Power Consumption Of Data Centers Research Articles

Local bandgap narrowing in the forming state of threshold switching materials

Threshold switching (TS) materials, such as amorphous chalcogenide, have received significant attention for their application in storage class memory and in-memory computing. These materials contribute to efficient data processing and reduced power consumption in data centers. The initial switching process after fabricating a TS device, known as “forming,” has a profound impact on its subsequent TS behavior. However, it remains unclear how TS materials undergo changes in their atomic and electronic structures during the forming process. Consequently, the key factors that govern TS behavior remain obscure, necessitating a deeper understanding of the underlying physics behind TS phenomena. In this Letter, we investigated the forming state of the TS material AlTeN by combining scanning internal photoemission microscopy (SIPM) and ab initio calculations. Thanks to nondestructive evaluation by SIPM measurements, we observed local bandgap narrowing of AlTeN after its forming process. This is an experimental demonstration showing the presence of nuclei of the conductive filament formed in its ON state. Moreover, we conducted an ab initio calculation to reveal the origin of bandgap narrowing. We applied strong electrothermal stresses to the AlTeN model by ab initio molecular dynamics simulation with high electronic and lattice temperatures. By quenching from the electrothermal stress conditions, we reproduced an experimentally observed forming state with a narrowed bandgap. Analysis of the electronic structures of the forming state revealed that the origin of bandgap narrowing is the generation of the valence band top and conduction band bottom stemming from the increased homopolar bonds.

OCL-MEC: An online CPU-core prediction based on load balancing framework for offloading resource management in mobile edge computing environment

Clients can increase or decrease the number of resources they use dynamically over time due to the elasticity of cloud resources. As a result, variations in resource demands and predefined VM sizes result in a lack of resource utiliation, load imbalances, and excessive power consumption. A framework of efficient resource management is proposed to address these issues, balancing the load accordingly and anticipating the resource utilization of the servers. By optimizing resource utilization and minimizing the number of active servers, this technique facilitates power savings. Under/overloaded servers reduce energy consumption, execution delay, and performance degradation through a resource prediction system that is deployed at the CPU. Moreover, OCL-MEC load-balancing and resource allocation algorithms are proposed to reduce data center network traffic and power consumption. Experiments on real-world workload datasets, namely Bitsbrain VM traces, are conducted to evaluate the proposed framework. Different performance metrics demonstrate the superiority of the proposed framework over state-of-the-art approaches. Power savings of up to 98 % can be achieved by the OCL-MEC framework using a decision tree load balancing model based on HMM prediction systems.

Exploring the Evolution Trend of China’s Digital Carbon Footprint: A Simulation Based on System Dynamics Approach

The rapid growth of the digital economy has heightened concerns over its environmental impacts, particularly in terms of carbon dioxide emissions. In contrast to previous studies that focus on the positive effects of digital technology on reducing carbon emissions, this paper provides a detailed analysis of the various factors that influence digital economy carbon emissions and their interrelationships, using the system dynamics method to simulate and predict China’s future digital economy carbon emission baseline from 2016 to 2046. Four different scenarios were established by adjusting parameters for the percentage of the digital economy, e-waste growth rate, and data center power consumption. The simulation results indicate the following: (1) The baseline scenario shows China’s digital economy carbon emissions peaking at 1.9045 billion tons in 2041 after an initial increase and subsequent decrease. (2) Single-policy simulations indicate that changing the digital economy’s scale and e-waste growth independently leads to peak emissions of 1.9205 billion tons and 1.5525 billion tons, respectively. Adjusting data center power consumption has a greater impact, increasing the peak emissions to 2.1675 billion tons, a 13.82% rise from the baseline. (3) Under the comprehensive regulation scenario, emissions peak in 2040 at 2.0813 billion tons, considering the interactions between the digital economy, data center power, and e-waste. Based on the findings, we recommend fostering innovation in the digital industry, enhancing the e-waste treatment process, strategically developing digital infrastructure, and exploring effective carbon reduction strategies for the digital economy aimed at supporting China in achieving its dual-carbon goals.

