Green Data Centers Research Articles

Online delay-guaranteed workload scheduling to minimize power cost in cloud data centers using renewable energy

More and more cloud data centers are turning to leverage on-site renewable energy to reduce power cost for sustainable development. But how to effectively coordinate the intermittent renewable energy with workload remains to be a great challenge. This paper investigates the problem of workload scheduling for power cost minimization under the constraints of different Service Level Agreements (SLAs) of delay tolerant workload and delay sensitive workload for green data centers in a smart grid. Different from the existing studies, we take into consideration of the impact of zero price in the smart grid and the cost of on-site renewable energy. To handle the randomness of workload, electricity price and renewable energy availability, we first formulate the problem as a constrained stochastic problem. Then we propose an efficient online control algorithm named ODGWS (Online Delay-Guaranteed Workload Scheduling) which makes online scheduling decisions achieve a bounded guarantee from the worst scheduling delay for delay tolerant workload. Compared with the existing solutions, our ODGWS decomposes the problem into that of solving a simple optimization problem within each time slot in O(1) time without needing any future information. The rigorous theoretical analysis demonstrates that our algorithm achieves a [O(1V),O(V)] cost-delay tradeoff, where V is a balance parameter between the cost optimality and service quality. Extensive simulations based on real-world traces are done to evaluate the performance of our algorithm. The results show that ODGWS saves about 5% average power cost compared with the baseline algorithms.

Stand-alone renewable power system scheduling for a green data center using integer linear programming

In light of the rapid growth of data centers around the world and their huge energy consumption, several researchers have focused on the task scheduling and resource allocation problem in order to minimize the energy consumed by the data center. Other initiatives focus on the implementation of green energy sources in order to minimize the consumption of fossil fuels and their emission of CO $$_{2}$$ . As part of the ANR DATAZERO project (Pierson et al. in IEEE Access 7, 2019. https://doi.org/10.1109/ACCESS.2019.2930368 ), several research teams have engaged in efforts at defining the main concepts of a full green data center, powered only by renewable energy. Achieving this goal necessitates a focus on the efficient management of an autonomous hybrid power system consisting of solar panels, wind turbines, batteries, and fuel cell systems. The purpose of this work is not to show that a stand-alone data center is economically viable, but rather is feasible. This paper proposes a set of models based on mixed integer linear programs capable of managing the energy commitment to address data center power demand. The approach takes the season and weather forecasts into account at the time of optimization.

Temporal Task Scheduling of Multiple Delay-Constrained Applications in Green Hybrid Cloud

A growing number of global companies select Green Data Centers (GDCs) to manage their delay-constrained applications. The fast growth of users’ tasks dramatically increases the energy consumed by GDCs owned by a company, e.g., Google and Amazon. The random nature of tasks brings a big challenge of scheduling tasks of each application with limited infrastructure resources of GDCs. Therefore, hybrid cloud is widely employed to smartly outsource some tasks to public clouds. However, the temporal variation in many factors including revenue, price of power grid, solar irradiance, wind speed, price of public clouds makes it challenging to schedule all tasks of each application in a cost-effective way while strictly meeting their expected delay constraints. This work proposes a temporal task scheduling algorithm investigating the temporal variation in green hybrid cloud to schedule all tasks within their delay constraints. Besides, it explicitly presents a mathematical equation of service rates and task refusal. The maximization problem is formulated and tackled by the proposed hybrid optimization algorithm called Genetic Simulated-annealing-based particle swarm optimization. Trace-driven experiments demonstrate that larger profit are achieved than several existing scheduling algorithms.

GreenPacker: renewable- and fragmentation-aware VM placement for geographically distributed green data centers

GreenPacker: renewable- and fragmentation-aware VM placement for geographically distributed green data centers

Modeling and control of a hydrogen-based green data center

Data centers consume a large amount of energy, contributing to CO2 emissions, global warming, and resulting in significant electricity cost. To address these concerns, an increasing number of companies have considered building green data centers. The most abundant energy resource is solar; it is now a key player in the global energy transformation. Tidal energy has also received particular attention recently. The predictable characteristic of the resource makes the kinetic energy of tidal current an extremely competitive power resource compared to other commonly used renewable energies. For this purpose, a hybrid Tidal/Photovoltaic system is used for powering an MW scale green data center on a remote island. Green data centers are mainly dependent on renewable energies, which have intermittent nature and require a storage system capable of ensuring sustainable energy feeding. The proposed system consists of an MW scale proton exchange membrane electrolyzer and fuel cell. This system is associated with the LiFePO4 battery to cover the fast-electrical dynamics. In this paper, the system modeling is presented along with an initial proposal of control and energy management system to align the data center energy consumption with the renewable energy generated while respecting the different system constraints. The model is implemented in Matlab/Simulink platform where the simulation results exhibit the system performance under different operation conditions.

