Green Data Centers Research Articles

Lowering The Bit‐Energy of Electro‐Optic Modulators Via Polarization‐Phase Modulation in Thin‐Film BaTiO3 Ferroelectric Crystal Waveguide

AbstractThe electro‐optic (EO) modulator is a crucial device for achieving high‐speed optical communication and optical interconnection by loading the high‐frequency electrical signals onto the optical carrier. To date, modern communication technique demands for a low energy consumption for green data center establishment. In this work, a thin‐film BaTiO3 crystal waveguide Mach–Zehnder interferometer (MZI) type EO intensity modulator is experimentally implemented and its dynamic‐polarization interference process involving a phase‐polarization modulation (PPM) scheme other than conventional optical phase modulation (OPM) is carefully analyzed. Its findings show that the PPM scheme can efficiently reduce the half‐wave voltage () and the equivalent half‐wave voltage () of the modulator. Then, a new theory of deriving the bit energy (BE) from is put forward, which has proven that the BE value under PPM is lower than the OPM counterpart. The EO modulator prototype featuring a length of 1.4 mm, = 3.0 V and BE = 1.2 pJ/bit match well with the numerical calculations, thus validating the feasibility to achieve a BE<0.2 pJ/bit for an EO modulator through optimization of the electrode gap and state‐of‐the‐art fabrication techniques in the near future.

Energy-Efficient Computing and Green Computing Techniques

Energy-efficient computing and strategies for environmentally sustainable computing have become indispensable in the contemporary world, given the escalating demand for computing power and the growing emphasis on ecological conservation. This summary delves into diverse facets of energy efficient computing and green computing methodologies, elucidating their significance, hurdles, and potential remedies. The primary objective of energy-efficient computing is to curtail the energy consumption of computing systems while either maintaining or enhancing their performance. This proves pivotal in alleviating the ecological ramifications of computing, especially as data centers and similar facilities consume substantial amounts of energy. Green computing techniques encompass a broad spectrum of practices geared towards diminishing the environmental footprint of computing. These encompass the utilization of renewable energy sources, the crafting of energy-efficient hardware, the optimization of software algorithms, and the cultivation of energy consciousness among users. Despite the advantages associated with energy-efficient computing and green computing methodologies, various challenges necessitate attention. These encompass the delicate balance between energy efficiency and performance, the intricacies involved in designing energy-efficient systems, and the absence of standardized metrics for gauging and comparing energy efficiency. Exploration in this domain propels technological progress, resulting in the creation of more effective hardware, software, and systems. These advancements not only positively impact the environment but also propel the overall evolution of computing technology. The Information and Communication Technology (ICT) sector substantially adds to worldwide carbon emissions. Energy-efficient computing and environmentally conscious computing approaches can play a crucial role in mitigating this impact, decreasing energy usage, and advocating for the adoption of renewable energy sources. The aim of this research is to investigate the complexities associated with multiple attribute decision-making when confronted with intuitionist fuzzy information. In this context, the weights of attributes are not entirely known, and the attribute values are expressed using intuitionist fuzzy numbers. To ascertain the attribute weights, an optimization model is formulated based on the foundational principles of traditional grey relational analysis (GRA). The proposed approach entails computing the grey relation degree between each alternative and the positive-ideal solution as well as the negative-ideal solution. This degree is subsequently utilized to establish a relative relational degree, facilitating the simultaneous ranking of all alternatives concerning both the positive-ideal solution (PIS) and negative-ideal solution (NIS). From the result Efficient Algorithms is ranked at first position and Green Data Centers is ranked at fifth position.

Analysis of Barriers to Green Data Centers Implementation in Malaysia, using Interpretive Structural Modelling (ISM)

The data center market is expected to grow at 7% during 2022- 2027 and the market size is to reach RM2.0 billion in 2027. The high impact of ICT contributes to high energy consumption, which will impact the environment and greenhouse gas emissions. To reduce energy consumption is to introduce a green data centre. Implementing a green data center involves the introduction of energy-efficient measures for the data center. This will include using energy-efficient ICT equipment and energy-efficient facilities equipment, especially the HVAC system. The successful introduction of the green data center involves new technologies in which there will be barriers that need to be addressed. Based on the study, green awareness and the benefit of switching to green data centers is essential for green data center initiatives in Malaysia.

