Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.

Data Center Energy Consumption Modeling: A Survey

As computation continues to move into the cloud, the computing platform of interest no longer resembles a pizza box or a refrigerator, but a warehouse full of computers These new large datacenters are quite different from traditional hosting facilities of earlier times and cannot be viewed simply as a collection of co-located servers Large portions of the hardware and software resources in these facilities must work in concert to efficiently deliver good levels of Internet service performance, something that can only be achieved by a holistic approach to their design and deployment In other words, we must treat the datacenter itself as one massive warehouse-scale computer (WSC) We describe the architecture of WSCs, the main factors influencing their design, operation, and cost structure, and the characteristics of their software base We hope it will be useful to architects and programmers of today’s WSCs, as well as those of future many-core platforms which may one day implement the equivale

The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Second Edition

Interest has been growing in powering datacenters (at least partially) with renewable or "green" sources of energy, such as solar or wind. However, it is challenging to use these sources because, unlike the "brown" (carbon-intensive) energy drawn from the electrical grid, they are not always available. This means that energy demand and supply must be matched, if we are to take full advantage of the green energy to minimize brown energy consumption. In this paper, we investigate how to manage a datacenter's computational workload to match the green energy supply. In particular, we consider data-processing frameworks, in which many background computations can be delayed by a bounded amount of time. We propose GreenHadoop, a MapReduce framework for a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup). GreenHadoop predicts the amount of solar energy that will be available in the near future, and schedules the MapReduce jobs to maximize the green energy consumption within the jobs' time bounds. If brown energy must be used to avoid time bound violations, GreenHadoop selects times when brown energy is cheap, while also managing the cost of peak brown power consumption. Our experimental results demonstrate that GreenHadoop can significantly increase green energy consumption and decrease electricity cost, compared to Hadoop.

/pdf/greenhadoop-leveraging-green-energy-in-data-processing-2g8hnftgpt.pdf

GreenHadoop: leveraging green energy in data-processing frameworks

This paper presents an overview of the state of the art control strategies specifically designed to coordinate distributed energy storage (ES) systems in microgrids. Power networks are undergoing a transition from the traditional model of centralised generation towards a smart decentralised network of renewable sources and ES systems, organised into autonomous microgrids. ES systems can provide a range of services, particularly when distributed throughout the power network. The introduction of distributed ES represents a fundamental change for power networks, increasing the network control problem dimensionality and adding long time-scale dynamics associated with the storage systems’ state of charge levels. Managing microgrids with many small distributed ES systems requires new scalable control strategies that are robust to power network and communication network disturbances. This paper reviews the range of services distributed ES systems can provide, and the control challenges they introduce. The focus of this paper is a presentation of the latest decentralised, centralised and distributed multi-agent control strategies designed to coordinate distributed microgrid ES systems. Finally, multi-agent control with agents satisfying Wooldridge’s definition of intelligence is proposed as a promising direction for future research.

https://orbit.dtu.dk/files/190879836/C_Users_Thomas_Documents_UNSW_PhD_Research_Paper_Review_Control_for_Microgrids_with_Distributed_Energy_Storage_Systems_Draft_Final_160612_Review_MA_MG_ES.dvi.pdf

Control Strategies for Microgrids With Distributed Energy Storage Systems: An Overview

Several companies have recently announced plans to build "green" datacenters, i.e. datacenters partially or completely powered by renewable energy. These datacenters will either generate their own renewable energy or draw it directly from an existing nearby plant. Besides reducing carbon footprints, renewable energy can potentially reduce energy costs, reduce peak power costs, or both. However, certain renewable fuels are intermittent, which requires approaches for tackling the energy supply variability. One approach is to use batteries and/or the electrical grid as a backup for the renewable energy. It may also be possible to adapt the workload to match the renewable energy supply. For highest benefits, green datacenter operators must intelligently manage their workloads and the sources of energy at their disposal.In this paper, we first discuss the tradeoffs involved in building green datacenters today and in the future. Second, we present Parasol, a prototype green datacenter that we have built as a research platform. Parasol comprises a small container, a set of solar panels, a battery bank, and a grid-tie. Third, we describe GreenSwitch, our model-based approach for dynamically scheduling the workload and selecting the source of energy to use. Our real experiments with Parasol, GreenSwitch, and MapReduce workloads demonstrate that intelligent workload and energy source management can produce significant cost reductions. Our results also isolate the cost implications of peak power management, storing energy on the grid, and the ability to delay the MapReduce jobs. Finally, our results demonstrate that careful workload and energy source management can minimize the negative impact of electrical grid outages.