A two-stage stochastic collaborative planning approach for data centers and distribution network incorporating demand response and multivariate uncertainties

As a critical infrastructure to support the development of digital economy, the scale of data center (DC) power consumption has been growing continuously in recent years. The plug-in of a large number of DCs to the distribution network (DN) presents both opportunities and challenges for the secure, stable, and sustainable operation of the system. In order to address the planning issues arising from the integration of DCs into the DN, this paper proposes a two-stage (planning stage and operation stage) collaborative planning method for DCs and the DN while considering the source-demand uncertainty. Firstly, spatial-temporal demand response characteristic of DCs is explicitly elaborated, and based on the energy consumption model, a two-stage stochastic planning model considering conditional value at risk (CVaR) for DCs and the DN is constructed using probabilistic stochastic simulation, which obeys low-carbon and security, and satisfies the source-network-load constraints of the system. The siting and sizing for DCs and renewable generation, feeder expansion for the DN are determined to minimize the investment cost during the planning stage, while the power output for the devices considering uncertainties from load and renewable energy are optimized to minimize the operation cost during the operation stage. Secondly, the L-shaped decomposition algorithm is proposed to solve the two-stage stochastic optimization problem by reconstructing the problem into a master problem and subproblems, where the dual multiplies are calculated to generate feasibility cuts and optimality cuts to achieve iterative solving. Finally, numerical case studies on a 33-bus distribution system demonstrate that the participation of DCs in demand response can achieve a significant reduction in the investment and operation cost of the DN, and further promote the consumption of distributed renewable energy sources.

Energy and carbon-aware distributed machine learning tasks scheduling scheme for the multi-renewable energy-based edge-cloud continuum

As an increasing number of Distributed Machine Learning (DML) tasks are hosted on cloud platforms in the edge-cloud continuum, Data Centers (DCs) with massive data and computational requirements have become one of the world’s largest energy consumers, leading to significant carbon emissions. Reducing energy consumption and carbon emissions is an extremely crucial and challenging issue for the sustainable development of cloud service providers. While utilizing renewable energy can help reduce the carbon emissions of DCs, the intermittent and unstable nature still causes DCs to rely heavily on high-carbon brown energy. For the resource-intensive and delay-tolerant DML tasks, this paper introduces multi-renewable energy in the geo-distributed continuum to address this issue, the spatiotemporal complementarity maximizes the renewable energy utilization and compensates for time-dependent energy differences with geographic advantages. Additionally, considering the dynamic differences in carbon intensity and electricity prices across distributed DCs in the continuum, we propose an energy and carbon-aware algorithm called ECMR for scheduling heterogeneous virtual machine creation tasks of DML among multi-clouds in different time zones. It is demonstrated that compared with the baseline methods, the ECMR significantly reduces the total power consumption, energy cost, and carbon emission of data centers while maintaining an acceptable service quality. The utilization of renewable energy in data centers has been significantly improved to 90.8% by flexibly leveraging the spatiotemporal complementarity of multi-renewable energy. Compared with existing competing algorithms, the proposed method exhibits significant improvements with an achieved average response time of 12.6 ms, and a task failure rate of 1.25%.

TADRP: Toward Thermal-Aware Data Replica Placement in Data-Intensive Data Centers

With the mushrooming growth of data volumes, data replica placement plays a key role in promoting the energy efficiency and Quality-of-Service (QoS) of data-intensive data centers. The existing data placement strategies mainly focus on storage performance improvement or QoS enhancement in data centers, but ignore the indispensable factor -heat recirculation. To bridge this gap, we propose a thermal-aware data replica placement strategy called TADRP, aiming to improve cooling efficiency and minimize the total power consumption of data-intensive data centers. TADRP leverages an ant colony optimization (ACO) algorithm coupled with Laplacian probability distribution to find a quasi-optimal disk sequence (or Disk Sequence for short), which consists of disks selected from different rack servers to place data replicas. TADRP categorizes disks of Disk Sequence into active and inactive ones, by placing hot and cold replicas on active and inactive disks, respectively. We quantitatively evaluate TADRP in terms of cooling costs, total power consumption, number of power-state transitions, and execution time. We compare TADRP with four alternative solutions, namely, Random, Hadoop, SRS, and CDP-NSGAII. Experimental results show that TADRP can reduce the cooling costs and the total power of the existing solutions by 14.7% -61.7% and 19.2 %-55.1%, respectively, without undesirable I/O performance drops.

