Harmony: Dynamic Heterogeneity-Aware Resource Provisioning in the Cloud

In this contemporary world, technology is advancing at a rapid pace. In the recent decades, information technology has grown exponentially. Improving data center energy efficiency is an urgent issue with remarkable economic and environmental impacts. Enormous heat generation is fuelled by the data centers. Distribution of heat is a crucial parameter that affects data center cooling and energy consumption. Heat reduction is the crux of environmentally sustainable computing. Typically, data centres lack in effective sensing systems to monitor heat distribution at a large scale. In this chapter, I aim at using sensor networks as a dense instrumentation technology to comprehend and control cooling in data centers. This chapter presents the Aquasar project as the case study and explores the environmental challenges in developing sensor networks in data centers. I also explore the effective way of dealing with the excess heat from multiple servers in the data centers and the upper hand of water cooling over air cooling.

Energy savings in direct air-side free cooling data centers: A cross-system modeling and optimization framework

Cloud Computing has emerged as a computing paradigm where services are provided through the internet in recent years. Offering on-demand services has transformed the IT companies' working environment, leading to a linearly increasing trend of its usage. The provisioning of the Computing infrastructure is achieved with the help of virtual machines. A great figure of physical devices is required to satisfy the users' resource requirements. To meet the requirements of the submitted workloads that are usually dynamic, the cloud data centers cause the over-provisioning of cloud resources. The result of this over-provisioning is the resource wastage with an increase in the levels of energy consumption, causing a raised operational cost. High CO2 emissions result from this huge energy consumption by data centers, posing a threat to environmental stability. The environmental concern demands for the controlled energy consumption, which can be attained by optimal usage of resources to achieve in the server load, by minimizing the number of active nodes, and by minimizing the frequency of switching between active and de-active server mode in the data center. Motivated by these actualities, we discuss numerous statistical, deterministic, probabilistic, machine learning and optimization based computational solutions for the cloud computing environment. A comparative analysis of the computational methods, on the basis of architecture, consolidation step involved, objectives achieved, simulators involved and resources utilized, has also been presented. A taxonomy for virtual machine (VM) consolidation has also been derived in this research article followed by emerging challenges and research gaps in the field of VM consolidation in cloud computing environment.

Data centers energy efficiency has become an issue given the rising of energy prices and the increase of Web applications, which are hosted, mainly in data centers. In order to understand the opportunities for improving data center energy efficiency, it is necessary to determinate the real energy requirements for one computer application. This article will examine the power chain starting from the facility level and ending at the processor level. In this paper a data center energy model is proffered along with two important indicators that address energy efficiency: the first one is addressing Facility Efficiency and the second one is addressing IT Efficiency. The energy model is taking into account a global overview of the data center (power distribution units, cooling system and the IT system). The aim of the research is to find effective solutions to make data center reduce power consumption while keeping the desired quality of service or service level agreement.

Independent optimization for workload and power management, and active cooling control have been studied extensively to improve data center energy efficiency. Recently, proposals have started to advocate unified workload, power, and cooling management for further energy savings. In this paper, we study this problem with the objectives of both saving energy and capping power. We present the detailed models derived in our previous work from experiments on an blade enclosure system that can be representative of a data center, discuss the optimization opportunities for coordinated power and cooling management, and the challenges for controller design. We then propose a few design principles and examples for unified workload management, power minimization, and power capping. Our simulation-based evaluation shows that the controllers can cap the total power consumption while maintaining the thermal conditions and improve the overall energy efficiency. We argue that the same opportunities, challenges, and designs are also generally applicable to data center level management.

Cooling is a major component in the enormous energy consumption in data centers. Accurate evaluation of cooling inside a data center forms the backbone of all the attempts for improving cooling efficiency. Models based on computational fluid dynamics (CFD) are typically used for accurate evaluation, but have a drawback of high computation time. This paper presents a novel thermal predictor to evaluate data center cooling in seconds. The key idea is to extract information from a single instance of CFD simulation using metrics called as influence indices to build the fast thermal predictor. Then, this predictor can evaluate the cooling for altered operation of data center with comparable accuracy in seconds without the need for repetitive CFD simulations. This paper demonstrates the accuracy of the thermal predictor by comparing with CFD simulations for a sample, but realistic data center. The fast thermal predictor then successfully passed more challenging tests in a real production data center and proved its practical utility. The results of the thermal predictor compared with measurements carried out in the production data center are also presented. This fast thermal predictor is an important milestone in the development of a method for model-based real time control of data center cooling.

