Energy Management In Data Centers Research Articles

Next-generation data center energy management: a data-driven decision-making framework

In the era of society’s ongoing digitization and the exponential growth in data volume, alongside a growing energy demand, energy management plays an integral role in data centers (DCs) and is a key factor in the quest for decarbonization. In light of the complex nature of DCs, traditional energy management strategies are inadequate. This research introduces a data-driven decision-making framework for DCs, grounded in the OODA (Observation, Orientation, Decision, and Action) loop and based on insights from an Ericsson-operated DC in Linköping, Sweden. The developed framework enables DCs to enhance energy efficiency effectively. Rooted in the OODA loop and leveraging extensive datasets from DCs’ building management systems, this framework aids in decreasing cooling energy usage through strategic, data-driven decision-making. By adopting AI methods, specifically K-means clustering in this research, for continuous monitoring and fine-tuning (Proportional, Integral, Derivative) PID parameters, the framework aids in improving operational efficiency.

Stochastic optimization for capacity configuration of data center microgrid thermal energy management equipment considering flexible resources

The rapid development of Internet and cloud computing technologies has led to the expansion of the scale of data centers, resulting in a continuous increase in the demand for data processing and storage. This not only results in higher energy consumption but also makes thermal management a key factor of energy management in data centers. A capacity configuration method is proposed for the core thermal management equipment of data center microgrids, electric boilers, cooling systems, and heat pumps. This method not only considers the uncertainty factors of the source and load but also effectively utilizes the characteristics of various flexible resources in data centers, such as the adjustable ability of different types of batch processing loads, air thermal inertia, and waste heat recovery, significantly improving the energy utilization efficiency and enhancing the system’s economy and flexibility. In addition, a wind power scenario generation method based on conditional least squares generative adversarial networks was proposed to improve the generation quality of wind power scenarios, and a random optimization model was constructed. Considering a certain province’s data center microgrid as a case study, the effectiveness of the model was verified, and a sensitivity analysis of the relevant parameters provided important references for microgrid planning. This study provides a new perspective and tool for the thermal management of data center microgrids and comprehensive utilization of renewable energy, which is of great significance for promoting the improvement of data center energy efficiency and sustainable development.

Unveiling Genetic Reinforcement Learning (GRLA) and Hybrid Attention-Enhanced Gated Recurrent Unit with Random Forest (HAGRU-RF) for Energy-Efficient Containerized Data Centers Empowered by Solar Energy and AI

The adoption of renewable energy sources has seen a significant rise in recent years across various industrial sectors, with solar energy standing out due to its eco-friendly characteristics. This shift from conventional fossil fuels to solar power is particularly noteworthy in energy-intensive environments such as cloud data centers. These centers, which operate continuously to support active servers via virtual instances, present a critical opportunity for the integration of sustainable energy solutions. In this study, we introduce two innovative approaches that substantially advance data center energy management. Firstly, we introduce the Genetic Reinforcement Learning Algorithm (GRLA) for energy-efficient container placement, representing a pioneering approach in data center management. Secondly, we propose the Hybrid Attention-enhanced GRU with Random Forest (HAGRU-RF) model for accurate solar energy prediction. This model combines GRU neural networks with Random Forest algorithms to forecast solar energy production reliably. Our primary focus is to evaluate the feasibility of solar energy in meeting the energy demands of cloud data centers that utilize containerization for virtualization, thereby promoting green cloud computing. Leveraging a robust German photovoltaic energy dataset, our study demonstrates the effectiveness and adaptability of these techniques across diverse environmental contexts. Furthermore, comparative analysis against traditional methods highlights the superior performance of our models, affirming the potential of solar-powered data centers as a sustainable and environmentally responsible solution.

Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization

As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.

Optimal Data Center Energy Management With Hybrid Quantum-Classical Multi-Cuts Benders' Decomposition Method

Optimal Data Center Energy Management With Hybrid Quantum-Classical Multi-Cuts Benders' Decomposition Method

Spatio-temporal load migration potential of data centers: Evaluation and application

The wide distribution of data centers and the delay tolerance of computing tasks endow data center loads with adjustable characteristics in both temporal and spatial dimensions. Due to the characteristics of abundant spatiotemporal flexibility, data centers can participate in the optimization of power system operation and regulation. To quantify this flexibility, this paper proposes a spatiotemporal load migration potential evaluation model. Meanwhile, a data center energy management strategy is combined with the characteristics of spatiotemporal load migration, which deeply exploits the migration potential through the spatiotemporal redistribution of delay-tolerant tasks, as well as server ON/OFF scheduling and CPU operating frequency scaling across different spatial locations. A case study demonstrates that adopting the proposed approach considering an energy management strategy can effectively improve the load migration potential of data centers. The migration characteristics of data centers have great application prospects in reducing carbon emissions and enhancing operational flexibility.

