On energy consumption of switch-centric data center networks

Data center network (DCN) is the core of cloud computing and accounts for 40% energy spend when compared to cooling system, power distribution and conversion of the whole data center (DC) facility. It is essential to reduce the energy consumption of DCN to esnure energy-efficient (green) data center can be achieved. An analysis of DC performance and efficiency emphasizing the effect of bandwidth provisioning and throughput on energy proportionality of two most common switch-centric DCN topologies: three-tier (3T) and fat tree (FT) based on the amount of actual energy that is turned into computing power are presented. Energy consumption of switch-centric DCNs by realistic simulations is analyzed using GreenCloud simulator. Power related metrics were derived and adapted for the information technology equipment (ITE) processes within the DCN. These metrics are acknowledged as subset of the major metrics of power usage effectiveness (PUE) and data center infrastructure efficiency (DCIE), known to DCs. This study suggests that despite in overall FT consumes more energy, it spends less energy for transmission of a single bit of information, outperforming 3T

Power-Aware Datacenter Networking and Optimization

Present-day datacenter networks (DCNs) are designed to achieve full bisection bandwidth in order to provide high network throughput and server agility. However, the average utilization of typical DCN infrastructure is below 10% for significant time intervals. As a result, energy is wasted during these periods. In this thesis we analyze traffic behavior of datacenter networks using traces as well as simulated models. Based on the insight developed, we present techniques to reduce energy waste by making energy use scale linearly with load. The solutions developed are analyzed via simulations, formal analysis, and prototyping. The impact of our work is significant because the energy savings we obtain for networking infrastructure of DCNs are near optimal. A key finding of our traffic analysis is that network switch ports within the DCN are grossly under-utilized. Therefore, the first solution we study is to modify the routing within the network to force most traffic to the smallest of switches. This increases the hop count for the traffic but enables the powering off of many switch ports. The exact extent of energy savings is derived and validated using simulations. An alternative strategy we explore in this context is to replace about half the switches with fewer switches that have higher port density. This has the effect of enabling even greater traffic consolidation, thus enabling even more ports to sleep. Finally, we explore a third approach in which we begin with end-to-end traffic models and incrementally build a DCN topology that is optimized for that model. In other words, the network topology is optimized for the potential use of the datacenter. This approach makes sense because, as other researchers have observed, the traffic in a datacenter is heavily dependent on the primary use of the datacenter. A second line of research we undertake is to merge traffic in the analog domain prior to feeding it to switches. This is accomplished by use of a passive device we call a merge network. Using a merge network enables us to attain linear scaling of energy use with load regardless of datacenter traffic models. The challenge in using such a device is that layer 2 and layer 3 protocols require a one-to-one mapping of hardware addresses to IP (Internet Protocol) addresses. We overcome this problem by building a software shim layer that hides the fact that traffic is being merged. In order to validate the idea of a merge network, we build a simple mere network for gigabit optical interfaces and demonstrate correct operation at line speeds of layer 2 and layer 3 protocols. We also conducted measurements to study how traffic gets mixed in the merge network prior to being fed to the switch. We also show that the merge network uses only a fraction of a watt of power, which makes this a very attractive solution for energy efficiency. In this research we have developed solutions that enable linear scaling of energy with load in datacenter networks. The different techniques developed have been analyzed via modeling and simulations as well as prototyping. We believe that these solutions can be easily incorporated into future DCNs with little effort

Energy-Efficient Flow Scheduling and Routing with Hard Deadlines in Data Center Networks

The power consumption of enormous network devices in data centers has emerged as a big concern to data center operators. Despite many traffic-engineering-based solutions, very little attention has been paid on performance-guaranteed energy saving schemes. In this paper, we propose a novel energy-saving model for data center networks by scheduling and routing "deadline-constrained flows" where the transmission of every flow has to be accomplished before a rigorous deadline, being the most critical requirement in production data center networks. Based on speed scaling and power-down energy saving strategies for network devices, we aim to explore the most energy efficient way of scheduling and routing flows on the network, as well as determining the transmission speed for every flow. We consider two general versions of the problem. For the version of only flow scheduling where routes of flows are pre-given, we show that it can be solved polynomially and we develop an optimal combinatorial algorithm for it. For the version of joint flow scheduling and routing, we prove that it is strongly NP-hard and cannot have a Fully Polynomial-Time Approximation Scheme (FPTAS) unless P=NP. Based on a relaxation and randomized rounding technique, we provide an efficient approximation algorithm which can guarantee a provable performance ratio with respect to a polynomial of the total number of flows.Comment: 11 pages, accepted by ICDCS'1

