Data Center Energy Research Articles

Optimal cost management of the CCHP based data center with district heating and district cooling integration in the presence of different energy tariffs

Data centers are expected to be one of the major prosumers in energy networks because of their high electricity demands as well as being a possible source of waste heat energy utilization. A combined cooling, heat, and power (CCHP) system can be a perfect match with a data center for supplying the data center's less fluctuated, uninterrupted power and cooling demand. Moreover, CCHP based data center integration with regional district heating and cooling system can gain data center's excess heat, improve the efficiency of the primary energy sources, and give flexibility to district energy operators. This paper aims to offer a comprehensive energy cost management for a CCHP based data center that considers power, heating, cooling, and data center's excess heat utilization in the presence of hourly energy prices and varying outdoor temperatures. Mixed Integer Linear Programming (MILP) is used to determine a cost-optimized operational strategy for the overall system. The result shows that CCHP and regional energy integration to the data center decrease the overall energy cost of the operation while providing more flexibility to the data center and district energy network operators. Optimized operation of the CCHP system with the data center's excess heat recovery, district heating, and cooling integration can cut its annual electricity costs by 40.3% compared to the local operation of the CCHP based data center. Return of investment (ROI) is estimated to be in 6.6 years for additional required systems for the conventional data center.

CUCKOO-ANN Based Novel Energy-Efficient Optimization Technique for IoT Sensor Node Modelling

Wireless sensor networks (WSNs) based on the Internet of Things (IoT) are now one of the most prominent wireless sensor communication technologies. WSNs are often developed for particular applications such as monitoring or tracking in either indoor or outdoor environments, where battery power is a critical consideration. To overcome this issue, several routing approaches have been presented in recent years. Nonetheless, the extension of the network lifetime in light of the sensor capabilities remains an open subject. In this research, a CUCKOO-ANN based optimization technique is applied to obtain a more efficient and dependable energy efficient solution in IoT-WSN. The proposed method uses time constraints to minimize the distance between sources and sink with the objective of a low-cost path. Using the property of CUCKOO method for solving nonlinear problem and utilizing the ANN parallel handling capability, this method is formulated. The resented model holds significant promise since it reduces average execution time, has a high potential for enhancing data centre energy efficiency, and can effectively meet customer service level agreements. By considering the mobility of the nodes, the technique outperformed with an efficiency of 98% compared with other methods. The MATLAB software is used to simulate the proposed model.

Cleaner, greener data centres

Abstract John Booth MBCS, Data Centre Energy Efficiency and Sustainability Consultant at Carbon3IT, explores the detrimental trajectory of data centre energy use, against a backdrop of COP26, climate change and proposed EU directives.

Multi-resource collaborative optimization for adaptive virtual machine placement.

The unbalanced resource utilization of physical machines (PMs) in cloud data centers could cause resource wasting, workload imbalance and even negatively impact quality of service (QoS). To address this problem, this paper proposes a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration. It uses Gaussian model to adaptively estimate the probability that the running PMs are in the multi-resource utilization balance status. Given the estimated probability of the multi-resource utilization balance state, we propose effective selection algorithms for live VM migration between the source hosts and destination hosts, including adaptive Gaussian model-based VMs placement (AGM-VMP) algorithm and VMs consolidation (AGM-VMC) method. Experimental results show that the AGM-VMC method can effectively achieve load balance and significantly improve resource utilization, reduce data center energy consumption while guaranteeing QoS.

Retracted] Dynamic Combined Optimal Scheduling of Electric Energy and Natural Gas Energy Consumption in Data Center

Energy consumption is the key factor leading to high operating costs in data center. In this paper, considering the data center service level under the premise of using electricity and gas as energy supply of energy supply and energy consumption from two angles, the data center energy scheduling model is built. The lower model is calculation scheduling model for the data center, and the upper model is data center energy supply scheduling model. Then the particle swarm algorithm is used to simulate the schedule. The results show that using natural gas as supplementary energy supply can effectively reduce the overall energy consumption of data center in considering the service delay level of data center.

Performance and energy-aware bi-objective tasks scheduling for cloud data centers

Cloud computing enables remote execution of users’ tasks. The pervasive adoption of cloud computing in smart cities’ services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm.

MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers

Improving the energy efficiency of data centers while guaranteeing Quality of Service (QoS), together with detecting performance variability of servers caused by either hardware or software failures, are two of the major challenges for efficient resource management of large-scale cloud infrastructures. Previous works in the area of dynamic Virtual Machine (VM) consolidation are mostly focused on addressing the energy challenge, but fall short in proposing comprehensive, scalable, and low-overhead approaches that jointly tackle energy efficiency and performance variability. Moreover, they usually assume over-simplistic power models, and fail to accurately consider all the delay and power costs associated with VM migration and host power mode transition. These assumptions are no longer valid in modern servers executing heterogeneous workloads and lead to unrealistic or inefficient results. In this paper, we propose a centralized-distributed low-overhead failure-aware dynamic VM consolidation strategy to minimize energy consumption in large-scale data centers. Our approach selects the most adequate power mode and frequency of each host during runtime using a distributed multi-agent Machine Learning (ML) based strategy, and migrates the VMs accordingly using a centralized heuristic. Our Multi-AGent machine learNing-based approach for Energy efficienT dynamIc Consolidation (MAGNETIC) is implemented in a modified version of the CloudSim simulator, and considers the energy and delay overheads associated with host power mode transition and VM migration, and is evaluated using power traces collected from various workloads running in real servers and resource utilization logs from cloud data center infrastructures. Results show how our strategy reduces data center energy consumption by up to 15 percent compared to other works in the state-of-the-art (SoA), guaranteeing the same QoS and reducing the number of VM migrations and host power mode transitions by up to 86 and 90 percent, respectively. Moreover, it shows better scalability than all other approaches, taking less than 0.7 percent time overhead to execute for a data center with 1,500 VMs. Finally, our solution is capable of detecting host performance variability due to failures, automatically migrating VMs from failing hosts and draining them from workload.

Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System

Integrating cloud computing with wireless sensor networks creates a sensor cloud (WSN). Some real‐time applications, such as agricultural irrigation control systems, use a sensor cloud. The sensor battery life in sensor clouds is constrained. The data center’s computers consume a lot of energy to offer storage in the cloud. The emerging sensor cloud technology‐enabled virtualization. Using a virtual environment has many advantages. However, different resource requirements and task execution cause substantial performance and parameter optimization issues in cloud computing. In this study, we proposed the hybrid electro search with ant colony optimization (HES‐ACO) technique to enhance the behavior of task scheduling, for those considering parameters such as total execution time, cost of the execution, makespan time, the cloud data center energy consumption like throughput, response time, resource utilization task rejection ratio, and deadline constraint of the multicloud. Electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC‐PSO, and PSO‐COGENT algorithms, the created HES‐ACO algorithm was simulated at CloudSim and found to optimize all parameters.

Hybrid and adaptive harmony search algorithm for optimizing energy efficiency in VMP problem in cloud environment

Data Center energy usage has risen dramatically because of the rapid growth and demand for cloud computing. This excessive energy usage is a challenge from an economic and environmental point. Virtual Machine Placement (VMP) along with virtualization technologies is widely used to manage power utilization in data centers. The assignment of virtual machines to physical machines affects energy consumption. VMP is a process of mapping VMs onto a set of PMs in a data center to minimize total power consumption and maximize resource utilization. The VMP is an NP-hard problem due to its constraints and huge combinations. In this paper, we formulated the problem as a single objective optimization problem in which the objective is to minimize the energy consumption in cloud data centers. The main contribution of this paper is hybrid and adaptive harmony search algorithm for optimal placements of VMs to PMs. HSA with adaptive PAR settings, simulated annealing and local search strategy aims at minimizing energy consumption in cloud data centers with satisfying given constraints. Experiments are conducted to validate the performance of these variations. Results show that these hybrid HSA variations produce better results than basic HSA and adaptive HSA. Hybrid HS with simulated annealing, and local search strategy gives better results than other variants for 80 percent datasets.

The Use of Single-Phase Immersion Cooling by Using Two Types of Dielectric Fluid for Data Center Energy Savings

Data centers are recognized as one of the most important aspects of the fourth industrial revolution since conventional data centers are inefficient and have dependency on high energy consumption, in which the cooling is responsible for 40% of the usage. Therefore, this research proposes the immersion cooling method to solving the high energy consumption of data centers by cooling its component using two types of dielectric fluids. Four stages of experimental methods are used, such as fluid types, cooling effectiveness, optimization, and durability. Furthermore, benchmark software is used to measure the CPU maximum work with the temperature data performed for 24 h. The results of this study show that the immersion cooling reduces 13°C lower temperature than the conventional cooling method which means it saves more energy consumption in the data center. The most optimum variable used to decrease the temperature is 1.5 lpm of flow rate and 800 rpm of fan rotation. Furthermore, the cooling performance of the dielectric fluids shows that the mineral oil (MO) is better than the virgin coconut oil (VCO). In durability experiment, there are no components damage after five months immersed in the fluid.