Joint IT-Facility Optimization for Green Data Centers via Deep Reinforcement Learning

The data center market grows rapidly with the increase of data and its corresponding applications (e.g., machine learning, cloud storage, Internet of Things, and so on). The growth is boosted recently due to the shift of activities online during the COVID-19 pandemic. Reducing the energy consumption of data centers faces various challenges that are further aggravated by the tropical conditions with high temperature and humidity in the tropics like Singapore. The prevailing siloed approach of operating the information technology (IT) and the facility systems separately has resulted in wasteful over-provisioning. The recently proposed approaches for energy usage minimization under various constraints including thermal safety scale poorly with the data center size and often result in non-optimal solutions. To advance the state of the art, we apply deep reinforcement learning (DRL) to address the scalability problem and achieve optimality over a long time horizon in reducing data center energy usage. In particular, we deploy the data-driven deep model and physical rule based model in lieu of the physical data center during the training and validation phases to manage the thermal safety risks caused by DRL's strategy of learning from errors.

A multi-dimensional double descending maximum padding priority algorithm for cloud data centers

With the development of big data technologies, green cloud data centers have become a key factor in academia and industry. An energy-efficient cloud data center can save costs for cloud computing users. However, the problem of virtual machine mapping has always been a core problem. In the most existing research, the energy consumption generated by cloud data centers has become an important bottleneck restricting the technology of cloud computing. This paper establishes a consumption model of cloud data center energy and a virtual machine mapping rule. Based on this, a multi-dimensional double descending maximum padding priority (MD3MP2) virtual machine mapping algorithm is proposed. The algorithm can not only solve the one-dimensional virtual machine mapping problem of homogeneous data centers, but also successfully solve the multi-dimensional virtual machine mapping problem of homogeneous data centers. Finally, the algorithm is compared with four other algorithms. The experimental results show that the MD3MP2 algorithm is better than the compared algorithms.

Incentive-Scheduling Algorithms to Provide Green Computational Data Center

Incentive-Scheduling Algorithms to Provide Green Computational Data Center

EXPLORATION ON GREEN COMMUNICATION TECHNOLOGIES FOR IOT IN HEALTH, EDUCATION AND INDUSTRY

To improve the protection of economyas well as environment, Green communication is used in Health, Education, Industry, as well as Ecosystem for overcoming the stumbling blocks which destroy the IoT’s ecofriendly as well as sustainable nature of the sources of energy. Followed by that, to meet these problems, green communication technologies started to decrease the losses of energy as well as decrease electronic -wastes and contaminations; for example, energy-aware tags, green cloud models, as well as resource-constrained data centers. Energy-aware decreased RFID tags with least possible frequencies employed to efficientlytrackpatients, hospital bed allotments, automatically distributing the pills, managing attendances at the entrances in various institutes, tracking children at school, digital library cards, managing supply chains. Adding to that, the green cloud employs effective switching as well as VM techniques to save energy, thereforeemployedto efficiently storeas well as retrieve of medical, institutional, as well as industrial data. Following that, green data centers alongside the least carbo footprints as well asconsuming power assistingMedical field, Educational, Industrial needsthrough saving the price for managing repositories. Though there are benefits, it is crucial to investigate it better using the new green computing technologies for IoT.This paper describesdifferent trade-offs whilemaking green IoTbe sustainable.It finds out an efficient platform to start new studies to demonstrate new and innovative green communication solutions for IoT in Health, Education, Industry.

Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings.