Fine-Grained Online Energy Management of Edge Data Centers Using Per-Core Power Gating and Dynamic Voltage and Frequency Scaling

It is important to minimize the energy consumption of large-scale, geographically distributed edge data centers (EDCs). While modern processing units (PUs) have energy-saving features like Dynamic Voltage and Frequency Scaling (DVFS) and Per-Core Power Gating (PCPG), optimization is still complex and requires a holistic approach. This paper presents a new decentralized, three-timescale, online optimization approach that enables multicore micro data centers (MDCs) to optimize their per-PU power states, per-enabled-PU voltage-frequency levels and offloading schedules at three different timescales. The key idea is that we employ multi-timescale Lyapunov optimization to decouple the energy minimization between workload scheduling and result delivery at a small timescale and PU configuration at large timescales. Another important aspect is that we apply the primal decomposition to decouple the PU configuration between a per-enabled-PU voltage-frequency level at an intermediate timescale and a per-PU power state at a large timescale. Experiments demonstrate that the proposed approach improves energy efficiency significantly by up to 4.5 times in our considered lightly loaded situations where DVFS alone does not work effectively, compared to existing benchmarks.

Advanced data analytics modeling for evidence-based data center energy management

Over the past few decades, the demand for Data Center (DC) services has significantly increased due to the world’s growing need for internet access, social networking, and data storage. Data Centers are among the most energy-intensive businesses, so optimizing IT operations in DC requires energy-efficient techniques. This paper presents AI based modeling strategies for effective energy management with a particular emphasis on DC’s two most energy intensive systems (i.e., cooling and IT systems). This study addresses the issues of IT equipment performance degradation, inappropriate IT room thermal conditions, inefficient workload placement, and excessive energy waste. This research entails the application of machine learning for DC thermal classification, and deployment of deep learning models to predict resource utilization and energy consumption in DC. Furthermore, a comparative analysis is performed with existing relevant methods to demonstrate the effectiveness and accuracy of the proposed AI techniques. The findings of this study also provide evidence-based recommendations for DC efficient energy management.

Research on overall energy consumption optimization method for data center based on deep reinforcement learning

With the rapid development of cloud computing, there are more and more large-scale data centers, which makes the energy management of data centers more complex. In order to achieve better energy-saving effect, it is necessary to solve the problems of concurrent management and interdependence of IT, refrigeration, storage, and network equipment. Reinforcement learning learns by interacting with the environment, which is a good way to realize the independent management of the data center. In this paper, a overall energy consumption method for data center based on deep reinforcement learning is proposed to achieve collaborative energy saving of data center task scheduling and refrigeration equipment. A new multi-agent architecture is proposed to separate the training process from the execution process, simplify the interaction process during system operation and improve the operation effect. In the deep learning stage, a hybrid deep Q network algorithm is proposed to optimize the joint action value function of the data center and obtain the optimal strategy. Experiments show that compared with other reinforcement learning methods, our method can not only reduce the energy consumption of the data center, but also reduce the frequency of hot spots.

A digital-twin and machine-learning framework for precise heat and energy management of data-centers

A digital-twin and machine-learning framework for precise heat and energy management of data-centers

Challenges and opportunities for the energy management of sustainable data centers in smart grids

With the increase of cloud computing and internet services, data centers are emerging to satisfy the requirement, leading to incremental energy consumption demand and emissions of green house gases. Thus, integration of renewable energies with the traditional power grid is preferred to reduce the environmental impact and increase energy efficiency, which lead to a demand of energy management strategies to coordinate the energy demand and generation. In this paper, we review the challenges for the sustainable data centers in smart grids with regards to energy management strategies, integration with renewable energies and cyber-attacks and propose possible solutions. Through the analysis of the data centers from the perspective of both smart grids level and micro-grid level, the research challenges and potential research directions in the energy management of sustainable data centers have been discussed.

Exploiting Renewable Energy and UPS Systems to Reduce Power Consumption in Data Centers

Leveraging on-site renewable sources like solar and wind provides ample opportunities on developing environmental friendly and energy-efficient data centers. Due to intermittent nature of renewable energy and fluctuating grid energy price, advanced data centers often deploy distributed UPS systems with high efficiency, scalability, and reliability. In this study, we investigate the problem of energy management for data centers with renewable resources and energy storages. We propose an interface-backbone layer framework designed unified energy efficiency management system called REDUX, which properly allocate fluctuating renewable energy, UPS battery energy storage or grid power with dynamic price aiming to minimize the long-term electricity bill for the data center. With prediction of renewable energy supply, categorization of grid power price level and energy storage in the UPS devices, REDUX orchestrates workload distribution with heuristic algorithms which act as renewable energy smoothing, UPS device control, and high level control strategies, and make back-fills or defer decisions for the non-urgent jobs. Compared with the existing strategies, REDUX provides a comprehensive view for the energy efficiency data center and demonstrates a prominent capability of mitigating average peak workload and boosting renewable-energy utilization.