Cross-Layer Design for Energy Efficiency on Data Center Network

Energy efficient infrastructures or green IT (Information Technology) has recently become a hot button issue for most corporations as they strive to eliminate every inefficiency from their enterprise IT systems and save capital and operational costs. Vendors of IT equipment now compete on the power efficiency of their devices, and as a result, many of the new equipment models are indeed more energy efficient. Various studies have estimated the annual electricity consumed by networking devices in the U.S. in the range of 6 - 20 Terra Watt hours. Our research has the potential to make promising solutions solve those overuses of electricity. An energy-efficient data center network architecture which can lower the energy consumption is highly desirable. First of all, we propose a fair bandwidth allocation algorithm which adopts the max-min fairness principle to decrease power consumption on packet switch fabric interconnects. Specifically, we include power aware computing factor as high power dissipation in switches which is fast turning into a key problem, owing to increasing line speeds and decreasing chip sizes. This efficient algorithm could not only reduce the convergence iterations but also lower processing power utilization on switch fabric interconnects. Secondly, we study the deployment strategy of multicast switches in hybrid mode in energy-aware data center network: a case of famous Fat-tree topology. The objective is to find the best location to deploy multicast switch not only to achieve optimal bandwidth utilization but also minimize power consumption. We show that it is possible to easily achieve nearly 50% of energy consumption after applying our proposed algorithm. Finally, although there exists a number of energy optimization solutions for DCNs, they consider only either the hosts or network, but not both. We propose a joint optimization scheme that simultaneously optimizes virtual machine (VM) placement and network flow routing to maximize energy savings. The simulation results fully demonstrate that our design outperforms existing host- or network-only optimization solutions, and well approximates the ideal but NP-complete linear program. To sum up, this study could be crucial for guiding future eco-friendly data center network that deploy our algorithm on four major layers (with reference to OSI seven layers) which are physical, data link, network and application layer to benefit power consumption in green data center

Achieving Energy Efficiency on Networking Systems with Optimization Algorithms and Compressed Data Structures

To cope with the increasing quantity, capacity and energy consumption of transmission and routing equipment in the Internet, energy efficiency of communication networks has attracted more and more attention from researchers around the world. In this dissertation, we proposed three methodologies to achieve energy efficiency on networking devices: the NP-complete problems and heuristics, the compressed data structures, and the combination of the first two methods. We first consider the problem of achieving energy efficiency in Data Center Networks (DCN). We generalize the energy efficiency networking problem in data centers as optimal flow assignment problems, which is NP-complete, and then propose a heuristic called CARPO, a correlation-aware power optimization algorithm, that dynamically consolidate traffic flows onto a small set of links and switches in a DCN and then shut down unused network devices for power savings. We then achieve energy efficiency on Internet routers by using the compressive data structure. A novel data structure called the Probabilistic Bloom Filter (PBF), which extends the classical bloom filter into the probabilistic direction, so that it can effectively identify heavy hitters with a small memory foot print to reduce energy consumption of network measurement. To achieve energy efficiency on Wireless Sensor Networks (WSN), we developed one data collection protocol called EDAL, which stands for Energy-efficient Delay-aware Lifetime-balancing data collection. Based on the Open Vehicle Routing problem, EDAL exploits the topology requirements of Compressive Sensing (CS), then implement CS to save more energy on sensor nodes

Host and Network Optimizations for Performance Enhancement and Energy Efficiency in Data Center Networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows

Structural issues and energy efficiency in data centers

Mención Internacional en el título de doctorWith the rise of cloud computing, data centers have been called to play a main role in the Internet scenario nowadays. Despite this relevance, they are probably far from their zenith yet due to the ever increasing demand of contents to be stored in and distributed by the cloud, the need of computing power or the larger and larger amounts of data being analyzed by top companies such as Google, Microsoft or Amazon. However, everything is not always a bed of roses. Having a data center entails two major issues: they are terribly expensive to build, and they consume huge amounts of power being, therefore, terribly expensive to maintain. For this reason, cutting down the cost of building and increasing the energy efficiency (and hence reducing the carbon footprint) of data centers has been one of the hottest research topics during the last years. In this thesis we propose different techniques that can have an impact in both the building and the maintenance costs of data centers of any size, from small scale to large flagship data centers. The first part of the thesis is devoted to structural issues. We start by analyzing the bisection (band)width of a topology, of product graphs in particular, a useful parameter to compare and choose among different data center topologies. In that same part we describe the problem of deploying the servers in a data center as a Multidimensional Arrangement Problem (MAP) and propose a heuristic to reduce the deployment and wiring costs. We target energy efficiency in data centers in the second part of the thesis. We first propose a method to reduce the energy consumption in the data center network: rate adaptation. Rate adaptation is based on the idea of energy proportionality and aims to consume power on network devices proportionally to the load on their links. Our analysis proves that just using rate adaptation we may achieve average energy savings in the order of a 30-40% and up to a 60% depending on the network topology. We continue by characterizing the power requirements of a data center server given that, in order to properly increase the energy efficiency of a data center, we first need to understand how energy is being consumed. We present an exhaustive empirical characterization of the power requirements of multiple components of data center servers, namely, the CPU, the disks, and the network card. To do so, we devise different experiments to stress these components, taking into account the multiple available frequencies as well as the fact that we are working with multicore servers. In these experiments, we measure their energy consumption and identify their optimal operational points. Our study proves that the curve that defines the minimal power consumption of the CPU, as a function of the load in Active Cycles Per Second (ACPS), is neither concave nor purely convex. Moreover, it definitively has a superlinear dependence on the load. We also validate the accuracy of the model derived from our characterization by running different Hadoop applications in diverse scenarios obtaining an error below 4:1% on average. The last topic we study is the Virtual Machine Assignment problem (VMA), i.e., optimizing how virtual machines (VMs) are assigned to physical machines (PMs) in data centers. Our optimization target is to minimize the power consumed by all the PMs when considering that power consumption depends superlinearly on the load. We study four different VMA problems, depending on whether the number of PMs and their capacity are bounded or not. We study their complexity and perform an offline and online analysis of these problems. The online analysis is complemented with simulations that show that the online algorithms we propose consume substantially less power than other state of the art assignment algorithms.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Joerg Widmer.- Secretario: José Manuel Moya Fernández.- Vocal: Shmuel Zak