GEECO: Green Data Centers for Energy Optimization and Carbon Footprint Reduction

Cloud computing has revolutionized data storage, processing, and access in modern data center operations. Conventional data centers use enormous amounts of energy for server operation, power supply, and cooling. The processors produce heat while processing the data and therefore increase the center’s carbon footprint, and the rising energy usage and carbon emissions caused by data centers pose serious environmental challenges. Under these circumstances, energy-efficient green data centers are being used as a phenomenal source of sustainable modernization. This study proposes the implementation of the Green Energy Efficiency and Carbon Optimization (GEECO) model for enhancing energy usage. Within the data center, the GEECO model dynamically adjusts workload distribution and task assignment to balance performance and manage service-level reconciliation. The ability to identify possibilities for energy optimization and carbon emission reduction is possible through real-time monitoring of energy usage and workload demand. The results revealed a considerable increase in energy efficiency, with significant decreases in energy usage and related costs. The GEECO model provides a significant improvement in energy consumption and carbon emission reduction for the different introduced scenarios. This model’s introduction to practical application would be made possible by these improvements in the quantitative results. The approach of this study also has a positive impact on the environment by reducing carbon emissions. The resilience and practicality of the solution are also analyzed, highlighting the probability of widespread adoption and its associated improvements in the advancement of sustainable cloud computing.

Combination optimization of green energy supply in data center based on simulated annealing particle swarm optimization algorithm

At present, the high energy consumption of data centers based on grid power supply not only brings huge direct cost of electricity, but also indirectly produces a lot of greenhouse gases, which affects the natural environment. Academia and industry are beginning to introduce clean renewable energy sources such as wind and solar power into data centers to reduce operating costs and environmental damage by building new green data centers. To solve this problem, this study considers the use of waste heat for refrigeration while taking natural gas power generation into account, and introduces wind energy as a green energy source. On the premise of considering the response level of data centers, the two resources are combined and deployed to improve resource utilization and reduce energy consumption costs. Aiming at the instability of wind power generation, a particle swarm energy scheduling optimization algorithm based on simulated annealing algorithm was proposed by combining simulated annealing algorithm and particle swarm optimization algorithm. The research shows that, considering the response level of data centers, the use of natural gas and wind energy as the main energy supply can effectively reduce the overall energy consumption of data centers.

Energy-efficient virtual machine placement in distributed cloud using NSGA-III algorithm

Cloud computing is the most widely adapted computing model to process scientific workloads in remote servers accessed through the internet. In the IaaS cloud, the virtual machine (VM) is the execution unit that processes the user workloads. Virtualization enables the execution of multiple virtual machines (VMs) on a single physical machine (PM). Virtual machine placement (VMP) strategically assigns VMs to suitable physical devices within a data center. From the cloud provider's perspective, the virtual machine must be placed optimally to reduce resource wastage to aid economic revenue and develop green data centres. Cloud providers need an efficient methodology to minimize resource wastage, power consumption, and network transmission delay. This paper uses NSGA-III, a multi-objective evolutionary algorithm, to simultaneously reduce the mentioned objectives to obtain a non-dominated solution. The performance metrics (Overall Nondominated Vector Generation and Spacing) of the proposed NSGA-III algorithm is compared with other multi-objective algorithms, namely VEGA, MOGA, SPEA, and NSGA-II. It is observed that the proposed algorithm performs 7% better that the existing algorithm in terms of ONVG and 12% better results in terms of spacing. ANOVA and DMRT statistical tests are used to cross-validate the results.