Energy management of Internet data centers in multiple local energy markets

Active distribution network Internet Data Centers (IDCs) are large energy consumers in modern distribution networks. The integration of renewable energy and energy storage systems transforms IDCs into proactive energy prosumers, and enables them to participate in the energy trading in regional local energy markets. This paper proposes an energy management framework for a Cloud Service Provider (CSP) that manages multiple geographically distributed IDCs. The framework provides decision-making support for the CSP to optimally schedule the IDCs’ cyber-energy resources (computing requests, on-site battery energy storage systems, and on-site renewable energy sources). In this way, the proposed framework facilitates the CSP to proactively participate in the local energy markets situated in the regions that host the IDCs, with the objective to minimize the CSP's total cost over a finite operation horizon. We use an effective evolutionary computation algorithm to solve the proposed energy management model and design extensive numerical simulations to validate the effectiveness of the proposed method.

Smart Architecture Energy Management through Dynamic Bin-Packing Algorithms on Cloud

Smart Home Architecture is suitable for progressive and symmetric urbanization. Data being generated in smart home appliances using internet of things should be stored in cloud where computing resources can analyze the data and generate the decisive pattern within no time. This additional requirement of storage, majorly, comprising of unfiltered data escalates requirement of host machines which carries with itself extra overhead of energy consumption; thus, extra cost has to be beard by service providers. Various static algorithms are already proposed to improve energy management of cloud data centers by reducing number of active bins. These algorithms are not able to cater to the needs of present heterogeneous requests generated in cloud machines by people of diversified work environment with adhering to the requirements of quality parameters. Therefore, the paper has proposed and implemented dynamic bin-packing approaches for smart architecture that can significantly reduce energy consumption without compromising upon makespan, resource utilization and Quality of Service (QoS) parameters. The novelty of the proposed dynamic approaches in comparison to the existing static approaches is that the proposed approach dynamically creates and dissolves virtual machines as per incoming and completed requests which is a dire need of present computing paradigms via attachment of time-frame with each virtual machine. The simulations have been performed on JAVA platform and dynamic energy utilized-best fit decreasing bin packing technique has produced better results in maximum runs.

Aggregation-Based Colocation Datacenter Energy Management in Wholesale Markets

In this paper, we study how colocation datacenter energy cost can be effectively reduced in the wholesale electricity market via cooperative power procurement. Intuitively, by aggregating workloads and renewables across a group of tenants in a colocation datacenter, the overall power demand uncertainty of the colocation datacenter can be reduced, resulting in less chance of being penalized when participating in the wholesale electricity market. We use cooperative game theory to model the cooperative electricity procurement process of tenants as a cooperative game, and show the cost saving benefits of aggregation. Then, a cost allocation scheme based on the marginal contribution of each tenant to the total expected cost is proposed to distribute the aggregation benefits among the participating tenants. Besides, we propose proportional cost allocation scheme to distribute the aggregation benefits among the participating tenants after realizations of power demand and market prices. Finally, numerical experiments based on real-world traces are conducted to illustrate the benefits of aggregation compared to noncooperative power procurement.

Distributed Energy Management for Multiple Data Centers With Renewable Resources and Energy Storages

For Internet and cloud computing service providers, running massive geo-distributed data centers incurs prodigious electricity cost and water consumption as well as carbon emission rooted in electricity generation. Thus, it is critical significant for providers to lower down the operation cost of data centers. In this article, we investigate the problem of energy management for geo-distributed data centers with renewable resources and energy storages. We aim to minimize the long-term operation cost including electricity cost, water consumption, and carbon emission by leveraging the spatiotemporal diversity of these system states. To this end, we first formulate the cost minimization problem as a stochastic optimization problem, then we adopt the Lyapunov optimization technique to design a close-to-optimal online algorithm which only needs the current system information and achieves a delicate tradeoff between system cost and performance of delay tolerant workloads. To reduce the computational complexity and unnecessary communication, we further propose a distributed algorithm based on the distributed computing framework alternating direction method of multipliers (ADMM), which enables each data center to make their own control decisions. Based on the real-world traces and extensive simulations, we demonstrate the effectiveness of our proposed algorithms.

