Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Data Center Network Traffic Engineering <s> In this paper, we consider migration of virtual machines in a data center to minimize network power consumption. Network power is consumed when switches are turned on, and conserved when they are turned off; the optimization problem then is to site virtual machines within the data center to achieve connectivity and desired bandwidth while turning on as few switches as possible. Depending upon specifics of the permissible optimization, and topology considerations within the data center, this optimization problem can be easy, or hard. We fully taxonomize the suite of optimization problems in this general space, showing that the most complex problem (placement and routing of virtual machines in a topologically-rich data center network) is NP- hard. We offer a placement technique based on a classic VLSI placement algorithm, and demonstrate efficacy on a trace set derived from a production data center. <s> BIB001 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Data Center Network Traffic Engineering <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> BIB002 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Data Center Network Traffic Engineering <s> Today's data centers need efficient traffic management to improve resource utilization in their networks. In this work, we study a joint tenant (e.g., server or virtual machine) placement and routing problem to minimize traffic costs. These two complementary degrees of freedom—placement and routing—are mutually-dependent, however, are often optimized separately in today's data centers. Leveraging and expanding the technique of Markov approximation, we propose an efficient online algorithm in a dynamic environment under changing traffic loads. The algorithm requires a very small number of virtual machine migrations and is easy to implement in practice. Performance evaluation that employs the real data center traffic traces under a spectrum of elephant and mice flows, demonstrates a consistent and significant improvement over the benchmark achieved by common heuristics. <s> BIB003 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Data Center Network Traffic Engineering <s> Power optimization has become a key challenge in the design of large-scale enterprise data centers. Existing research efforts focus mainly on computer servers to lower their energy consumption, while only few studies have tried to address the energy consumption of data center networks (DCNs), which can account for 20% of the total energy consumption of a data center. In this paper, we propose CARPO, a correlation-aware power optimization algorithm that dynamically consolidates traffic flows onto a small set of links and switches in a DCN and then shuts down unused network devices for energy savings. In sharp contrast to existing work, CARPO is designed based on a key observation from the analysis of real DCN traces that the bandwidth demands of different flows do not peak at exactly the same time. As a result, if the correlations among flows are considered in consolidation, more energy savings can be achieved. In addition, CARPO integrates traffic consolidation with link rate adaptation for maximized energy savings. We implement CARPO on a hardware testbed composed of 10 virtual switches configured with a production 48-port OpenFlow switch and 8 servers. Our empirical results with Wikipedia traces demonstrate that CARPO can save up to 46% of network energy for a DCN, while having only negligible delay increases. CARPO also outperforms two state-of-the-art baselines by 19.6% and 95% on energy savings, respectively. Our simulation results with 61 flows also show the superior energy efficiency of CARPO over the baselines. <s> BIB004 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Data Center Network Traffic Engineering <s> With the rapid growth of data center networks in providing a myriad of services and applications to store, access, and process data, the power consumption of data center networks has become critically important and must be efficiently managed. In this paper, we discuss the power optimization of data center networks in a hierarchical perspective. We establish a two-level power optimization model to reduce the power consumption of data center networks by switching off network switches and links while still guaranteeing full connectivity and maximum link utilization. After having shown that the addressed problem falls in the class of capacity constraint multi-commodity flow problems, we design several simple heuristic algorithms to solve the problem. Our simulations show that hierarchical energy optimization can effectively save power consumption in data center networks. <s> BIB005 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Data Center Network Traffic Engineering <s> Cloud based applications and services require high performance and strong reliability provided by data center networks. To overcome the problem of traditional tree based data center network, recently many new network architectures are proposed, such as Fat-Tree and BCube. They use aggressively over-provisioned network devices and links to achieve 1:1 oversubscription ratio. However, most of the time data center traffic is far below the peak value and a large number of idle network devices and links in data centers consume a significant amount of power, which is now becoming a big problem for many cloud providers.In this paper, we aim to reduce the power consumption of high-density data center networks from the routing perspective while meeting the network performance requirement. We call this kind of routing throughput-guaranteed power-aware routing. The essence of our idea is to use as little network power as possible to provide the