Energy efficient Green Cloud: Underlying structure

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

Cloud computing is the most prominent computing paradigm in the present era of information technology. However, data centers needed for hosting cloud services demand huge amount of electrical energy and release harmful gases to the atmosphere. To ensure a sustainable future, there is a need to focus on energy efficiency in cloud computing. Early literature pertaining to energy consumption in cloud computing is primarily focused on individual sub-domains like scheduling techniques, optimization, and green computing metrics. Research literature on cloud resource optimization is found to be the most discussed but less structured. This paper intends to provide a complete picture of energy efficiency in cloud computing. It also classifies heuristics-based optimization methods and the dynamic power management techniques. The survey shows the research trends based on regions, journals, conferences, etc., in the domain of energy efficiency in cloud computing. The study concludes with research issues and future research directions.

Cloud computing has been hailed as the achievement of the long-held dream of computing as a utility and has the potential to transform a large part of the Information and Communication Technology (ICT) industry. Cloud computing is both a business and an economic model which has been gaining popularity since 2006 and it is currently the most talked about technology in the ICT industry. Because it views hardware and software as commodities, the cloud is an example of a disruptive technology. It offers enterprises the opportunity to reduce hardware and software cost and the potential reduction of maintenance and support staff. Data centers and cloud computing services providers hope that the widespread adoption of the cloud will bring them more profit and they are actively promoting the technology. The cloud has had its share of controversy; ranging from the definition of cloud computing to its energy efficiency. This paper discusses one area of controversy; the energy efficiency of cloud computing. We outline previous contributions to the discussion of energy efficiency of cloud computing, provide a working definition of cloud computing and discuss its importance, which will grow as the technology matures and becomes well known.

Green Cloud computing is envisioned to achieve not only efficient processing and utilization of computing but also to minimize energy consumption. This is essential for ensuring that the future growth of Cloud computing is sustainable. Otherwise, Cloud computing with increasingly pervasive client devices interacting with data centers will cause an enormous escalation of energy usage. To address this problem, data center resources need to be managed in an energy-efficient manner to drive Green Cloud computing. The management of power consumption in data centers has led to a number of substantial improvements in energy efficiency. Techniques such as ON/OFF mode on server of data centers improve the energy efficiency of Cloud computing. In this chapter, the authors present how to calculate power consumption in Cloud computing and how power consumption in a data center can be reduced when its storage is used in a way that decreases the time needed to access it.

Cloud computing is offering utility-oriented IT services to users world wide. The market for cloud computing services has continued to expand despite a general decline in economic activity in most of the world. It enables hosting of applications from consumer, scientific and business domains. However, data centres hosting cloud computing applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. With energy shortages and global climate change leading our concerns these days, the power consumption of data centres has become a key issue. Therefore, we need green cloud computing solutions that cannot only save energy, but also reduce operational costs. The vision for energy efficient management of cloud computing environments is presented here. Cloud computing – either public, private or hybrid cloud – will become an increasingly important factor in green computing.

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

In the current era of information technology, data is increasing exponentially due to the large number of devices in the network. This leads to the rise of a concept called cloud computing, which becomes the need of time and also results in the rise in popularity of cloud computing. It becomes critical to provide on-demand services that adapt to the needs of the customer. Two important factors that trigger cloud computing systems (CCS) are reliability and energy efficiency. This article provides a comprehensive overview of available strategies for ensuring reliability and energy efficiency in cloud computing, combined approaches, the current status, as well as their trade-offs.