/pdf/parasol-and-greenswitch-managing-datacenters-powered-by-2ivpdktjry.pdf

Parasol and GreenSwitch: managing datacenters powered by renewable energy

Power over-subscription can reduce costs for modern data centers. However, designing the power infrastructure for a lower operating power point than the aggregated peak power of all servers requires dynamic techniques to avoid high peak power costs and, even worse, tripping circuit breakers. This work presents an architecture for distributed per-server UPSs that stores energy during low activity periods and uses this energy during power spikes. This work leverages the distributed nature of the UPS batteries and develops policies that prolong the duration of their usage. The specific approach shaves 19.4% of the peak power for modern servers, at no cost in performance, allowing the installation of 24% more servers within the same power budget. More servers amortize infrastructure costs better and, hence, reduce total cost of ownership per server by 6.3%.

/pdf/managing-distributed-ups-energy-for-effective-power-capping-4a7vttob3f.pdf

Managing distributed ups energy for effective power capping in data centers

As brown energy costs grow, renewable energy becomes more widely used. Previous work focused on using immediately available green energy to supplement the non-renewable, or brown energy at the cost of canceling and rescheduling jobs whenever the green energy availability is too low [16]. In this paper we design an adaptive data center job scheduler which utilizes short term prediction of solar and wind energy production. This enables us to scale the number of jobs to the expected energy availability, thus reducing the number of cancelled jobs by 4x and improving green energy usage efficiency by 3x over just utilizing the immediately available green energy.

Utilizing green energy prediction to schedule mixed batch and service jobs in data centers

As brown energy costs grow, renewable energy becomes more widely used Previous work focused on using immediately available green energy to supplement the non-renewable, or brown energy at the cost of canceling and rescheduling jobs whenever the green energy availability is too low [16] In this paper we design an adaptive data center job scheduler which utilizes short term prediction of solar and wind energy production This enables us to scale the number of jobs to the expected energy availability, thus reducing the number of cancelled jobs by 4x and improving green energy usage efficiency by 3x over just utilizing the immediately available green energy

Object localization is a key primitive in pervasive computing environments, where numerous applications depend on the rapid and accurate position estimation of objects We present a general RFID-based localization framework that reliably determines the positions of objects with unprecedented accuracy and speed This is achieved by varying the power levels of the RFID readers, calibrated against reference tags of known sensitivity Our implementation and experiments are able to localize objects to an accuracy of 15 cm within a few seconds, and this compares favorably with previous techniques We also suggest several practical optimizations for further enhancing the speed and accuracy of the method

/pdf/object-localization-using-rfid-4q7glyw199.pdf

Object localization using RFID

Location-awareness of mobile objects is the key to numerous emerging ubiquitous computing applications. We show that RFID technology can be leveraged to achieve mobile object localization in an inexpensive, power efficient, scalable, widely applicable, flexible, and user-friendly manner. We outline the challenges that can adversely affect RFID-based localization techniques, and propose solutions to mitigate them. We present several algorithms for RFID-based mobile object localization that compare favorably or exceed previous methods in terms of accuracy, speed, reliability, scalability, and cost.

/pdf/efficient-rfid-based-mobile-object-localization-593bap49eq.pdf

Liuyi Zhang

Papers

Managing distributed ups energy for effective power capping in data centers

Utilizing green energy prediction to schedule mixed batch and service jobs in data centers

Utilizing green energy prediction to schedule mixed batch and service jobs in data centers

Object localization using RFID

Efficient RFID-based mobile object localization