Национальное стратегическое планирование цифровой экономики: конкурентный анализ на материале Китая и России

At present, the wave of digital economy is sweeping the world. Countries all over the world are competing for the strategic commanding height in digital economy. In order to firmly seize this major historical opportunity and to transform and upgrade its economic structure faster, Russia keeps on strengthening the core capabilities of the digital economy such as information infrastructure, new generation information technology, ICT industry, human resources, information security, and digital environment supervision. Research purpose: a competitive analysis of the national strategic planning for the digital economies in China and Russia. Research objectives: 1) to provide definition for the digital economy; 2) to show the current improvement of the digital economy; 3) to study Russia’s digital economy; 4) to explore China’s digital economy. Research methodology: study of theoretical literature, data collection and analysis. Research results. “Strategy of the Information Society Development in the Russian Federation for 2017–2030” (2017) implies creating conditions for developing large companies in the information and communications technology industry in order to protect national interests in the digital economy. In 2021, Russia’s investment in the development of the digital economy reached the total of 4.094 trillion rubles. It represents an increase of 7.8 % compared to 2020 and amounts to 3.7 % of GDP. Over the past decade, China’s digital economy has made world-renowned development achievements. The overall scale has been the second best in the world for many years. The construction of information and communication networks is a global leader in terms of scope. The "Broadband China" strategy has been diligently implemented. The country has built the world’s largest fiber optic and mobile broadband network. The length of optical cable lines increased 2.7 times from 14.79 million km in 2012 to 54.81 million km in 2021. By June 2022, the total number of data center racks in China exceeded 5.9 million standard racks, and 153 national green data centers were built. Key core technologies have experienced breakthroughs. Investment in research and development of digital technologies is increasing every year. The level of digitalization in the service sector has increased significantly. The digital transformation of agriculture is making steady progress. In China, the deep implementation of the development strategy focused on innovation, promoting key technology research, accelerating the forging of long boards, making up for short boards, and building an independent and controlled industrial ecology.

Simulation-based Multi-unit Collaborative Optimization in Data Centers

With the rapid development of information technology in China, the problem of energy conservation in data centers has become increasingly prominent, and the green data center model is inevitable. The refrigeration equipment which is the main power consumption in the data center is affected by the environment, room temperature, unit output, and other factors. This paper provides a scheme of multi-unit collaborative optimization cooling. It is mainly divided into four stages: first, a model of the data center is built through Modelica, and it is exported as a fmu file. Second, the PSO optimization strategy is used to generate the data sets which contain unit output and cooling effect at different times. Third, LSTM and TCN load forecasting models are trained. Finally, through a comparison of various optimization algorithms, the PSO-LSTM-TCN model is used to simulate the unit output and optimize the output parameters of multiple units.

Intelligent privacy-preserving demand response for green data centers

Intelligent privacy-preserving demand response for green data centers

Enabling Practical Decision Making For Sustainable Green Data Center Planning

Data centers play a crucial role in storing and processing data in today's digital age, leading to a surge in demand for sustainable green data center planning. However, implementing practical measures to achieve sustainability remains a challenge for data center managers. This study aims to aid their informed decision-making in sustainable green data center planning. Previous research has identified seven green data center key components: ICT governance, infrastructure, energy, equipment lifecycle, green technology, benchmarking, and business continuity. Subsequently, the study expanded by utilizing the FAHP method to evaluate the perspectives of various experienced data center. Those green data center components were evaluated against each other regarding the three sustainability criteria: environment, economy, and corporate. Consequently, it was discovered that infrastructure, green technology, and business continuity consistently held the highest fuzzy weight in multiple sensitivity analysis scenarios. Thus, data center managers can allocate resources based on priority rankings and adjust accordingly.

Massively scalable Kerr comb-driven silicon photonic link

The growth of computing needs for artificial intelligence and machine learning is critically challenging data communications in today’s data-centre systems. Data movement, dominated by energy costs and limited ‘chip-escape’ bandwidth densities, is perhaps the singular factor determining the scalability of future systems. Using light to send information between compute nodes in such systems can dramatically increase the available bandwidth while simultaneously decreasing energy consumption. Through wavelength-division multiplexing with chip-based microresonator Kerr frequency combs, independent information channels can be encoded onto many distinct colours of light in the same optical fibre for massively parallel data transmission with low energy. Although previous high-bandwidth demonstrations have relied on benchtop equipment for filtering and modulating Kerr comb wavelength channels, data-centre interconnects require a compact on-chip form factor for these operations. Here we demonstrate a massively scalable chip-based silicon photonic data link using a Kerr comb source enabled by a new link architecture and experimentally show aggregate single-fibre data transmission of 512 Gb s−1 across 32 independent wavelength channels. The demonstrated architecture is fundamentally scalable to hundreds of wavelength channels, enabling massively parallel terabit-scale optical interconnects for future green hyperscale data centres.