SafeCool: Safe and Energy-Efficient Cooling Management in Data Centers With Model-Based Reinforcement Learning

Optimizing the cooling system plays a central role for capping the data center power consumption. However, the performance of traditional cooling management strategies is not satisfactory due to the complexity of thermodynamic process. Recently, several works leveraged Reinforcement Learning (RL) to improve the energy efficiency of data center cooling system. While they demonstrated that it is possible to reduce the cooling power consumption via RL, there are still some key challenges that have to be addressed before field deployment, such as safe operation guarantee and sample complexity, etc. In this paper, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> , an actor-critic Model-Based Reinforcement Learning (MBRL) algorithm for data center cooling management. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> incorporates two system models, i.e., a transition model to predict the future system state and a risk model to estimate the negative effect of executing an action. The safety of proposed algorithm is ensured by Model Predictive Control (MPC) and risk-guided exploration. In addition, by employing the MBRL framework, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> achieves higher sample efficiency and accelerated convergence. Simulations using real-world workload trace reveal that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> saves up to 13.18% cooling power compared with state-of-the-art MBRL data center cooling solutions.

An idea to construct integrated energy systems of data center by combining CO2 heat pump and compressed CO2 energy storage

Aiming at the characteristics of high power consumption and abundant waste heat resources in data centers, the integrated energy systems of data center are constructed by combining CO2 heat pump and compressed CO2 energy storage. Considering different stages of compression and expansion in energy storage, System I and System II are proposed. The systems can continuously provide cooling to data center through the CO2 heat pump, and can also store the low-cost electricity so as to supply power to data center during the peak electricity consumption period. Meanwhile, hot water can also be provided by fully extracting the residual heat from the systems. Furthermore, to investigate system performances, the thermal and economic models are developed. On this basis, the thermal and economic performances for the two systems are analyzed in detail under design conditions. The results indicate that when providing cooling capacity of 5000 kW for data center, the Coefficient of Performance (COP) of heat pump is 7.22, and Round-Trip Efficiency (RTE) of System I and System II are 56.94 % and 55.85 % respectively. Systems I and System II can provide 879.26 tons and 1158.73 tons of domestic hot water, respectively. The total system capital cost is 234.35 × 104$ for System I, and 344.45 × 104$ for System II. Accordingly, the payback periods are 12.71 years and 11.19 years respectively. Furthermore, the effects of pressures on the system performances are also analyzed. The results show that every 0.5 MPa increase for heat pump outlet pressure, RTE of System II increases by 0.6 %, but the effect on System I is not significant.

Energy Efficient in Cloud Computing

Abstract: Today, both parallel and distributing computing have became ubiquitous in the forms of clouds and cyber physical systems, The size of cloud data center is growing rapidly. The larger the data center is, the more energy it consumes. Therefore, more and more researchers focus on energy efficient resource management techniques in the data center. In which manage the load factor and cpu utilization. we consider about cloud computing, its working, its problem definition, virtual machine requirements( executing VMs with similar execution time), objectives and future development of cloud computing. This has led to immense usage of energy, power consumption, usage of e-equipment’s and so on. To overcome this, efforts and techniques are reinforced to save energy, power consumption and the usage of equipment’s to save the planet. Energy efficiency is increasingly important for future ICT. To reduce the power consumption of cloud data centers we aim to reduce the efficient usage of power consumed by IT equipments. EEICC attainable by using eco-friendly computing resources.

Energy-Efficient Virtual Machine Migration Algorithm in Data Centers

The issue of data center power consumption is becoming increasingly serious. One of the most effective techniques for increasing resource utilization and lowering power consumption is virtual machine consolidation. This paper proposes a threshold-based energy-efficient and SLA-aware algorithm for virtual machine migration and consolidation. Based on statistics and analysis of historical virtual machine status, the algorithm dynamically obtains the upper CPU utilization threshold of the physical servers and identifies whether to perform the virtual machine migration, so as to reduce the number of active hosts to save energy. The experimental results demonstrate that the proposed algorithm has better comprehensive performance. When compared to the existing algorithm, our method can effectively decrease energy consumption and SLA violations.

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

Cost-sensitive Tensor-based Dual-stage Attention LSTM with Feature Selection for Data Center Server Power Forecasting

Power forecasting has a guiding effect on power-aware scheduling strategies to reduce unnecessary power consumption in data centers. Many metrics related to power consumption can be collected in physical servers, such as the status of CPU, memory, and other components. However, most existing methods empirically exploit a small number of metrics to forecast power consumption. To this end, this article uses feature selection based on causality to explore the metrics that strongly influence the power consumption of different tasks. Moreover, we propose a tensor-based dual-stage attention LSTM to forecast the non-linear and non-periodic power consumption. In the proposed model, a multi-way delay embedding transform is utilized to convert the time series into tensors along the temporal direction. The LSTM combines with the tensor technique and the attention mechanism to capture the temporal pattern effectively. In addition, we adopt the cost-sensitive loss function to optimize the specific power forecasting problem in data centers. The experimental results demonstrate that our method can achieve up to 1.4% to 4.3% forecasting accuracy improvement compared with the state-of-the-art models.