There are two types of approaches for analyzing various aspects related to green-house gas (GHG) emissions, i.e., top-down and bottom-up approaches. Although the top-down approach focuses on macro-economic perspectives, the bottom-up approach is more suitable to investigate GHG emissions at an industry level utilizing domain-specific knowledge. For example, a bottom-up analysis requires a wide variety of data such as energy demands, conversion factors, and energy efficiency, which may be obtained by analyzing industrial process data. This study aims to provide a bottom-up approach for analyzing GHG emissions from shipbuilding processes in Korea. Reference energy system and energy balance for shipbuilding processes are derived for bottom-up modeling. Based on the midterm forecast on energy demands of the Korean shipbuilding industry, it is shown that the business-as-usual GHG emissions may be obtained. Relevant mitigation measures are then investigated to analyze their mitigation potentials for low-carbon ship production. 1. Introduction Global climate change has recently drawn an increasing attention due to its adverse effects on our environment. Since the inception of Kyoto Protocol to the United Nations Frame-work conventions on climate change, local and international experts have long called for more international cooperation in coping with global warming. The main idea of international cooperative efforts is to impose binding obligations for greenhouse gas (GHG) emissions on participating countries. Even though some countries have withdrawn their commitment and others have been reluctant to adopting definite targets for emission reduction, many countries have already established a designated national authority to manage their GHG emissions. Korea has also established a national authority called "GHG Inventory and Research Center (GIR)" in 2010. One of the most important roles of GIR is to manage the national GHG emission levels and set the abatement target of various sectors through an efficient and integrated management of GHG-related information. Recently, GIR has conducted a series of research projects to analyze GHG emissions of industrial sectors in cooperation with a group of experts. This study presents the results from the analysis of GHG emissions and mitigation potentials for the shipbuilding processes in Korea. It should be noted that the scope of this study is limited to constructions processes in a shipyard even though the shipbuilding industry may encompass a broader range of industrial sectors such as steel production and transport. Adopting Model for Energy Supply Strategy Alternatives and their General Environmental Impacts (MESSAGE) developed by International Institute for Applied Systems Analysis in 1980s (Messner 1997), a bottom-up mathematical programming model is generated to derive the business-as-usual (BAU) GHG emissions in the construction processes in a shipyard. Abatement potentials of several technical abatement measures are also analyzed to help shipbuilders effectively cope with the issue of climate change.

Sweet potato (Ipomoea batatas L.) is an important starch-producing crop used worldwide. However, few studies have been conducted on the energy efficient, cost benefit, and greenhouse gas (GHG) emissions of sweet potato production. To address this issue, the data were collected using a questionnaire for face-to-face interviews of 78 sweet potato growers and 74 reference crop (i.e., rice, maize, and potato) growers in Guangdong province. Results revealed that sweet potato production exhibited the highest value of energy efficiency (0.83 kg MJ−1) and economic productivity (0.85 kg CNY−1) among four crops. The GHG emissions from sweet potato production (1165 kg CO2-eq ha−1) were significantly higher than GHG from rice and maize but lower than GHG from potatoes. Moreover, plantation size significantly (p < 0.05) affected inputs of labor, machinery, and diesel fuel and further affected the energy rate, energy efficiency, and GHG emissions of sweet potato production. Sweet potato production in small-size farms (<2.0 ha) exhibited the highest energy efficiency (0.97 kg MJ−1) and the lowest GHG emissions (1045 kg CO2-eq ha−1). Quartering assessments based on energy efficiency, economic productivity, and GHG emissions showed that fertilizers and labor were the major contributors to energy consumption, economic costs, and GHG emissions. Future efforts should be made to reduce fertilizer application and increase fertilizer use efficiency for sustainable sweet potato production.

With the rise of cloud computing, the high-energy consumption of data centers has become a serious concern. The cloud is an energy-intensive technology, with data centers housing hundreds of hosts. Energy usage is quite high 24 hours a day, seven days a week. As a result, there is a need to investigate various ways to minimize cloud energy consumption and to develop an algorithm to reduce cloud energy consumption. This work presents a hybrid method for energy efficiency in cloud computing that uses an artificial bee colony and a gravitational search algorithm. The proposed hybrid algorithm shows better energy efficiency compared to existing methodologies. Our proposed methodology consumes 15% lower energy than ABC algorithm and 27% lower than Gravitational search method in cloud computing. On other hand, number of active host and has been reduced w.r.t no. of Virtual Machine.

Energy-related activities are a major contributor of greenhouse gas (GHG) emissions. A growing body of knowledge clearly depicts the links between human activities and climate change. Over the last century the burning of fossil fuels such as coal and oil and other human activities has released carbon dioxide (CO2) emissions and other heat-trapping GHG emissions into the atmosphere and thus increased the concentration of atmospheric CO2 emissions. The main human activities that emit CO2 emissions are (1) the combustion of fossil fuels to generate electricity, accounting for about 37% of total U.S. CO2 emissions and 31% of total U.S. GHG emissions in 2013, (2) the combustion of fossil fuels such as gasoline and diesel to transport people and goods, accounting for about 31% of total U.S. CO2 emissions and 26% of total U.S. GHG emissions in 2013, and (3) industrial processes such as the production and consumption of minerals and chemicals, accounting for about 15% of total U.S. CO2 emissions and 12% of total ...