Investigation of bidirectional converter utilizing battery energy storage for an isolated load system

To explore the design of a bidirectional isolated converter for usage with battery energy storage systems, the study aims to analyses this investigation. The change resulted in a reduced workload, which is an obvious advantage. We instead think that there will be no effect on the load-carrying capacity due to the increase in production. This research provides an alternating-direct-current renewable energy supply system. Solar, wind, power storage, and a load are included. This is an in-depth discussion of a power conversion converter's control system. Using an energy storage device in addition to the controller is feasible (ESSs). This research resulted in adaptive PI control, which is known for lowering link voltage and faster responses. Using this control system, you can cut down on both transient and static issues and also have a better shot at minimizing defects. MATLAB/Simulink is an application which does simulations. To evaluate the efficacy of an envisioned system, one simulates the systems and compares the results. The results show that the hybrid power supply model proposed may have a substantial effect on the energy supply to the isolated load and can be utilized to build a unique plan for a green data center.

A Multiformalism-Based Model for Performance Evaluation of Green Data Centres

Although the coexistence of ARM and INTEL technologies in green data centres is technically feasible, significant challenges exist that must be addressed. These challenges stem from the differences in instruction sets and power consumption between the two processor architectures. While ARM processors are known for their energy efficiency, INTEL processors tend to consume more power. Consequently, evaluating the performance of hybrid architectures can be a complex task. The contributions of this article consist of (i) a multiformalism-based model of a data centre, providing a natural and convenient approach to the specification process and performance analysis of a realistic scenario and (ii) a review of the performance indices, including the choice of one architecture over another, power consumption, the response time, and request loss, according to different policies. As a result, the model aims to address issues such as system underutilization and the need to estimate the optimal workload balance, thereby providing an effective solution for evaluating the performance of hybrid hardware architectures.

LOAD BALANCING IN DATA CENTRE OF CLOUD COMPUTING

The internet is expanding its viewpoint into each conceivable part of the cutting-edge economy. Unshackled from our web programs programs today, the internet is characterizing our way of life, regardless of whether it’s sitting in front of the TV or driving an independent car. Cloud computing which is an on violently expanded our computerized lives, through, there have been critical improvement as far as accessibility fluc-tution, time and quality in administration are concerned, the unbounded development of our computerized way of life requires monstrous measures of power especially for the data centres that fill in as the mind of the advanced economy. All things considered, the move by clients toward cloud, will exceeding any energy productivity increase, which has record for wer 70% os data centre development in 2018. Many research advancements are already made in this domain for minimizing the energy utilization of the computing types of gear included; for effificient power energy consumption, decrease of carbon impression and e-squander. These procedures are supporters of green cloud computing, which are focused on planning and advancing energy-profificient activities to contain inordinate energy utilization in data centers. Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most signifificant technology in IT sector. From the notion of cloud computing to its energy effificiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy effificiency in containers and problem-solving approaches used for reducing power consumption in data centers. The cloud computing revolution is redesigning modern networking, and offering promising environmentalprotection prospects as well as economic and technological advantages. These technologies have the potential to improve energy effificiency and to reduce carbon footprints and (e- )waste. These features can transform cloud computing into green cloud computing. In this survey, we review the main achievements of green cloud computing. First,an overview of cloud computing is given.Then, recent studies and developments are summarized, and environmental issues are specififically addressed. Finally, future research directions and open problems regarding green cloud computing are presented. This survey is intended to serve asup-to-date guidance for research with respect to green cloud computing. Key Words: Server, Memory, Data center, Load balancing, Workload categorization, Energy effificiency ,Green information

Can data center green reform facilitate urban green technology innovation? Evidence from China