Towards Thermal-Aware Workload Distribution in Cloud Data Centers Based on Failure Models

Increasing workload conditions lead to a significant surge in power consumption and computing node failures in data centers. The existing workload distribution strategies focused on either thermal awareness or failure mitigation, overlooking the impact of node failures on the energy efficiency of cloud data centers. To address this issue, a new holistic model is built to characterize the impacts of workloads, computing and cooling costs, heat recirculation, and node failure on the energy efficiency of cloud data centers. Leveraging such a holistic model, we propose a novel thermal-aware workload distribution strategy called <i>HGSA</i> that takes node failure into accountand can improve the energy efficiency of cloud data centers. Our empirical findings confirm that (i) faulty nodes lead to a large rise in power consumption, and (ii) failure locations play a vital role in the power consumption of data centers. Experimental results unveil that HGSA is adroit at making near-optimal decisions in workload distribution strategies. In particular, HGSA cuts down the minimum inlet temperature by 5.2 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> -15 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , improves the maximum air temperature of a Computer Room Air Conditioner (CRAC) model by 4.2 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> -26.5 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , lowers the cooling cost by 15.4 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> -50 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> compared to the existing solutions. Furthermore, HGSA cuts back the total power consumption by 0.65 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> -78 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

An Analog EIC-PIC Receiver With Carrier Phase Recovery for Self-Homodyne Coherent DCIs

Coherent techniques, used for high-capacity optical links, conventionally use high-speed ADCs and digital signal processing, which make them power hungry. For short-reach coherent links such as data center interconnects, power consumption, form factor, cost, and latency have to be greatly reduced. Co-designed photonic and electronic integrated circuits, in the optical and electrical analog domains, respectively, can achieve these goals. In this brief, for the first time, we present integration of a silicon photonic integrated coherent receiver (ICR) front-end with an electronic carrier phase recovery chip, as a part of a complete coherent receiver solution. A phase shifter in the ICR (fabricated in a 220 nm silicon-on-insulator technology) receives feedback from the CPR chip, and the combination compensates for the time varying phase offset between the modulated signal and the unmodulated carrier in the closed loop configuration. In this proof-of-concept demonstration, we present experimental results obtained from the stand-alone ICR, and its system level integration with CPR chip, for QPSK signals. The technique can be extended to a higher-order modulation formats, such as 16-QAM, for capacity scaling. The proposed scheme is suitable for carrier-forwarded coherent links, such as polarization multiplexed carrier based self-homodyne links.

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.

Optimization of Data and Energy Migrations in Mini Data Centers for Carbon-Neutral Computing

Due to large-scale applications and services, cloud computing infrastructures are experiencing an ever-increasing demand for computing resources. At the same time, the overall power consumption of data centers has been rising beyond 1&#x0025; of worldwide electricity consumption. The usage of renewable energy in data centers contributes to decreasing their carbon footprint and overall electricity costs. Several green-energy-aware resource allocation approaches have been studied recently. None of them takes advantage of the joint migration of <i>jobs</i> and <i>energy</i> in green data centers to increase energy efficiency. This paper presents an optimization approach for energy-efficient resource allocation in mini data centers. The observed momentum around edge computing makes the design of geographically distributed mini data centers highly desirable. Our solution exploits both virtual machines (VMs) and energy migrations between green compute nodes in mini data centers. These nodes have energy harvesting, storage, and transport capabilities. They enable the migration of VMs and energy across different nodes. Compared to VM allocation alone, joint-optimization of VM and energy allocation reduces utility electricity consumption by up to 22&#x0025;. This reduction can reach up to 28.5&#x0025; for the same system when integrating less energy-efficient servers. The gains are demonstrated using simulation and a Mixed Integer Linear Programming formulation for the resource allocation problem. Furthermore, we show how our solution contributes to sustaining the energy consumption of old-generation and less efficient servers in mini data centers.