Combined IT and power supply infrastructure sizing for standalone green data centers

In this work, we propose a two-step methodology for designing and sizing a data center solely powered by local renewable energy. The first step consists in determining the necessary IT equipment for processing a given IT workload composed of batch and service tasks. We propose an adapted binary search algorithm and prove its optimality to find the minimum number of servers to handle the IT workload. When the IT sizing is computed, the second step consists in defining the supplying electrical infrastructure using wind turbines and photovoltaic panels as primary sources. Batteries and a hydrogen system are added as secondary sources for short- and long-term energy storage, respectively. In this electrical sizing step, first a set of primary source configurations is determined using a binary search algorithm, then the secondary sources are calibrated so that levels of charge are constant during one day and one year, respectively. Experiments using real IT workload traces and actual meteorological data are conducted to illustrate the provided methodology to decision makers for choosing the best configuration for their data center.

Design and Analysis of Sustainable Green Data Center with Hybrid Energy Sources

Development of renewable energy (RE) and mitigation of carbon dioxide, as the two largest climate action initiatives are the most challenging factors for new generation green data center (GDC). Reduction of conventional electricity consumption as well as cost of electricity (COE) with preferred quality of service (QoS) has been recognized as the interesting research topic in Information and Communication Technology (ICT) sector. Moreover, it becomes challenging to design a large-scale sustainable GDC with standalone RE supply. This paper gives spotlight on hybrid energy supply solution for the GDC to reduce grid electricity usage and minimum net system cost. The proposed framework includes RE source such as solar photovoltaic, wind turbine and non-renewable energy sources as Disel Generator (DG) and Battery. A hybrid optimization model is designed using HOMER software for cost assessment and energy evaluation to validate the effectiveness of the suggested scheme focusing on eco-friendly implication.

Research on key indicators and regional comparison of green data centre

Research on key indicators and regional comparison of green data centre

Sustainability Analysis of Green Data Centers With CCHP and Waste Heat Reuse Systems

Data centers worldwide consume a large amount of energy and pose serious threats to the environment. Combined cooling, heating, and power (CCHP) and waste heat reuse (WHR) systems are two effective approaches to improve the data center energy efficiency. However, whether and to what extent the combination of them can bring further performance improvement have not been explored yet. Moreover, it is unclear how to manage the energy flow in such a hybrid system. In this article, we try to answer these questions by proposing a data center energy architecture that includes both CCHP and WHR technologies. We formulate the energy management problem as two optimization problems with different objectives. By analytically solving the optimization problems, two energy management strategies, namely, Following the electrical load with waste heat reuse (FEL-WHR) and operating cost-aware energy management with waste heat reuse (OCM-WHR), are developed. We evaluate the performance of proposed solutions by extensive simulations using a real-world workload trace from a production data center. Numerical results suggest that the CCHP and WHR technologies generally reduce the operating cost and are more environmental friendly. If the power generation efficiency of CCHP system is high, the impact of WHR will be more conspicuous (up to 8 percent improvement) and FEL-WHR is recommended because it achieves better sustainability at an operating cost comparable to OCM-WHR. Otherwise, WHR only improves the sustainability trivially, and the energy management policy should be carefully chosen according to the data center operator's objective.

Minimizing Energy Cost for Green Data Center by Exploring Heterogeneous Energy Resource

With the deteriorating effects resulting from global warming in many areas, geographically distributed data centers contribute greatly to carbon emissions, because the major energy supply is fossil fuels. Considering this issue, many geographically distributed data centers are attempting to use clean energy as their energy supply, such as fuel cells and renewable energy sources. However, not all workloads can be powered by a single power sources, since different workloads exhibit different characteristics. In this paper, we propose a fine-grained heterogeneous power distribution model with an objective of minimizing the total energy costs and the sum of the energy gap generated by the geographically distributed data centers powered by multiple types of energy resources. In order to achieve these two goals, we design a two-stage online algorithm to leverage the power supply of each energy source. In each time slot, we also consider a chance-constraint problem and use the Bernstein approximation to solve the problem. Finally, simulation results based on real-world traces illustrate that the proposed algorithm can achieve satisfactory performance.

Research on key indicators and regional comparison of green data centre

With the continuous growth of the amount of information, the scale of green data centre is becoming larger and larger, and it is more and more widely used, the construction of green index system of green data centre has become a key problem to be solved urgently. This paper studied on the basic index system of green data centre, with the help of PUE, CEI and TCO index analysis, it is made in depth analysis of the factors affecting the green index of the green data centre, factor analysis is used from nine aspects such as annual average temperature, annual precipitation, air quality index, earthquake belt and fixed assets of information transmission enterprises, the mathematical model of evaluating the operation advantage of green data centre is constructed, to made quantitative analysis and evaluation classification, and according to the calculation, the comparison results of green data centre development area are obtained.

