the network traffic in 10 data centers belonging to three different categories, including university, enterprise campus, and cloud data centers. Our definition of cloud data centers includes not only data centers employed by large online service providers offering Internet-facing applications but also data centers used to host data-intensive (MapReduce style) applications). We collect and analyze SNMP statistics, topology and packet-level traces. We examine the range of applications deployed in these data centers and their placement, the flow-level and packet-level transmission properties of these applications, and their impact on network and link utilizations, congestion and packet drops. We describe the implications of the observed traffic patterns for data center internal traffic engineering as well as for recently proposed architectures for data center networks. <s> BIB004 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> I. INTRODUCTION <s> Large-scale Internet applications, such as content distribution networks, are deployed across multiple datacenters and consume massive amounts of electricity. To provide uniformly low access latencies, these datacenters are geographically distributed and the deployment size at each location reflects the regional demand for the application. Consequently, an application's environmental impact can vary significantly depending on the geographical distribution of end-users, as electricity cost and carbon footprint per watt is location specific. In this paper, we describe FORTE: Flow Optimization based framework for request-Routing and Traffic Engineering. FORTE dynamically controls the fraction of user traffic directed to each datacenter in response to changes in both request workload and carbon footprint. It allows an operator to navigate the three-way tradeoff between access latency, carbon footprint, and electricity costs and to determine an optimal datacenter upgrade plan in response to increases in traffic load. We use FORTE to show that carbon taxes or credits are impractical in incentivizing carbon output reduction by providers of large-scale Internet applications. However, they can reduce carbon emissions by 10% without increasing the mean latency nor the electricity bill. <s> BIB005 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> I. INTRODUCTION <s> Numerous studies have shown that datacenter networks typically see loads of between 5% – 25% but the energy draw of these networks is equal to operating them at maximum load. In this paper, we propose a novel way to make these networks more energy proportional - that is, the energy draw scales with the network load. We propose the idea of traffic aggregation, in which low traffic from N links is combined together to create H < N streams of high traffic. These streams are fed to H switch interfaces which run at maximum rate while the remaining interfaces are switched to the lowest possible one. We show that this merging can be accomplished with minimal latency and energy costs (less than 0.1W) while simultaneously allowing us a deterministic way of switching link rates between maximum and minimum. Using simulations based on previously developed traffic models, we show that 49% energy savings are obtained for 5% of the load while we get an energy savings of 22% for a 50% load. Hence, forasmuch as the packet losses are statistically insignificant, the results show that energy-proportional datacenter networks are indeed possible. <s> BIB006 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> I. INTRODUCTION <s> Cloud computing offers utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of electrical energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only minimize operational costs but also reduce the environmental impact. In this paper, we define an architectural framework and principles for energy-efficient Cloud computing. Based on this architecture, we present our vision, open research challenges, and resource provisioning and allocation algorithms for energy-efficient management of Cloud computing environments. The proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS). In particular, in this paper we conduct a survey of research in energy-efficient computing and propose: (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering QoS expectations and power usage characteristics of the devices; and (c) a number of open research challenges, addressing which can bring substantial benefits to both resource providers and consumers. We have validated our approach by conducting a performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant cost savings and demonstrates high potential for the improvement of energy efficiency under dynamic workload scenarios. <s> BIB007
As hosts of large-scale service applications, data centers now play a crucial role in modern Information Technology (IT) infrastructures. With the development of cloud computing, data centers are growing exponentially in number and size. A new study from International Data Corporation (IDC) predicts that the total number of data centers deployed worldwide will peak at 8.6 million in 2017  . Consequently, the extremely high energy consumption and carbon dioxide emissions of data centers are becoming a major concern worldwide. The Natural Resources Defense Council (NRDC) reported that 91 billion KWh of electricity was consumed by U.S. data centers in 2013. Continuing this trend, NRDC estimates that the electricity usage will reach 140 billion KWh by 2020. As a result, data center electricity consumption will cost American businesses $13 billion annually in electricity bills and result in the emission of nearly 100 million metric tons of carbon pollution per year [2] . Globally, it had revealed that power requirements grew by 63% to 38 Gigawatts in 2012, up from 24 Gigawatts in 2011  . Moreover, data centers will consume 8% of the worldwide electricity supply by 2020 while this fraction was about 1.3% in 2010 BIB005 . Therefore, making data centers "green" can reduce both costs for energy consumption and carbon dioxide emissions. Existing data centers often operate at low utilization because of over-provisioning and fragmentation of resources BIB002 ,  , leading to considerable energy waste. According to a McKinsey study in 2008  , the typical utilization ratio is approximately 6%. A Gartner report from 2012  found the typical utilization rates to be in the 7% to 12% range, slightly better than the result of the 2008 McKinsey study. Recently, Google Inc. reported that they can improve the utilization of their servers to relatively high rates of 20-40%  . Many studies BIB006 , BIB003 , BIB004 have also shown that data center networks experience high underutilization, with typical utilization ranging between 5% and 25%. What makes the matter worse is that the low utilization of these servers and network resources also cause the waste of other supporting infrastructure, such as power distribution and cooling. From these data, we can see that the present energy efficiency of data centers is extremely low and that there is great potential for reducing energy consumption in data centers. Motivated by the high energy consumption and low utilization of data centers, many research efforts over the past few years have focused on the design of green data center infrastructures and services. Generally, these approaches can be categorized into two major classes BIB001 : 1) those that adopt "green" equipment in the preliminary design and building phase of the data centers, and 2) those that increase the efficiency of the daily expenditures incurred during the operation of the data centers. In this paper, we focus on the latter class of methods, i.e., those that emphasize managing the procedures for running and operating data centers in a "green" manner. To this end, research studies on the development of green data centers have concentrated on the following aspects: 1) decreasing the power consumption of data center resources, 2) increasing the utilization of data centers, 3) controlling the thermal behavior of data centers, and 4) developing green metrics, monitoring and experimental techniques. From the perspective of data center architecture, the fundamental components of data centers are 1) computing servers, 2) connection networks, and 3) cooling equipment. Correspondingly, green data center technologies can be applied to a individual component or to a hybrid scheme of components. Although obvious progress has been made in the construction of green data centers over the past decade, there is still a large gap, and therefore a large opportunity for savings, between the current average and the characteristics of a bestpractice green data center  . In this paper, we present a survey of the current state-of-the-art research on green data centers, as summarized in Fig. 1 . Some recent surveys have conducted on energy-aware resource allocation for cloud computing BIB007 or green data center networks [16] . Different from them, our survey covers many aspects of the development of green data centers. Our main contributions are two-fold: 1) we discuss the key insights underlying recent strategies and compare the relevant proposals, and 2) we note the future research challenges and directions for various aspects of the design of green data centers. The remainder of this paper is organized as follows. First, we discuss the studies on and challenges of decreasing power consumption in Section II. We introduce efforts toward and opportunities for the efficient utilization of data center re-Green Monitoring and Experimental Techniques sources in Section III. We explore studies and potential cooling issues related to data center temperature and thermal control in Section IV, and we describe the development of and guidelines for green metrics, monitoring and experimental techniques in Section V. Finally, we conclude the paper in Section VI.