Data Center Power Research Articles

Security and Privacy of Smart Cities: A Survey, Research Issues and Challenges

With recent advances of information and communication technology, smart city has been emerged as a new paradigm to dynamically optimize the resources in cities and provide better facilities and quality of life for the citizens. Smart cities involve a variety of components, including ubiquitous sensing devices, heterogeneous networks, large-scale databases, and powerful data centers to collect, transfer, store, and intelligently process real-time information. Smart cities can offer new applications and services for augmenting the daily life of citizens on making decisions, energy consumption, transportation, health-care, and education. Despite the potential vision of smart cities, security and privacy issues remain to be carefully addressed. This paper delineates a comprehensive survey of security and privacy issues of smart cities, and presents a basis for categorizing the present and future developments within this area. It also presents a thematic taxonomy of security and privacy issues of smart cities to highlight the security requirements for designing a secure smart city, identify the existing security and privacy solutions, and present open research issues and challenges of security and privacy in smart cities.

Data Center Demand Response With On-Site Renewable Generation: A Bargaining Approach

The rapid growth of cloud computing and data centers with skyrocketing energy consumption, together with the accelerating penetration of renewable energy sources, is creating both severe challenges and tremendous opportunities. Data centers offering large flexible loads in the grid, opens up a unique opportunity to smooth out the significant fluctuation and uncertainty of renewable generation and hence enable seamless integration. To take the market power of data centers into consideration, this paper proposes a bargaining solution to the market program for data center demand response when the load serving entity (LSE) has power supply deficiency. Specifically, due to the uncertainty of load flexibility of data centers incurred by the intermittent on-site renewable generation and dynamic service requests, there exists information asymmetry between the LSE and the data center, which complicates the design of the bargaining solution. Making use of the log-concavity of the (expected) utility functions, a computationally efficient method to implement the best response updates in the bargaining procedure is presented. Furthermore, it is shown analytically that the bid sequences of the LSE and the data center are guaranteed to converge and the final price clinched by the bargaining algorithm is indeed the Nash bargaining solution, which is proportionally fair. In addition, the proposed bargaining solution is compared with two other schemes, namely the Stackelberg game and the social welfare maximization schemes. Finally, extensive numerical experiments are conducted to validate the theoretical guarantees of the bargaining and to examine the impact of various model parameters. Empirical comparison indicates the fairness advantage of the bargaining approach over the other two schemes, especially when the load of the data center is not very flexible, highlighting the importance of information feedback embodied by the bargaining procedure.

Exploring Customizable Heterogeneous Power Distribution and Management for Datacenter

Large-scale datacenters are facing increasing pressure of capping their carbon emission and power cost. Many leading-edge studies have started to explore server clusters running on multiple power sources. Existing approaches do not sufficiently consider the fine-grained power delivery to satisfy diverse requirements in datacenter, especially in the multi-tenant/colocation datacenter, which may yield low energy utilization. To address the emerging trend and new requirements, this article proposes a novel Datacenter inner Power Switch Network ( DiPSN ) to improve datacenter power efficiency and user satisfaction. DiPSN is a reconfigurable and easy-to-scale-out power architecture, which enables datacenter to distribute various power sources in a fine-grained manner. Moreover, a tailored machine learning based power source management framework is proposed for DiPSN to dynamically optimize user customized performance metrics and maximize datacenter revenue. Compared with conventional single-switch power distribution system, our DiPSN can be configured to improve solar energy utilization by 39.6 percent, reduce utility power cost by 11.1 percent and improve workload performance by 33.8 percent. Meanwhile, our design can extend battery lifetime by 9.3 percent. This work could provide valuable guidelines for designing heterogeneous power distribution architecture and management methodology in datacenters for improving user-customizable efficiency, sustainability and economy.

Reinforcement learning based methodology for energy-efficient resource allocation in cloud data centers

Energy-efficient Cloud Infrastructure Resource Allocation Framework is getting popularity as it is paying effective attention to cloud data management with a view to achieve maximize revenue and minimize cost. This infrastructure can encourage for both cloud providers and users for allocating cloud infrastructure resources for fulfilling not only good energy efficiency measured in Power Usage Effectiveness (PUE) and data center Infrastructure Efficiency (DCiE) but also high CPU utilization. Therefore, in this paper we proposed a framework which can show effective performance for achieving the high data center energy efficiency and preventing Service Level Agreement (SLA) violation respectively with the aim of green cloud resources deployment. The framework accomplishes cloud infrastructure resource allocation on the basic of Reinforcement Learning mechanism and Fuzzy Logic for green solutions. The evaluation for Energy-efficient Resource Allocation is experimented on the traces of the PlanetLab virtualized environment for gaining good PUE and CPU utilization.