routing service, without significantly compromise on the network performance. The idle network devices and links can be shut down or put into the sleep mode for power saving. Extensive simulations conducted in typical data center networks show that our power-aware routing can effectively reduce the power consumption of network devices, especially under low network loads. <s> BIB006 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Data Center Network Traffic Engineering <s> The popularization of cloud computing has raised concerns over the energy consumption that takes place in data centers. In addition to the energy consumed by servers, the energy consumed by large numbers of network devices emerges as a significant problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering, which is usually adapted from traditional networks. We propose a new framework to embrace the new opportunities brought by combining some special features of data centers with traffic engineering. Based on this framework, we characterize the problem of achieving energy efficiency with a time-aware model, and we prove its NP-hardness with a solution that has two steps. First, we solve the problem of assigning virtual machines (VM) to servers to reduce the amount of traffic and to generate favorable conditions for traffic engineering. The solution reached for this problem is based on three essential principles that we propose. Second, we reduce the number of active switches and balance traffic flows, depending on the relation between power consumption and routing, to achieve energy conservation. Experimental results confirm that, by using this framework, we can achieve up to 50 percent energy savings. We also provide a comprehensive discussion on the scalability and practicability of the framework. <s> BIB007 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Data Center Network Traffic Engineering <s> Data centers consume significant amounts of energy. As severs become more energy efficient with various energy saving techniques, the data center network (DCN) has been accounting for 20% or more of the energy consumed by the entire data center. While DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns. The objective of this work is to improve the energy efficiency of DCNs during off-peak traffic time by powering off idle devices. Although there exist a number of energy optimization solutions for DCNs, they consider only either the hosts or network, but not both. In this paper, we propose a joint optimization scheme that simultaneously optimizes virtual machine (VM) placement and network flow routing to maximize energy savings, and we also build an OpenFlow based prototype to experimentally demonstrate the effectiveness of our design. First, we formulate the joint optimization problem as an integer linear program, but it is not a practical solution due to high complexity. To practically and effectively combine host and network based optimization, we present a unified representation method that converts the VM placement problem to a routing problem. In addition, to accelerate processing the large number of servers and an even larger number of VMs, we describe a parallelization approach that divides the DCN into clusters for parallel processing. Further, to quickly find efficient paths for flows, we propose a fast topology oriented multipath routing algorithm that uses depth-first search to quickly traverse between hierarchical switch layers and uses the best-fit criterion to maximize flow consolidation. Finally, we have conducted extensive simulations and experiments to compare our design with existing ones. The simulation and experiment results fully demonstrate that our design outperforms existing hostor network-only optimization solutions, and well approximates the ideal linear program. <s> BIB008 </s> Green Data Centers: A Survey, Perspectives, and Future Directions <s> B. Data Center Network Traffic Engineering <s> Data center power optimization has recently received a great deal of research attention. For example, server consolidation has been demonstrated as one of the most effective energy saving methodologies. Likewise, traffic consolidation has also been recently proposed to save energy for data center networks (DCNs). However, current research on data center power optimization focuses on servers and DCN separately. As a result, the optimization results are often inferior, because server consolidation without considering the DCN may cause traffic congestion and thus degraded network performance. On the other hand, server consolidation may change the DCN topology, allowing new opportunities for energy savings. In this paper, we propose PowerNetS, a power optimization strategy that leverages workload correlation analysis to jointly minimize the total power consumption of servers and the DCN. The design of PowerNetS is based on the key observations that the workloads of different servers and DCN traffic flows do not peak at exactly the same time. Thus, more energy savings can be achieved if the workload correlations are considered in server and traffic consolidations. In addition, PowerNetS considers the DCN topology during server consolidation, which leads to less inter-server traffic and thus more energy savings and shorter network delays. We implement PowerNetS on a hardware testbed composed of 10 virtual switches configured with a production 48port OpenFlow switch and 6 servers. Our empirical results with Wikipedia, Yahoo!, and IBM traces demonstrate that PowerNetS can save up to 51.6% of energy for a data center. PowerNetS also outperforms two state-of-the-art baselines by 44.3% and 15.8% on energy savings, respectively. Our simulation results with 72 switches and 122 servers also show the superior energy efficiency of PowerNetS over the baselines. <s> BIB009