Data centre energy efficiency optimisation in high-speed packet I/O frameworks

To meet the demands for faster network packet processing requirements in data centres, packet I/O accelerator solutions have emerged which are dedicated to enabling faster packet forwarding capabilities. Often, such I/O frameworks are implemented in software that is hosted on a Linux operating system and for many of the solutions the software is run in the system's userspace, thus bypassing the operating system's kernel to facilitate a streamlined packet throughput rate. However, operating outside of the kernel loses the real-time interrupt facility, so many of the I/O accelerator schemes rely on a very high-speed polling solution to react to receive packet traffic arrivals; the continuous fast polling rate results in the CPU running hot and wasting energy. An experimental approach to reduce such energy inefficiencies is introduced based on an adaptive polling mechanism (APM). The investigation results show potential energy CPU savings ranging between 9% and 76%.

Datacentre energy efficiency optimization in high-speed packet I/O frameworks

Datacentre energy efficiency optimization in high-speed packet I/O frameworks

SLA-Aware and Energy-Efficient Virtual Machine Placement and Consolidation in Heterogeneous DVFS Enabled Cloud Datacenter

Increasing demand for computational resource as services over the internet has led to the expansion of datacenter infrastructures. Thus, datacenter authorities are striving to adopt optimal power usage schemes to minimize costs, emissions and Service Level Agreement (SLA) violations in their task scheduling for heterogeneous computation centers. One of the most effective strategies to reduce datacenter energy consumption is to maximize the utilization of physical machines and shut down the idle ones. This can be realized through two main algorithms, namely virtual machine placement and virtual machine consolidation. The VM placement method is a dynamic process to put these virtual devices on physical machines. The consolidation technique, however, tries to improve physical machine efficiency through grouping and live migration of dispersed virtual machines on lower number of active physical machine. In this paper, a novel approach is proposed for improving the physical machine efficiency. The approach employs heuristics and meta-heuristic algorithms with eight performance criteria and is implemented on small to medium scale data centers using simulated cloud module. The results indicates that the proposed method showed up to 10.3%, 5.3%, and 12.5% the more significant efficiency rather best previous algorithms, respectively, in terms of the energy consumption, number of SLA violation and number of VMs migration.

LECC: Location, energy, carbon and cost-aware VM placement model in geo-distributed DCs

The wide adoption of the cloud computing model in the business environment has led to a rapid increase in the development of geo-distributed data centers (DCs) to support customers’ needs. On the other hand, cloud DCs that contain thousands of computing and storage nodes consumes a large amount of energy that leads to a high carbon footprint. Therefore, minimizing the geo-distributed DCs’ energy consumption is a must which will decrease the cloud providers’ operational cost and minimize the high non-environment carbon emission. In addition, minimizing the cloud users’ network latency, in such a distributed environment, is one of the important challenges faced by cloud providers. Thus, to address these challenges, this paper proposed a novel location, energy, carbon, and cost-aware (LECC) virtual machine (VM) placement model for geo-distributed cloud DCs. Both online and offline placement problems are tackled. The migration technology is employed in consolidating the VMs to less number of active servers for significant energy reductions. The VM placement problem is formulated as a multi-objective optimization problem and solved by greedy policies. Also, an intelligent machine-learning model is constructed and implemented to leverage the performance of the LECC model. To validate the usefulness of the LECC model, extensive simulations using synthetic and real data are conducted on CloudSim toolkit. The experimental results showed the merits of our proposed LECC model in solving the important VM placement problem.

Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures

The rapid increase in the IT infrastructure has led to demands in more Data Center Space & Power to fulfil the Information and Communication Technology (ICT) services hosting requirements. Due to this, more electrical power is being consumed in Data Centers therefore Data Center power & cooling management has become quite an important and challenging task. Direct impacting aspects affecting the power energy of data centers are power and commensurate cooling losses. It is difficult to optimise the Power Usage Efficiency (PUE) of the Data Center using conventional methods which essentially need knowledge of each Data Center facility and specific equipment and its working. Hence, a novel optimization approach is necessary to optimise the power and cooling in the data center. This research work is performed by varying the temperature in the data center through a machine learning-based linear regression optimization technique. From the research, the ideal temperature is identified with high accuracy based on the prediction technique evolved out of the available data. With the proposed model, the PUE of the data center can be easily analysed and predicted based on temperature changes maintained in the Data Center. As the temperature is raised from 19.73 oC to 21.17 oC, then the cooling load is decreased in the range 607 KW to 414 KW. From the result, maintaining the temperature at the optimum value significantly improves the Data Center PUE and same time saves power within the permissible limits.