The Datacenter as a Computer: Designing Warehouse-Scale Machines, Third Edition

This book describes warehouse-scale computers (WSCs), the computing platforms that power cloud computing and all the great web services we use every day. It discusses how these new systems treat the datacenter itself as one massive computer designed at warehouse scale, with hardware and software working in concert to deliver good levels of internet service performance. The book details the architecture of WSCs and covers the main factors influencing their design, operation, and cost structure, and the characteristics of their software base. Each chapter contains multiple real-world examples, including detailed case studies and previously unpublished details of the infrastructure used to power Google's online services. Targeted at the architects and programmers of today's WSCs, this book provides a great foundation for those looking to innovate in this fascinating and important area, but the material will also be broadly interesting to those who just want to understand the infrastructure powering the internet. The third edition reflects four years of advancements since the previous edition and nearly doubles the number of pictures and figures. New topics range from additional workloads like video streaming, machine learning, and public cloud to specialized silicon accelerators, storage and network building blocks, and a revised discussion of data center power and cooling, and uptime. Further discussions of emerging trends and opportunities ensure that this revised edition will remain an essential resource for educators and professionals working on the next generation of WSCs.

PUE=1.0x（補機動力nearlyゼロ）のデータセンター用冷却システムの開発 汎用機器を用いた冷却システムの運転状況

PUE=1.0x（補機動力nearlyゼロ）のデータセンター用冷却システムの開発 汎用機器を用いた冷却システムの運転状況

Energy savings and usability of zero-client computing in office settings

ABSTRACTThis study provides a detailed comparison of the power consumption, usability, and applicability of virtual machines (VMs)—accessed through zero-client devices—and traditional laptop computers in office settings. The study analyzed high-level plug loads across two office spaces, one using traditional laptops, the other using zero clients. In addition, the individual power consumption of four workstations was monitored and compared. Each workstation user switched between using a laptop or zero client for one week and then using the alternate system the second week. Results of the high-level and workstation analysis show that average workstation plug loads are lower for occupants using zero clients. However, this does not include power consumed by the data center managing VMs. This study calculates the affiliated data center power draw of VMs and shows that server-related loads push total zero-client computing energy higher than that of traditional laptops. Finally, a questionnaire was administered to building occupants to determine the appropriateness of zero-client computing for various user groups, with the results indicating that VMs are most appropriate for more basic software functions. The findings of this study suggest that advances in server technology can help improve both the overall efficiency and the usability of zero-client computing.

Data center holistic demand response algorithm to smooth microgrid tie-line power fluctuation

With the rapid development of cloud computing, artificial intelligence technologies and big data applications, data centers have become widely deployed. High density IT equipment in data centers consumes a lot of electrical power, and makes data center a hungry monster of energy consumption. To solve this problem, renewable energy is increasingly integrated into data center power provisioning systems. Compared to the traditional power supply methods, renewable energy has its unique characteristics, such as intermittency and randomness. When renewable energy supplies power to the data center industrial park, this kind of power supply not only has negative effects on the normal operation of precision equipment, such as CPU/GPU chips and hard disk, in data center, but it would also impact the stability of the utility power grids operation. To solve this problem, this paper presents a novel tie-line power fluctuation smoothing algorithm with consideration of data center’s holistic demand response. The contributions of this paper are: (1) overcoming the limitations of treating IT load as uncontrollable workload in the traditional demand response research, we design a data center resource scheduling model to realize IT load demand response controllability; (2) two novel mechanisms are proposed: (i) the server cluster workload scheduling method with time shift mechanism, and (ii) the data center UPS (Uninterruptible Power Supply) energy storage dynamic response mechanism. (3) Combining these two mechanisms as holistic demand response of data center, we present a tie-line power fluctuation smoothing algorithm to improve power supply reliability, which is beneficial to both the high density and precision IT equipment in the data center and the utility power grid. In the experiments, the results show that the new algorithm can effectively regulate the tie-line power fluctuations under different server cluster utilization ranges and scenarios of large-scale penetration of distributed renewable energy scenarios. The new algorithm is hence able to contribute beneficially to the reliability and stability of intelligent industrial park micro-grid and utility power grids.

