Energy efficiency and low carbon enabler green IT framework for data centers considering green metrics

The increasing demand for storage, networking and computation has driven the escalation of large complex data centers, the massive server farms that run many of today’s Internet, financial, commercial and business applications. A data center can comprise many thousands of servers and can use as much energy as a small city. The massive amounts of computation power required to drive these server systems results in many challenges like energy consumption, emission of green house gases, backups and recovery issues, etc. The rising costs of oil and global warming are some of the biggest challenges of today’s world. The research proposed in this paper discusses how virtualization can be used to improve the performance and energy efficiency of data centers. To prove this work, Green Information Technology (IT) based framework is developed to seamlessly and securely divide data center components into different resource pools depending on different parameters like energy consumption ratio, utilization ratio, workloads, etc. The framework highlights the importance of implementing green metrics like power usage effectiveness (PUE) and data center effectiveness, and carbon emission calculator to measure the efficiency of data center in terms of energy utilization and carbon dioxide (CO2) emissions. The framework is based on virtualization and cloud computing to increase the utilization ratio of already installed servers from 10% to more than 50%. Key words: Green Information Technology (IT), virtualization, server consolidation, energy efficient data centers, energy efficiency.

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

4 Abstract: This paper highlights the importance of identifying and implementing Power Usage Effectiveness metrics for measuring the performance and efficiency of data center to accomplish cost and operational savings. The research highlighted in this paper emphasizes the importance of green data centers to meet business industry requirements and to reduce the effects of global warming. The results clearly indicate that there a strong need for the implementation of green metrics like power usage effectiveness as done in one of the tier level data center in Pakistan. The outcome from paper show that overall performance and efficiency of data center investigated was very poor due to the underutilization of installed equipments like servers. This measurement helps data center managers to implement green IT initiatives and techniques to improv e performance of already installed components.

To determine whether a High-Performance Computing (HPC) data center is energy efficient, various aspects have to be taken into account: the data center's power distribution and cooling infrastructure, the HPC system itself, the influence of the system management software, and the HPC workloads; all can contribute to the overall energy efficiency of the data center. Currently, two well-established metrics are used to determine energy efficiency for HPC data centers and systems: Power Usage Effectiveness (PUE) and FLOPS per Watt (as defined by the Green500 in their ranking list). PUE evaluates the overhead for running a data center and FLOPS per Watt characterizes the energy efficiency of a system running the High-Performance Linpack (HPL) benchmark, i.e. floating point operations per second achieved with 1 watt of electrical power. Unfortunately, under closer examination even the combination of both metrics does not characterize the overall energy efficiency of a HPC data center. First, HPL does not constitute a representative workload for most of today's HPC applications and the rev 0.9 Green500 run rules for power measurements allows for excluding subsystems (e.g. networking, storage, cooling). Second, even a combination of PUE with FLOPS per Watt metric neglects that the total energy efficiency of a system can vary with the characteristics of the data center in which it is operated. This is due to different cooling technologies implemented in HPC systems and the difference in costs incurred by the data center removing the heat using these technologies. To address these issues, this paper introduces the metrics system PUE (sPUE) and Data center Workload Power Efficiency (DWPE). sPUE calculates the overhead for operating a given system in a certain data center. DWPE is then calculated by determining the energy efficiency of a specific workload and dividing it by the sPUE. DWPE can then be used to define the energy efficiency of running a given workload on a specific HPC system in a specific data center and is currently the only fully-integrated metric suitable for rating an HPC data center's energy efficiency. In addition, DWPE allows for predicting the energy efficiency of different HPC systems in existing HPC data centers, thus making it an ideal approach for guiding HPC system procurement. This paper concludes with a demonstration of the application of DWPE using a set of representative HPC workloads.

Energy efficiency (EE), energy consumption cost and environmental impact are vibrant challenges to cloud computing and data centers. Reducing energy consumption and emissions of carbon dioxide (CO2) in data centers represent open areas and driving force for future research work on green data centers. Our Literature review reveals that there are currently several energy efficiency frameworks for data centers which combine a green IT architecture with specific activities and procedures that led to decrease the impact on environment and less CO2 emissions. The current available frameworks have some pros and cons that is the reason why there is an urgent need for an integrated criterion for selecting and adopting energy efficiency framework for data centers. The required energy efficiency framework criteria should also consider the social network applications as a vital related factor in elevating energy consumption, as well as high potential for better energy efficiency in data centers. Additionally, in this paper, we highlighted the importance of the identification of efficient and effective energy efficiency metric that can be used for the measurement and determination of the value of data centers efficiency and their performance combined with sound and empirically validated integrated EE framework.

The research highlighted in this paper is based on the results obtained from a case study performed in different tier level data centres in Pakistan to test the validity, reliability and credibility of proposed green IT framework for implementing green energy efficient data centres. The results clearly indicate that proposed framework is valid, flexible and easy to implement at any tier level data centre. It considers all issues related to green data centres and helps data centre managers to implement proposed framework, to save power consumption and reduce the emission of greenhouse gases to lower the effects of global warming. The framework uses latest energy saving techniques like virtualisation, cloud computing and green metrics to achieve greener data centres.