Cooling power constitutes a large portion of the total electrical power consumption in data centers. Approximately 25%∼40% of the electricity used within a production data center is consumed by the cooling system. Improving the cooling energy efficiency has attracted a great deal of research attention. Many strategies have been proposed for cutting the data center energy costs. One of the effective strategies for increasing the cooling efficiency is using dynamic thermal management. Another effective strategy is placing cooling devices (heat exchangers) closer to the source of heat. This is the basic design principle of many hybrid cooling systems and liquid cooling systems for data centers. Dynamic thermal management of data centers is a huge challenge, due to the fact that data centers are operated under complex dynamic conditions, even during normal operating conditions. In addition, hybrid cooling systems for data centers introduce additional localized cooling devices, such as in row cooling units and overhead coolers, which significantly increase the complexity of dynamic thermal management. Therefore, it is of paramount importance to characterize the dynamic responses of data centers under variations from different cooling units, such as cooling air flow rate variations. In this study, a detailed computational analysis of an in row cooler based hybrid cooled data center is conducted using a commercially available computational fluid dynamics (CFD) code. A representative CFD model for a raised floor data center with cold aisle-hot aisle arrangement fashion is developed. The hybrid cooling system is designed using perimeter CRAH units and localized in row cooling units. The CRAH unit supplies centralized cooling air to the under floor plenum, and the cooling air enters the cold aisle through perforated tiles. The in row cooling unit is located on the raised floor between the server racks. It supplies the cooling air directly to the cold aisle, and intakes hot air from the back of the racks (hot aisle). Therefore, two different cooling air sources are supplied to the cold aisle, but the ways they are delivered to the cold aisle are different. Several modeling cases are designed to study the transient effects of variations in the flow rates of the two cooling air sources. The server power and the cooling air flow variation combination scenarios are also modeled and studied. The detailed impacts of each modeling case on the rack inlet air temperature and cold aisle air flow distribution are studied. The results presented in this work provide an understanding of the effects of air flow variations on the thermal performance of data centers. The results and corresponding analysis is used for improving the running efficiency of this type of raised floor hybrid data centers using CRAH and IRC units.

With the advent of cloud computing and online services, large enterprises rely heavily on their data centers to serve end users. Among different server components, hard disk drives are known to contribute significantly to server failures. Disk failures as well as their impact on the performance of storage systems and operating costs are becoming an increasingly important concern for data center designers and operators. However, there is very little understanding on the characteristics of disk failures in data centers. Effective disk failure management and data recovery also requires a deep understanding of the nature of disk failures. In this paper, we present a systematic approach to provide a holistic and insightful view of disk failures. We study a large-scale storage system from a production data center. We categorize disk failures based on their distinctive manifestations and properties. Then we characterize the degradation of disk errors to failures by deriving the degradation signatures for each failure category. The influence of disk health attributes on failure degradation is also quantified. We discuss leveraging the derived degradation signatures to forecast disk failures even in their early stages. To the best of our knowledge, this is the first work that shows how to discover the categories of disk failures and characterize their degradation processes on a production data center.

Due to different energy consumption characteristics in different regions, the planning of single regional energy system will lead to limited equipment capacity, low renewable energy penetration and poor regional economy. In response to the above problems, this article proposes a multi-regional joint planning method based on the interconnection of electricity and hydrogen. Firstly, the model of multi-regional electricity-hydrogen interconnected energy system is established. And then the optimal solution of multi-regional energy station collaborative planning is limited in the feasible region through interconnected cable power constraints, hydrogen transportation constraints, interconnected power balance constraints, interconnected power line loss constraints, etc. Finally, taking the functional areas with different energy supply and consumption characteristics as the research object, the effectiveness of the proposed method in improving system economy and promoting renewable energy consumption is verified.

Data centers are growing in size and complexity driven by trends such as cloud computing and on-line services. Such large data centers pose several challenges for system management. Key among them is anomaly detection which is required to monitor and analyze metrics across several thousands servers and across multiple layers of abstractions to detect anomalous system behavior. In practice, multiple anomaly detection tools are used to continuously raise alarms across multiple metrics and servers. These alarms include both true positives and false alarms. Administrators and management tools act on these alarms for diagnosis and deeper root cause analysis and take appropriate management actions to mitigate the anomalous behaviors. Given the scale and scope of the system, the administrators and management tools are overwhelmed with the large number of alarms at any given instant, many of which are false alarms. It is therefore necessary to prioritize and rank these alarms, so as to take timely actions that maintain the service level agreements for the data center. Existing techniques for such ranking are ad-hoc and not scalable. We propose ranking windows of monitored metrics based on their probability of occurrence. We explain how these probabilities can be computed based either on the false positive rates for which the accompanying anomaly detectors were designed, or, when available, on the probability models underlying the false positive rates. In the simplest case, the ranking procedure reduces to computing the Z-score of the observed measurements and computing a statistic from a window of Z-scores to use as a basis for ranking. The proposed techniques are reliable, lightweight and easy to deploy in the modern data center. We have validated these techniques on synthetic data containing injected anomalies and on data acquired from production data centers.