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

Data centers are growing consumers of energy and emitters of greenhouse gases (GHG) worldwide. This paper examines data center power and locational workload management as strategies for energy savings and GHG emissions reduction. The case study examined focuses on GHG emissions from the electricity grid supply in a controlled small-scale computer cluster experiment and location (Philadelphia, PA). Virtualization is a technique that consolidates multiple online services onto fewer computing resources within a data center and deploys computing resources only as needed. The method can be applied not only within a data center, but also among multiple data centers in different locations, thereby taking advantage of deploying data centers that are linked to “low-carbon” electricity grids. Understanding the interaction between data center location and real time power consumption is critical to optimizing computer cluster usage, since demand during certain times of the day may rely on coal as the marginal source. Using the power savings results generated from the virtualization experiments performed on a small computer cluster at Drexel University, and power supply from the electricity grid serving the data center over a 24hour day during a peak electricity summer month, we examine the time of day for shifting data center workloads in order to minimize GHG emissions.

Executive Summary: The California Climate Action Registry, which was initially established in 2000 and began operation in Fall 2002, is a voluntary registry for recording annual greenhouse gas (GHG) emissions. The purpose of the Registry is to assist California businesses and organizations in their efforts to inventory and document emissions in order to establish a baseline and to document early actions to increase energy efficiency and decrease GHG emissions. The State of California has committed to use its ''best efforts'' to ensure that entities that establish GHG emissions baselines and register their emissions will receive ''appropriate consideration under any future international, federal, or state regulatory scheme relating to greenhouse gas emissions.'' Reporting of GHG emissions involves documentation of both ''direct'' emissions from sources that are under the entity's control and indirect emissions controlled by others. Electricity generated by an off-site power source is consider ed to be an indirect GHG emission and is required to be included in the entity's report. Registry participants include businesses, non-profit organizations, municipalities, state agencies, and other entities. Participants are required to register the GHG emissions of all operations in California, and are encouraged to report nationwide. For the first three years of participation, the Registry only requires the reporting of carbon dioxide (CO2) emissions, although participants are encouraged to report the remaining five Kyoto Protocol GHGs (CH4, N2O, HFCs, PFCs, and SF6). After three years, reporting of all six Kyoto GHG emissions is required. The enabling legislation for the Registry (SB 527) requires total GHG emissions to be registered and requires reporting of ''industry-specific metrics'' once such metrics have been adopted by the Registry. The Ernest Orlando Lawrence Berkeley National Laboratory (Berkeley Lab) was asked to provide technical assistance to the California Energy Commission (Energy Commission) related to the Registry in three areas: (1) assessing the availability and usefulness of industry-specific metrics, (2) evaluating various methods for establishing baselines for calculating GHG emissions reductions related to specific actions taken by Registry participants, and (3) establishing methods for calculating electricity CO2 emission factors. The third area of research was completed in 2002 and is documented in Estimating Carbon Dioxide Emissions Factors for the California Electric Power Sector (Marnay et al., 2002). This report documents our findings related to the first areas of research. For the first area of research, the overall objective was to evaluate the metrics, such as emissions per economic unit or emissions per unit of production that can be used to report GHG emissions trends for potential Registry participants. This research began with an effort to identify methodologies, benchmarking programs, inventories, protocols, and registries that u se industry-specific metrics to track trends in energy use or GHG emissions in order to determine what types of metrics have already been developed. The next step in developing industry-specific metrics was to assess the availability of data needed to determine metric development priorities. Berkeley Lab also determined the relative importance of different potential Registry participant categories in order to asses s the availability of sectoral or industry-specific metrics and then identified industry-specific metrics in use around the world. While a plethora of metrics was identified, no one metric that adequately tracks trends in GHG emissions while maintaining confidentiality of data was identified. As a result of this review, Berkeley Lab recommends the development of a GHG intensity index as a new metric for reporting and tracking GHG emissions trends.Such an index could provide an industry-specific metric for reporting and tracking GHG emissions trends to accurately reflect year to year changes while protecting proprietary data. This GHG intensity index changes while protecting proprietary data. This GHG intensity index would provide Registry participants with a means for demonstrating improvements in their energy and GHG emissions per unit of production without divulging specific values. For the second research area, Berkeley Lab evaluated various methods used to calculate baselines for documentation of energy consumption or GHG emissions reductions, noting those that use industry-specific metrics. Accounting for actions to reduce GHGs can be done on a project-by-project basis or on an entity basis. Establishing project-related baselines for mitigation efforts has been widely discussed in the context of two of the so-called ''flexible mechanisms'' of the Kyoto Protocol to the United Nations Framework Convention on Climate Change (Kyoto Protocol) Joint Implementation (JI) and the Clean Development Mechanism (CDM).

Taking Stock of Strategies on Climate Change and the Way Forward: A Strategic Climate Change Framework for Australia