Issues in Energy Efficiency of Data Centers (DC) are important, due to the cumulative effects of the increase in the DCs number and in the energy consumption per center. Developing new design recommendations to improve a cooling system efficiency, commonly quantified by the PUE metric (Power Usage Effectiveness) is one objective of the Green IT organizations. For existing DCs, without considering the optimization of the IT workload, a possible way to improve the DC's energy efficiency is to adjust the cooling setpoints. In this paper, a methodology based on predictive models is used to optimize the PUE by improving the cooling setting. The modeling approach consists in exploiting the temperatures and energy measurements at various operating conditions to predict the PUE behavior using data-driven models commonly called black box models. The optimization procedures are based on the simulation of these models in order to estimate the best working conditions.

In this paper, we explore a data center's performance with a call for energy efficiency through green computing. Some performance metrics we examine in data centers are server energy usage, Power Usage Effectiveness and utilization rate, i.e., the extent to which data center servers are being used. Recent literature indicates that utilization rates at many internal data centers are quite low, resulting in poor usage of resources such as energy and materials. Based on our study, we attribute these low utilization rates to not fully taking advantage of virtualization, and not retiring phantom (unused) servers. This paper describes our initiative corroborated with real data in a university setting. We suggest that future data centers will need to increase their utilization rates for better energy efficiency, and moving towards a cloud provider would help. However, we argue that neither a pure in-house data center or cloud model is the best solution. Instead we recommend, from a decision support perspective, a hybrid model in data center management to lower costs and increase services, while also providing greater energy efficiency.

:Data canters are the foundation, of contemporary information Technology. The Cloud services and web applications requires data centres having huge storage, network and computation capacity, has driven increase of extensive complex data centres running many of today’s Internet, financial, commercial and business applications. With continuous increase in the demands of clouds services and web application the need for remote storage and computation will certainly grow. Data centres are large group of servers and consume huge quantity of computation power to drive and run these server farms bringing about numerous difficulties like enormous energy consumption, emanation of greenhouse gasses, security , backup and recovery ; This paper study and analyse the methods to achieve energy efficiency in datacenter for sustainable development of ICT and low carbon green IT structures for these large and complex data centres to save consumption of electricity and reduce the emission of greenhouse gases to lower the effects of global warming. The structure utilizes most recent energy saving techniques like virtualization, cloud computing and green metrics to achieve green data canters. It also explores five stage to legitimately actualize green IT structure strategies to accomplish green data centres

Most students know about Information Technology (IT) through the use of their PCs, laptops, IPads, smart phones, and all the social media used with those devices. Thus energy efficient IT (or Green IT), is an area that should grab their interest. Most schools in the U.S. use IT as a tool with their instruction. The student will need to understand the basics on data centers since data centers house the servers that students access when they use Google, Facebook, or any of the many Internet applications used by all users, young and old. This paper provides an overview on how Green IT is an excellent topic for STEM (Science, Technology, Engineering, and Mathematics). An important aspect of the STEM project would be data centers. Data centers are found in nearly every sector of the economy including financial services, media, high-tech, universities, and government institutions. Dramatic server growth at data centers is indicated by well known web services such as Google, Amazon, and eBay. Estimates indicate that Google maintains over 450,000 servers, arranged in racks located in clusters in cities around the world. Google has major data centers in California, Virginia, Georgia, Ireland, and new facilities in Oregon and Belgium. In 2009 Google opened one of its first sites in the upper Midwest in Council Bluffs, Iowa, close to abundant wind power resources for fulfilling green energy objectives and proximate to fiber optic communications links. There are also thousands of servers for Amazon.com and eBay. It is estimated that the Second Life Internet-based virtual world launched in 2003 has over 9,000 servers. Even with these large numbers of current servers, IBM Consulting estimates that in the next decade server shipments will grow by six times and data storage by an amazing 69-fold. Green energy efficient data centers will help us reduce greenhouse gases – which in turn will help reduce global warming. The ongoing UN and White House sessions on climate change emphasize the environmental importance of green projects. Although the extent of the Global Warming danger may continue to be open to debate, implementing Green Data Centers presents a significant opportunity for all of us to help reduce greenhouse gasses. This paper will bring in case studies based on the authors experiences with energy efficient computing and experiences discussing Green IT with STEM students.