Experimental investigation and seismic response behavior of a self-centering precast concrete frame with hysteretic dampers

Cloud data centers play a significant role in providing services needed by users in a quick way. Recent studies show that, traffic patterns in data centers have a special importance to be improved, since they have significant effects on various aspects such as congestion, overall energy consumption and service response time. The traffic patterns inside a cloud data center have two categories: North-South and East-West. The former one is the outside-inside and inside-outside traffic, Whereas, the latter is the traffic among Virtual Machines (VMs) within data centers. Previous studies have shown that the East-West traffic pattern is multiple times larger than the North-South one. This leads data centers to experience congestion and packet loss in the core layer of their topology. Common cause of large traffic patterns is that, VMs of service chains are scattered within the data center in different racks, so that, it causes lots of packet injection into the data center. In this paper, we propose a heuristic algorithm to place VMs of a service chain in a closer proximity of each other to improve the East-West traffic pattern by reducing response time of services and also data centers' overall energy consumption. The simulation results compared to the state-of-the-art method demonstrate about 18% improvement in response time for users' requests and 10% of total energy consumption reduction in the data center.

Given the high cost of large-scale data centers, an important design goal is to fully utilize available power resources to maximize the computing capacity. In this paper we present Ampere, a novel power management system for data centers to increase the computing capacity by over-provisioning the number of servers. Instead of doing power capping that degrades the performance of running jobs, we use a statistical control approach to implement dynamic power management by indirectly affecting the workload scheduling, which can enormously reduce the risk of power violations. Instead of being a part of the already over-complicated scheduler, Ampere only interacts with the scheduler with two basic APIs. Instead of power control on the rack level, we impose power constraint on the row level, which leads to more room for over provisioning. We have implemented and deployed Ampere in our production data center. Controlled experiments on 400+ servers show that by adding 17% servers, we can increase the throughput of the data center by 15%, leading to significant cost savings while bringing no disturbances to the job performance.

Energy management and reduction is one of the major challenges in the Cloud industry's data centres. The Uptime Institute's 2012 report, the industry's leading authority, highlighted the crisis in data centre energy management. 30% of data centres will run out of power, space and cooling in 2012. On average there's a 90% energy overhead in data centre operations, yet 55% of servers use less than 10% of their capacity. The 500,000 data centres worldwide have a server electricity consumption of 400 Billion kWh/year (approx $40 Billion ) which is growing at 12% CAGR, yet only 11% of the centres monitor their consumption. The global CO2 emissions of these centres, is equivalent to the airline industry, 2% of global emissions. It is expected that by 2020 20% of Europe's electrical energy will be data centre related. Data centre cooling requirements also have an enormous hydro footprint. A 1 MW data centre in a U.S-type climate has an annual hydro footprint of 99 million litres/annum Yet studies have shown that operational energy savings of 40% and substantial reductions in physical space, cooling requirements and water consumption can be achieved, where comprehensive and effective energy monitoring and management systems are employed. These can deliver a ROI within months, and further savings which extend over the centre's lifetime. The technologies which have the potential to deliver these benefits, DCIM (Data Centre Infrastructure Management) systems have only emerged in the last 3 to 4 years and are still in their infancy. Not surprisingly, a new global industry providing innovative, green, sustainable data centre technologies will be worth €45 Billion by 2016. The main barriers to effective monitoring/management is instrumentation and training. Vast volumes of data are gathered and must be competently analysed. Most monitoring systems, involve some form of physical metering and cabling and downtime, a major challenge when 10's thousands of servers are involved. Furthermore, all approaches physical and hybrid approaches limit the level of monitoring visibility to the server, individual processes cannot be monitored. A spin-out company from University College Dublin Ireland, Stratergia, has developed a unique data centre energy management system PAPILLON. It is totally software-based, measuring every server's energy consumption in real-time. It is intuitive to use, operates on any platform and can be installed in hours without any downtime or retrofitting. It uses a client-server type architecture. Software agents on servers communicate periodically, to a master server which has power models for each server type. Using the power models, the master computes in real-time and saves in a data base, all relevant energy information of the centre. In operation, Papillon identifies and quantifies energy saving actions that can be implemented, and coupled with electricity network data, more sustainable energy sources can be introduced into the power input of the centre. Papillon has been trialled at Swedish Compare Testlab and is one of the core tools being installed at state-of-the-art data centres at Kajaani, Finland and Cenit, Spain for a new international EU-funded Masters programme in green, sustainable data centre management.

A model predictive control for a multi-chiller system in data center considering whole system energy conservation