Reducing the Cooling Power of Data Centers by Intelligently Assigning Tasks

The explosive growth of Internet of Things is generating massive data which are normally stored in data centers. The power consumption has become a very important challenge of designing modern data centers due to the explosive growth of data. The power consumed by cooling system accounts for about half of the total power consumption. Reducing the peak inlet temperature of racks residing in data centers can effectively decrease the temperature requirement of supplied cold air, thus cutting down the cooling cost. Task distribution in data centers has a significant impact on this inlet temperature. Many investigations have been conducted on achieving an optimal task distribution in terms of the air organization [e.g., genetic algorithm (GA)]. However, the existing methods can be easily trapped into a local optimum. This paper constructs a power model to correlate the task assignment, heat recirculation, inlet temperature, and cooling cost in the homogeneous and heterogeneous data centers with under-floor air supply. Furthermore, genetic simulated annealing algorithm is proposed and designed to enhance the traditional GA and assign tasks in the data centers according to the corresponding air organization by integrating the advantages of simulated annealing, thus minimizing the inlet temperature and reducing the cooling cost. Experimental results indicate that the proposed approach can effectively decrease the temperature requirement of supplied cold air and reduce the power consumption of the cooling system in contrast to the traditional GA and ant colony algorithm, especially when the data centers are with medium utilization.

A Taxonomy of Software-Defined Networking (SDN)-Enabled Cloud Computing

Software-Defined Networking (SDN) opened up new opportunities in networking with its concept of the segregated control plane from the data-forwarding hardware, which enables the network to be programmable, adjustable, and reconfigurable dynamically. These characteristics can bring numerous benefits to cloud computing, where dynamic changes and reconfiguration are necessary with its on-demand usage pattern. Although researchers have studied utilizing SDN in cloud computing, gaps still exist and need to be explored further. In this article, we propose a taxonomy to depict different aspects of SDN-enabled cloud computing and explain each element in details. The detailed survey of studies utilizing SDN for cloud computing is presented with focus on data center power optimization, traffic engineering, network virtualization, and security. We also present various simulation and empirical evaluation methods that have been developed for SDN-enabled clouds. Finally, we analyze the gap in current research and propose future directions.

Design of fuel cell powered data centers for sufficient reliability and availability

It is challenging to design a sufficiently reliable fuel cell electrical system for use in data centers, which require 99.9999% uptime. Such a system could lower emissions and increase data center efficiency, but the reliability and availability of such a system must be analyzed and understood. Currently, extensive backup equipment is used to ensure electricity availability. The proposed design alternative uses multiple fuel cell systems each supporting a small number of servers to eliminate backup power equipment provided the fuel cell design has sufficient reliability and availability. Potential system designs are explored for the entire data center and for individual fuel cells. Reliability block diagram analysis of the fuel cell systems was accomplished to understand the reliability of the systems without repair or redundant technologies. From this analysis, it was apparent that redundant components would be necessary. A program was written in MATLAB to show that the desired system reliability could be achieved by a combination of parallel components, regardless of the number of additional components needed. Having shown that the desired reliability was achievable through some combination of components, a dynamic programming analysis was undertaken to assess the ideal allocation of parallel components.

A NOVEL APPROACH OF JOB ALLOCATION USING MULTIPLE PARAMETERS IN IN CLOUD ENVIRONMENT

Cloud computing is Internet ("cloud") based development and use of computer technology ("computing"). It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. This research deals with the balancing of work load in cloud environment. Load balancing is one of the essential factors to enhance the working performance of the cloud service provider. Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. We propose an improved load balancing algorithm for job scheduling in the Grid environment. Hence, in this research work, a multi-objective load balancing algorithm has been proposed to avoid deadlocks and to provide proper utilization of all the virtual machines (VMs) while processing the requests received from the users by VM classification. The capacity of virtual machine is computed based on multiple parameters like MIPS, RAM and bandwidth. Heterogeneous virtual machines of different MIPS and processing power in multiple data centers with different hosts have been created in cloud simulator. The VM’s are divided into 2 clusters using K-Means clustering mechanism in terms of processor MIPS, memory and bandwidth. The cloudlets are divided into two categories like High QOS and Low QOS based on the instruction size. The cloudlet whose task size is greater than the threshold value will enter into High QOS and cloudlet whose task size is lesser than the threshold value will enter into Low QOS. Submit the job of the user to the datacenter broker. The job of the user is submitted to the broker and it will first find the suitable VM according to the requirements of the cloudlet and will match VM depending upon its availability. Multiple parameters have been evaluated like waiting time, turnaround time, execution time and processing cost. This modified algorithm has an edge over the original approach in which each cloudlet build their own individual result set and it is later on built into a complete solution.