Minimizing the operation cost of distributed green data centers with energy storage under carbon capping

The expensive cost and intermittent availability of renewable energy bring great challenges to its efficient utilization in green data centers. In this paper, we propose a new way to achieve an explicit trade-off between operational cost and carbon emission by dynamic storing off-site renewable energy in distributed data centers. We first formulate a constrained stochastic optimization problem for cost minimization of data centers. Then, by leveraging Lyapunov optimization theory, we design an online Carbon Capped Cost Minimization algorithm (CCCM) to achieve a near-optimal cost with rigorous mathematical proof. Specially, the decisions at each time slot are determined with an efficient iterative algorithm based on the Generalized Benders Decomposition (GBD) technique. Finally, extensive simulations are conducted to show the effectiveness of our algorithm. The results show that our algorithm can save about 6% total costs compared with the algorithm without offsite energy storage.

Role of green data center in human resources development model

Human resources are now a very important factor in the development of various enterprises and even countries, and how to master and manage human resources has always been a problem. Nowadays, the development of science and technology has made the processing of various data simple, and green data processing centers that save energy have also been established. This paper is to study the role of green data center in terms of human resources development model, by using mixed integer linear programming optimization mathematical model of the existing data center, reduce data center energy consumption NRE while establishing a new green data center, and the data center conducts related research on the information search of human resources and its auxiliary role on how to efficiently hire and train employees. The research results show that the green data center can play a great role in the development of human resources. Compared with relying solely on human resource managers, the use of green data centers to manage human resources has increased the decision-making ability of corporate personnel by 12 %, and personnel efficiency has increased by about 10 %, basically realizing comprehensive and effective management of human resources.

Power Control Framework for Green Data Centers

In recent years, renewable energy, such as wind and photovoltaic electric power has been increasingly integrated into data center power provisioning systems to address high energy consumption of data centers. However, in reality, the intermittency and randomness of renewable energy (power supply fluctuation) is detrimental to the reliable operation of sophisticated IT equipment in those so-called green data centers. In this article, we address the problem of data center power regulation explicitly taking into account the unreliability and instability of renewable energy sources. To this extent, we design a novel data center power control framework that smoothens the power fluctuation and instability of renewable energy sources. The core of our framework is two power regulation optimization algorithms. In particular, a server workload scheduling algorithm deals with high frequency fluctuations while an Uninterruptable Power Supply (UPS) power regulation algorithm handles low frequency and large extent power fluctuations. These algorithms are also designed to satisfy service level agreement (SLA) and standby power supply capacity. We have conducted an extensive evaluation study using trace data of a real data center of 30000-node cluster with 50 x 250 UPS battery groups and 24-hour power generation data from real wind farm and photovoltaic power station. The experimental results show our framework effectively smoothens fluctuations of data center power supply, more effective use of renewable energy, and extend the UPS batteries’ lives to reduce the skyrocketed data center operating expenses.

Numerical simulation of solar chimney-based direct airside free cooling system for green data centers

The growing number, size, complexity, and energy density of data centers result in considerable energy challenge. The implementation of efficient and eco-friendly cooling systems, strategies, and methods that can help reduce the energy consumption of traditional cooling systems is imperative for data centers. In this study, an innovative concept of a solar chimney-based direct airside free cooling (SC-DAFC) system is proposed for data centers to achieve energy saving. A theoretical model of the SC-DAFC system is developed to evaluate the indoor thermal environment, ventilation flow rate, and heat recovery of the data center under given climate conditions. 3D numerical simulations are carried out to investigate the effects of raised floor height and turbine pressure drop on the cooling performance of the system. An economic analysis is also conducted by calculating the net present value and dynamic investment recovery period of the project. Results indicate that the thermal environment in the SC-DAFC data center can meet the cooling requirements under favorable climate conditions, and such project has strong profitability. The SC-DAFC system is technically and financially feasible, thereby providing efficient solutions that reduce energy consumption and operational costs.