Interact: IT infrastructure energy and cost analyzer tool for data centers

The environmental impact of data centers has become a major concern. In their Q1 2021 report, Intel estimated that there are around 100 million servers deployed globally. In this paper we provide an overview of Interact, a machine learning tool that helps data center operators easily analyze, manage, and optimize the energy, cost, and Carbon footprint of their current servers and infrastructure. We discuss in detail the machine learning algorithms and the energy models used. We also demonstrate the key features of the tool and how it can help data centers become more sustainable by reducing server inefficiencies.

A PERSPECTIVE OF ENERGY AND POWER QUALITY OF DATA CENTER ON THE GRID

Data centers are believed to be a significant part of the load in many areas in the future. The future trend of energy usage in data centers depends on many factors including the efficiency of the equipment used and IT usage. Loads such as residential loads, office loads, agriculture loads, etc. show a daily, weekly and seasonal variation in the power consumption. The behavior of data centers w.r.t power consumption may differ. The paper, therefore, characterizes data centers from the grid perspective in terms of their power consumption and power quality. An example grid consisting of data center and renewable energy sources is used as a reference and the impact on power quality, in terms of harmonics and their propagation for the system is presented. The paper also shows how different power generating sources (E.g. wind, solar, hydro, etc.) perceive the data center as a load.

Resource clustering algorithm for cloud data centers

Recently, the virtual machine deployment algorithm uses physical machine less or consumes higher energy in data centers, resulting in declined service quality of cloud data centers or rising operational costs, which leads to a decrease in cloud service provider’s earnings finally. According to this situation, a resource clustering algorithm for cloud data centers is proposed. This algorithm systematically analyzes the cloud data center model and physical machine’s use ratio, establishes the dynamic resource clustering rules through k-means clustering algorithm, and deploys the virtual machines based on clustering results, so as to promote the use ratio of physical machine and bring down energy consumption in cloud data centers. The experimental results indicate that, regarding the compute-intensive virtual machines in cloud data centers, compared to contrast algorithm, the physical machine’s use ratio of this algorithm is improved by 12% on average, and its energy consumption in cloud data center is lowered by 15% on average. Regarding the general-purpose virtual machines in cloud data center, compared to contrast algorithm, the physical machine’s use ratio is improved by 14% on average, and its energy consumption in cloud data centers is lowered by 12% on average. Above results demonstrate that this method shows a good effect in the resource management of cloud data centers, which may provide reference to some extent.

Research on the Application of Renewable Energy in DC Data Center

With the rapid development of the information technology industry, the demand for data centers is gradually expanding. As the foundation of carrying many information technology services, data centers have been built and put into use on a large scale. At the same time, the characteristics of huge energy consumption are more obvious. Due to the defects of low utilization rate, heavy pollution and limited reserves of traditional fossil fuels when supplying energy for data centers, the development of green renewable energy has become an inevitable demand. This paper studies the key technologies involved in supplying power to DC power supply data center with renewable energy, and makes a case to compare the DC power supply system and the AC power supply system. This paper also pays attention to the technology of energy storage in data center, and makes an analysis to prove the advantages of energy storage data center. In addition, this paper studies and summarizes the application of multi-station integration technology and other issues.

Electric Power-grid Friendly Characteristic Data Center Energy Consumption Optimization Method

With the acceleration of the digitization process, the load of the data center on the power grid continues to increase. During the operation of the data center, the load demand on the power grid is relatively large, and the load demand on the power grid fluctuates greatly. In the Power industry, the average PUE(power usage effectiveness) of data center is above 2, which is extremely unfriendly to the power grid. This paper proposes and summarizes the optimization parameters at various levels, the energy efficiency of software and hardware, and provides a method for optimization through global variables for high-efficiency and energyconsuming data centers, which can minimize cooling energy consumption and IT energy consumption. To build a electric power grid-friendly characteristic data center with high efficiency and low energy consumption.