Model-based Approach to Data Center Design and Power Usage Effectiveness Assessment

Data Centers (DC) are complex systems, which provide an environment to host Information and Technologies (IT) equipment. DCs are typically composed of a variety of components including servers, storage systems, networking infrastructures in addition to non-IT equipment such as power distribution and cooling systems. In order for a DC to function properly, all of its components need to be correctly configured and integrated. However, the variety of configurations and the interdependencies of DC sub-systems create challenges in understanding and optimizing DC complexities. In this paper, we present a system modeling approach, which supports the design of DCs and a quantitative assessment of their energy efficiency. This modeling approach aims at controlling the design complexity of DC infrastructure and ensuring the consistency of such designs. The cornerstone of our approach is a generic DC metamodel (DCMM), which captures the DCs heterogeneous structure, main characteristics, and diverse constraints. Furthermore, we propose a method to generate a Power Flow Model (PFM) from an input DC model. The PFM is then used to compute the DC Power Usage Effectiveness (PUE) metric. We evaluate the applicability of our approach through a use case of the Consortium Laval-UQAM-McGill and Eastern Quebec (CLUMEQ) data center.

Consolidate IoT Edge Computing with Lightweight Virtualization

Lightweight virtualization (LV) technologies have refashioned the world of software development by introducing flexibility and new ways of managing and distributing software. Edge computing complements today's powerful centralized data centers with a large number of distributed nodes that provide virtualization close to the data source and end users. This emerging paradigm offers ubiquitous processing capabilities on a wide range of heterogeneous hardware characterized by different processing power and energy availability. The scope of this article is to present an in-depth analysis on the requirements of edge computing from the perspective of three selected use cases that are particularly interesting for harnessing the power of the Internet of Things. We discuss and compare the applicability of two LV technologies, containers and unikernels, as platforms for enabling the scalability, security, and manageability required by such pervasive applications that soon may be part of our everyday lives. To inspire further research, we identify open problems and highlight future directions to serve as a road map for both industry and academia.

Efficiency and Reliability Analyses of AC and 380 V DC Distribution in Data Centers

Modern data centers consume large amounts of electricity, resulting in high operational costs. The efficiency of a data center power distribution system can be increased and the operational cost reduced if the number of power conversion stages can be minimized and more efficient converters, such as wide bandgap converters, are used. This can be achieved by using dc distribution voltage at the rack level by eliminating extra conversion stages. In this paper, benchmarks for both ac and 380-V dc data centers were developed and efficiency analyses were performed for an entire year. The impact of integrating photovoltaic (PV) systems into the data centers has also been analyzed in both cases in terms of efficiency. The results show that 380-V dc data centers are more efficient than ac data centers with and without PV integration. Furthermore, the reliability of an ac system was compared with a 380-V dc architecture with Tier-IV standard. Monte-Carlo simulations were used to perform reliability analyses for different levels of redundancy in the uninterruptible power supply system for both cases. The simulation results showed that the 380-V dc distribution system had a higher level of reliability than the ac distribution system in data centers.

A Neighbor-Based Probabilistic Broadcast Protocol for Data Dissemination in Mobile IoT Networks

The recent trend of implementing Internet of Things (IoT) applications is to transmit sensing data to a powerful data center and try to discover the valuable knowledge behind “Big Data” by various intelligent but resource-consuming algorithms. However, from the discussion with some industrial companies, it is understood that disseminating real-time sensing data to their nearby network-edge applications directly would produce a more economical design and lower service latency for some important smart city applications. Therefore, this paper proposes an efficient broadcast protocol to disseminate data in mobile IoT networks. The proposed protocol exploits the neighbor knowledge of mobile nodes to determine a rebroadcast delay that prioritizes different packet broadcasts according to their profits. An adaptive connectivity factor is also introduced to make the proposed protocol adaptive to the node density of different network parts. By combining the neighbor knowledge of nodes and adaptive connectivity factor, a reasonable probability is calculated to determine whether a packet should be rebroadcasted to other nodes or be discarded to prevent redundant packet broadcast. Extensive simulation results have validated that this protocol can improve the success ratio of packet delivery by 13% ~ 28% with a similar end-to-end transmission delay and network overhead of the most state-of-art approaches.