Blockchain-based decentralized workload and energy management of geo-distributed data centers

Minimizing the costs of cloud services is important for cloud service providers to grow their business. In cost calculation of cloud services, power consumption cost is a major factor. The variability present in renewable power generation, electricity price, and power demand variates the power utilization expenditures. Due to high computational requests, datacenters consume enormous energy every day, thereby necessitating the need for energy efficient designs. Therefore, an energy consumption cost reduction mechanism is required to optimize the data center’s power consumption cost locally and to have the option to redistribute the workload on geo-distributed data centers in peak hours for further reduction in power consumption cost. In this paper, the authors investigate the energy consumption cost optimization problem in cloud datacenters and propose a blockchain-based decentralized workload distribution and management model. Moreover, we minimize request scheduler time for transferring any job from one data center to another and secure the energy cost optimization process due to shut down/ communication failure. The proposed work is the primitive to present the energy cost optimization problem in geo-distributed data centers by introducing blockchain-based secure workload scheduling method considering both the temporal and spatial disparities associated with electricity tariffs and workload arrival process. The simulations are evaluated on real-world workload of the Google data center and associated electricity tariffs. The blockchain-based decentralized workload management framework is evaluate based on privacy and security analysis, update overhead, latency, and throughput. Moreover, the results show blockchain model migrate workload in minimum 46 % reduced time compared to conventional model.

Energy as a Service

Energy as a product is a commodity. Differentiation is very difficult, customer loyalty and brand values are low, competition is based primarily on price, and sustainable advantage comes from cost leadership. Energy as a product is subject to the typical commodity dynamics – recurring cycles in investment, capacity utilization, price competition, and profitability. Energy as a service (EaaS) offers opportunities to escape from the commodity game. It creates value by solving important problems for a segment of customers; The experience is key. Examples of EaaS include energy management for data centers, charging infrastructure for electric vehicles, fuel cost optimization for airlines, and energy risk management. EaaS often involves innovative business models that use customer information to create value. Trust plays a central role in the implementation of EaaS. It determines what you can do with information, i.e., observe, capture, analyze, and use to create value. Customer information has become the most valuable asset, especially in commoditized markets. This paper presents examples of EaaS including results from projects undertaken by teams of graduate students at the Massachusetts Institute of Technology. It discusses the implementation of EaaS and describes the way forward with a new generation of EaaS. The next generation of EaaS is redefining the relationship between energy consumers and suppliers in a more fundamental way.

On Energy Conservation in Data Centers

We formulate and study an optimization problem that arises in the energy management of data centers and, more generally, multiprocessor environments. Data centers host a large number of heterogeneous servers. Each server has an active state and several standby/sleep states with individual power consumption rates. The demand for computing capacity varies over time. Idle servers may be transitioned to low-power modes so as to rightsize the pool of active servers. The goal is to find a state transition schedule for the servers that minimizes the total energy consumed. On a small scale, the same problem arises in multicore architectures with heterogeneous processors on a chip. One has to determine active and idle periods for the cores so as to guarantee a certain service and minimize the consumed energy. For this power/capacity management problem, we develop two main results. We use the terminology of the data center setting. First, we investigate the scenario that each server has two states: an active state and a sleep state. We show that an optimal solution, minimizing energy consumption, can be computed in polynomial time by a combinatorial algorithm. The algorithm resorts to a single-commodity minimum-cost flow computation. Second, we study the general scenario that each server has an active state and multiple standby/sleep states. We devise a τ-approximation algorithm that relies on a two-commodity minimum-cost flow computation. Here, τ is the number of different server types. A data center has a large collection of machines but only a relatively small number of different server architectures. Moreover, in the optimization, one can assign servers with comparable energy consumption to the same class. Technically, both of our algorithms involve nontrivial flow modification procedures. In particular, given a fractional two-commodity flow, our algorithm executes advanced rounding and flow packing routines.

Flexibility-Based Energy and Demand Management in Data Centers: A Case Study for Cloud Computing

The power demand (kW) and energy consumption (kWh) of data centers were augmented drastically due to the increased communication and computation needs of IT services. Leveraging demand and energy management within data centers is a necessity. Thanks to the automated ICT infrastructure empowered by the IoT technology, such types of management are becoming more feasible than ever. In this paper, we look at management from two different perspectives: (1) minimization of the overall energy consumption and (2) reduction of peak power demand during demand-response periods. Both perspectives have a positive impact on total cost of ownership for data centers. We exhaustively reviewed the potential mechanisms in data centers that provided flexibilities together with flexible contracts such as green service level and supply-demand agreements. We extended state-of-the-art by introducing the methodological building blocks and foundations of management systems for the above mentioned two perspectives. We validated our results by conducting experiments on a lab-grade scale cloud computing data center at the premises of HPE in Milano. The obtained results support the theoretical model, by highlighting the excellent potential of flexible service level agreements in Green IT: 33% of overall energy savings and 50% of power demand reduction during demand-response periods in the case of data center federation.