With the expansion of the digital information industry, the size of global data centers is exploding. As part of a high-energy-consuming industry, data centers can support sustainable urban development through green transformation. This study uses a quasi-natural experiment of China's national green data center pilot policy implemented in 2015 to examine the impact of data center green transformation on green technology innovation in cities. Using a difference-in-difference (DID) analysis, this study finds that the national green data center pilot policy leads to higher levels of green technology innovation in cities, but the spillover effect of the policy on neighboring cities is insignificant. The mechanism test showed that the national green data center pilot policy could facilitate urban green technological innovation through capital deepening, market competition, and industrial agglomeration. In addition, the policy impact is more pronounced for cities with poorer environments, less civilization, high levels of network infrastructure, and more innovative endowments. The findings can provide new guidance for cleaner regional production from the standpoint of data center green development.

A case study of stand-alone hybrid power systems for a data center using HOMER and DIgSILENT

The number of mega data centers is steadily expanding as the cloud business expands. The data center consumes heavy electrical power because of their structural and equipment characteristics (e.g., workstations and cooling equipment). The mega data center becomes a microgrid (e.g., islanding operation). So, a lot of researchers have proposed the architecture design of a green energy data center to solve the heavy energy consumption problem and to reduce the emission release. For example, the goal of a green energy data center is to maximize energy efficiency while reducing environmental impacts (e.g., the reduction of greenhouse gases). So, the goal of this study is to design the most effective power system for a stand-alone hybrid green data center at the aspects of energy savings and the reliable operation. The ideal architecture of a stand-alone data center powered by renewable energy is initially designed by the HOMER program. Then, the steady-state and transient responses of a data center that functions as an island microgrid using DIgSILENT© Power Factory are examined.

Research on the Construction of Green and Low-Carbon Water Conservancy Data Center

In order to create a green and efficient data center and promote the technical level of green and low-carbon construction of data centers. This paper clarifies the necessity of building a green and low-carbon data center, and proposes construction methods suitable for data centers, which is in terms of architecture, electricity, power supply and distribution, air conditioning, and equipment configuration and layout.

Analysis on the Construction of Data Center of Large-scale Water Conservancy Project

This paper expounds the necessity of building data centers for large-scale water conservancy projects, which consists of data center site selection, transformation, modular cabinets, intelligent power supply, lithium battery packs and data center intelligent management platforms. It adopts new technologies and processes , new equipment, create an efficient and green data center, and provide solid computing power and data support for digital twin water conservancy projects.

A novel approach for new architecture for green data centre

The massive energy usage of data centers may be traced in part to the growing number of data centers and workstations because of economies of scale for cloud computing. It does, though, indicate wasteful power usage. Consequently, researching ways to improve the energy efficiency of datacenter equipment is now a crucial aspect in reducing datacenter power consumption. In this study, we describe methodologies and algorithms for flexible, energy-efficient, and effective load balancing in data centers, resulting in lower energy consumption by systems. Because of difficulties related with energy consumption, such as capital expenditure, operational costs, and environmental effect, renewable energy is becoming an increasing major consideration in data centers. With the rise in environmental consequences around the world, data centers must consider energy efficiency as one of the most critical factors. Cloud computing techniques have made a huge impact all over the world. Green DC is a concept that has been tested. We suggested an algorithm in this paper based on certain current algorithm limitations that will help to reduce environmental effect. We investigated the efficiency of our program further and used a load balancing method to improve its performance.

Analysis Report on Green Cloud Computing: A Report

Abstract: Cloud Computing is getting popular day by day as it reduces the cost of business, provides bettered services and security. In simple terms, pall computing is the delivery of services via the internet, including waiters, storehouse, databases, and much further. It gives the stoner power to pierce anything and anywhere through the internet and you generally have to pay only for the pall services you use. Green Computing is a recent trend towards operating computer systems to be energy effective and to reduce carbon vestiges andE-waste. pall computing is reshaping ultramodern networking with a capacity of environmental protection prospects as well. In This paper will talk about the attainments of green pall computing and green data centers and why we need them. also, recent studies with some being work are briefed and environmental issues are explicitly addressed.