A Stochastic Optimal Control Approach for Exploring Tradeoffs between Cost Savings and Battery Aging in Datacenter Demand Response

This brief paper optimizes power management for datacenters employing lithium-ion battery storage, with the specific goal of addressing the tradeoff between: 1) the cost saving achievable through the peak demand shaving and 2) the corresponding battery aging. To the best of the authors’ knowledge, this tradeoff has never been addressed using physics-based models of battery performance and degradation combined with stochastic models of datacenter demand. We build: 1) a Markov chain model of datacenter power demand; 2) a second-order model of battery diffusion/reaction dynamics; and 3) a physics-based model of battery aging via solid electrolyte interphase growth. Together, these models enable the solution of the battery health-conscious demand response problem via stochastic dynamic programming (SDP). A penalty function is used for enforcing a datacenter “power cap” within this SDP problem. By varying this power cap, we traverse the Pareto tradeoff between the cost savings due to demand response and battery health degradation.

Increasing Hyperscale Data Center Efficiency: A Better Way to Manage 54-V\/48-V-to-Point-of-Load Direct Conversion

The hyperspeed growth of data center power demand starts with the high power consumption required by the current and upcoming generations of digital application-specific integrated circuits (ASICs)-central processing unit (CPU), double data rate memories (DDR) and networking ASICs-in addition to the exploding use of high-power graphics processing units (GPUs) for artificial intelligence and machine-learning functions. Hyperscale data centers will also increase in number, from 259 at the end of 2015 to 485 by 2020. They will represent 47% of all installed data center servers by 2020 [1]. Server shipment growth has been driven by large, hyperscale data centers, where servers are often configured for maximum productivity and operate at high utilization rates. Server power consumption is perpetually escalating, and this trend will continue into the near future for both midrange and high-end power servers [2], as shown in Figure 1.

High-Efficiency High-Power-Density LLC Converter With an Integrated Planar Matrix Transformer for High-Output Current Applications

Isolated high-output current DC/DC converters are critical for future data center power architecture. LLC converters with matrix transformer are suitable for these applications due to its high efficiency and high power density. Different matrix transformer structures are investigated in this paper. To improve the current design practice, a high-frequency transformer loss model is developed and a detailed design methodology is proposed. Moreover, a novel matrix transformer structure is proposed to integrate four elemental transformers into one magnetic core with simple four-layer print circuit board windings implementation and further reduced core loss. By pushing switching frequency up to megahertz with GaN devices, the proposed matrix transformer can achieve high efficiency, high power density, and automatic manufacturing for magnetic components. A 1-MHz 380 V/12 V 800-W LLC converter with GaN devices is demonstrated. The prototype achieves a peak efficiency of 97.6% and a power density of 900 W/in3.

Future Energy Efficient Data Centers With Disaggregated Servers

The popularity of the Internet and the demand for 24/7 services uptime is driving system performance and reliability requirements to levels that today's data centers can no longer support. This paper examines the traditional monolithic conventional server (CS) design and compares it to a new design paradigm: the disaggregated server (DS) data center design. The DS design arranges data centers resources in physical pools, such as processing, memory, and IO module pools, rather than packing each subset of such resources into a single server box. In this paper, we study energy efficient resource provisioning and virtual machine (VM) allocation in DS-based data centers compared to CS-based data centers. First, we present our new design for the photonic DS-based data center architecture, supplemented with a complete description of the architectural components. Second, we develop a mixed integer linear programming (MILP) model to optimize VM allocation for the DS-based data center, including the data center communication fabric power consumption. Our results indicate that, in DS data centers, the optimum allocation of pooled resources and their communication power yields up to 42% average savings in total power consumption when compared with the CS approach. Due to the MILP high computational complexity, we developed an energy efficient resource provisioning heuristic for DS with communication fabric (EERP-DSCF), based on the MILP model insights, with comparable power efficiency to the MILP model. With EERP-DSCF, we can extend the number of served VMs, where the MILP model scalability for a large number of VMs is challenging. Furthermore, we assess the energy efficiency of the DS design under stringent conditions by increasing the CPU to memory traffic and by including high noncommunication power consumption to determine the conditions at which the DS and CS designs become comparable in power consumption. Finally, we present a complete analysis of the communication patterns in our new DS design and some recommendations for design and implementation challenges.