Traffic engineering is a highly effective approach to making data centers green based on different traffic patterns and network architectures  . Several solutions have been proposed for achieving energy proportionality by applying traffic aggregation and virtual machine assignment techniques. In BIB001 , the authors demonstrated that the minimization of network power consumption for any general data center topology is NP-complete. They proposed a centralized network power controller program that gathered traffic data and server statistics from all switches and servers and used this information to perform traffic aggregation and virtual machine assignment and migration in the target data center. They demonstrated the optimal placement for the virtual machines and the bandwidth savings through trace evaluation. In BIB002 , the authors developed REsPoNse, a framework based on identifying and using energy-critical paths to redirect traffic to allow large portions of the network to enter a low-power state. They demonstrated that energy savings were achieved without frequent recomputation and deployment of new routing tables, and they showed that REsPoNse had marginal impact on the application-level throughput and latency. Their results yielded energy savings of approximately 30% and 42%. Jiang et al. BIB003 considered a joint design incorporating virtual machine assignment and routing for data centers. They considered the optimization of both energy cost and network congestion, then proposed an offline algorithm based on a Markov chain model and an incremental online solution for a dynamic environment. The authors reported performance analysis results for various topologies, including clique, fat-tree, HyperX and BCube topologies, under a spectrum of elephant and mice workloads. Given that the bandwidth demands of different flows do not peak at exactly the same time, the authors BIB004 proposed a correlation-aware power optimization scheme that dynamically consolidated traffic flows onto a small set of switches and links and then shut down unused network devices. They designed a heuristic algorithm to determine the consolidation and rate configuration, which was fundamentally based on a bin-packing algorithm. They reported an energy savings of 46% in network energy for a data center network with only a negligible increase in delay. To exploit the well-structured topology of data centers, Zhang et al. BIB005 adopted a hierarchical perspective to study the task of optimizing power usage in data center networks while guaranteeing connectivity and maximum link utilization. They divided their discussion into two levels: 1) the core level, for the determination of the core switches that must be powered to carry outgoing traffic and the aggregation switches to serve the out-pod traffic in each pod, and 2) the pod level, for the determination of the aggregation switches that must be active to carry intra-pod traffic. They designed a hierarchical energy optimization algorithm and evaluated it on various traffic patterns, including large traffic flow only, small traffic flow only, and random traffic patterns. In BIB006 , the authors adopted the routing perspective to consider reducing the power consumption of high-density data center networks while satisfying network performance requirements. They proposed a routing algorithm that used as little network power as possible to provide routing services, and they reported a 39% energy savings for a link load of 20% and a reliability requirement of 2 (i.e., the threshold for the ratio of the network throughput that the data center operator could tolerate after switch and link elimination to the basic throughput achievable using all switches was 2). In BIB007 , the authors proposed a new framework to embrace the new opportunities offered by combining certain unique features of data centers with traffic engineering. They solved the problem in two steps: first, by allocating virtual machines to servers to reduce the amount of traffic and to generate favorable conditions for traffic engineering, and then, by reducing the number of active switches and balancing traffic flows to reduce energy consumption. The experimental results indicated an energy savings of up to 50%. In addition to considering the network level, the authors of BIB008 considered a joint host-network energy optimization scheme involving the simultaneous optimization of virtual machine assignment and network flow routing. They modeled the joint optimization problem as an integer linear program. Because this problem is NP-complete, they proposed a series of techniques to address the associated challenges: 1) they converted the problem to one that considered a single optimization solution, 2) they proposed a parallelization approach for rapid completion, and 3) they quickly identified efficient routing paths for the flows. The authors reported that their optimization method reduced energy consumption by 40% for a system load of 30%. By utilizing correlation analysis in the joint power optimization of a data center network and servers, the authors of BIB009 proposed PowerNetS to achieve improved energy savings. Their approach featured a well-designed heuristic algorithm to incrementally perform virtual machine and traffic consolidation with lower virtual machine migration overheads, thereby optimizing power consumption while guaranteeing the fulfillment of network constraint. They reported that their method could achieve 51.6% energy savings for a data center. In Table VI , we summarize and compare the proposals mentioned above. The features presented for comparison are 1) the traffic patterns, 2) the network architectures, 3) the techniques used in the proposals, 4) the considered QoS specifications, and 5) the results of the proposals.