ABSTRACT: The escalating environmental footprint of information technology (IT) and software development, particularly within energy-intensive sectors like utility operations, necessitates a paradigm shift towards sustainable practices. This article proposes a comprehensive framework for Green DevOps metrics tailored for the utility industry. It addresses the critical need to quantify and mitigate the environmental impact of software development and operational processes, which are increasingly intertwined with smart grid infrastructure and data centers. The framework integrates adaptations of traditional DevOps performance indicators, such as Deployment Frequency and Lead Time for Changes, with specialized Green IT metrics, including Power Usage Effectiveness (PUE), Carbon Usage Effectiveness (CUE), Water Usage Effectiveness (WUE), and the Software Carbon Intensity (SCI). This paper explores the causal relationships between software development practices, IT infrastructure, and environmental impact, highlighting implementation challenges such as data granularity, legacy system integration, and the need for standardization. Theoretical case studies illustrate the practical application of these metrics in optimizing smart grid software and data center operations for reduced carbon footprint and enhanced resource efficiency. The proposed framework aims to guide utility companies in improving their environmental sustainability, ensuring regulatory compliance, and enhancing operational efficiency through measurable Green DevOps initiatives.

Intensifying computation demand from enterprises has driven the growth of large, multifaceted data centers to manage current Internet, financial, commercial, and business applications. A d ata center comprises thousands of servers and other equipment that require substantial amounts of power to operate. This condition resu lts in numerous challenges for the data center industry, such as massive energy consumption, underutilization of installed equipment, emission of greenhouse gases, and effect on global warming. This paper highlights the significance of identifying metrics to determine the pe rformance and efficiency of a data center, which can help such a facility achieve operational cost savings through proper implementation of performance-measuring metrics. This paper discusses the implementation of Power Usage Effectiveness metrics in a tier-level data center in Pakistan. The results show that the overall performance value of the facility is 3.3, which indicates poor and inefficient operations.

The papers in this special section focus on green and energy efficient cloud computing. Cloud computing has had a huge commercial impact and has attracted the interest of the research community. Public clouds allow their customers to outsource the management of physical resources, and rent a variable amount of resources in accordance to their specific needs. Private clouds allow companies to manage on-premises resources, exploiting the capabilities offered by the cloud technologies, such as using virtualization to improve resource utilization and cloud software for resource management automation. Hybrid clouds, where private infrastructures are integrated and complemented by external resources, are becoming a common scenario as well, for example to manage load peaks. Cloud applications are hosted by data centers whose size ranges from tens to tens of thousands of servers, which raises significant challenges related to energy and cost management. It has been estimated that the Information and Communication Technology (ICT) industry alone is responsible for 2-3 percent of the global greenhouse gas emissions. Therefore, we must find innovative methods and tools to manage the energy efficiency and carbon footprint of data centers, so that they can operate and scale in a cost-effective and environmentally sustainable manner. These methods and tools are often categorized as Data Center Infrastructure Management (DCIM) to monitor, control, and optimize data centers with extensive automation. DCIM must also effectively manage the quality of service provided by the data center, since cloud customers require high reliability, availability, usability, and low response times.

The papers in this special section focus on green and energy efficient cloud computing. Cloud computing has had a huge commercial impact and has attracted the interest of the research community. Public clouds allow their customers to outsource the management of physical resources, and rent a variable amount of resources in accordance to their specific needs. Private clouds allow companies to manage on-premises resources, exploiting the capabilities offered by the cloud technologies, such as using virtualization to improve resource utilization and cloud software for resource management automation. Hybrid clouds, where private infrastructures are integrated and complemented by external resources, are becoming a common scenario as well, for example to manage load peaks. Cloud applications are hosted by data centers whose size ranges from tens to tens of thousands of servers, which raises significant challenges related to energy and cost management. It has been estimated that the Information and Communication Technology (ICT) industry alone is responsible for 2-3 percent of the global greenhouse gas emissions. Therefore, we must find innovative methods and tools to manage the energy efficiency and carbon footprint of data centers, so that they can operate and scale in a cost-effective and environmentally sustainable manner. These methods and tools are often categorized as Data Center Infrastructure Management (DCIM) to monitor, control, and optimize data centers with extensive automation. DCIM must also effectively manage the quality of service provided by the data center, since cloud customers require high reliability, availability, usability, and low response times.

The energy consumption growth of the Information and Communication Technology (ICT) sector contributes to almost 2% of the global carbon footprint with an estimated trend of 3−3.6% by 2020. Most of this growth (45%) can be attributed to data centers (DC) which now represent the core infrastructure for different industries. Furthermore, cloud DCs are complex systems composed of several ICT and non-ICT (i.e. mechanical and electrical) sub-systems. The variety of configurations and the inter-dependencies of the different DC sub-systems leads to enormous challenges in understanding and optimizing DC energy efficiency based on the Power Usage Effectiveness (PUE) metric. Within this context, we focus in this work on analyzing the behavior of Deep Neural Network (DNN)-based model to predict the DC energy efficiency metric (PUE). In fact, the proposed model is used to evaluate the impact of various DC sub-systems on energy efficiency. Through an experimentation with real datasets from a real DC, we observed that DNN-based model achieves a good Root Mean Square Error (RMSE). The obtained results of this experimentation indicate that our proposed DNN-based model improves the PUE optimization, and consequently, shows its promise for a practical implementation.