Solving the 0/1 Knapsack Problem Using Metaheuristic and Neural Networks for the Virtual Machine Placement Process in Cloud Computing Environment

Virtual machine placement (VMP) is carried out during virtual machine migration to choose the best physical computer to host the virtual machines. It is a crucial task in cloud computing. It directly affects data center performance, resource utilization, and power consumption, and it can help cloud providers save money on data center maintenance. To optimize various characteristics that affect data centers, VMs, and their runs, numerous VMP strategies have been developed in the cloud computing environment. This paper aims to compare the accuracy and efficiency of nine distinct strategies for treating the VMP as a knapsack problem. In the numerical analysis, we test out various conditions to determine how well the system works. We first illustrate the rate of convergence for algorithms, then the rate of execution time growth for a given number of virtual machines, and lastly the rate of development of CPU usage rate supplied by the nine methods throughout the three analyzed conditions. The obtained results reveal that the neural network algorithm performs better than the other eight approaches. The model performed well, as shown by its ability to provide near‐optimal solutions to test cases.

Energy saving strategy of cloud data computing based on convolutional neural network and policy gradient algorithm.

Cloud Data Computing (CDC) is conducive to precise energy-saving management of user data centers based on the real-time energy consumption monitoring of Information Technology equipment. This work aims to obtain the most suitable energy-saving strategies to achieve safe, intelligent, and visualized energy management. First, the theory of Convolutional Neural Network (CNN) is discussed. Besides, an intelligent energy-saving model based on CNN is designed to ameliorate the variable energy consumption, load, and power consumption of the CDC data center. Then, the core idea of the policy gradient (PG) algorithm is introduced. In addition, a CDC task scheduling model is designed based on the PG algorithm, aiming at the uncertainty and volatility of the CDC scheduling tasks. Finally, the performance of different neural network models in the training process is analyzed from the perspective of total energy consumption and load optimization of the CDC center. At the same time, simulation is performed on the CDC task scheduling model based on the PG algorithm to analyze the task scheduling demand. The results demonstrate that the energy consumption of the CNN algorithm in the CDC energy-saving model is better than that of the Elman algorithm and the ecoCloud algorithm. Besides, the CNN algorithm reduces the number of virtual machine migrations in the CDC energy-saving model by 9.30% compared with the Elman algorithm. The Deep Deterministic Policy Gradient (DDPG) algorithm performs the best in task scheduling of the cloud data center, and the average response time of the DDPG algorithm is 141. In contrast, the Deep Q Network algorithm performs poorly. This paper proves that Deep Reinforcement Learning (DRL) and neural networks can reduce the energy consumption of CDC and improve the completion time of CDC tasks, offering a research reference for CDC resource scheduling.

Combining looped heat pipe and thermoelectric generator module to pursue data center servers with possible power usage effectiveness less than 1

With the rapid increase in the power consumption of global data centers, it is becoming more and more important to reduce the power usage effectiveness (PUE) value, which is a key metric for evaluating the energy efficiencies of data centers. The previously proposed advanced thermal management methods can make PUE values very close to 1. To further reduce the PUE of the air-cooled data center, a novel looped heat pipe (LHP) and thermoelectric generator (TEG) coupled system based on a series–parallel scheme was first proposed. Attributed to the passive heat dissipation apparatus with extremely low thermal resistance and high-efficiency thermoelectric power generation devices as adopted in this design, the power output, especially the net power output, could be greatly improved compared to the power outputs achieved in previous research, and an electrical power of more than several watts was obtained while cooling a server CPU, which enable servers possibly with PUE values less than one. The results showed that, under the condition that the junction temperature was below 85 °C, by using nine 40 mm TEGs, the minimum PUE of 0.997 was achieved at a Reynolds number of 930 and a heat load of 155 W.

Workload and energy management of geo-distributed datacenters considering demand response programs

Datacenters usually consume huge energy and their workload can be adjusted dynamically, which makes them become good potential participants of demand response (DR) programs in the smart grid system. To overcome the drawbacks of centralization and enable fault-tolerance of a single datacenter, geographically distributed datacenters could be connected by a wide-area network to build up an integrated infrastructure, upon which multiple datacenter workloads could be balanced by taking advantage of space–time electricity price differences to reduce electricity costs. Particularly, when geo-distributed datacenters participate into DR programs, in order to obtain more economic benefits and to help achieve the load control objectives issued by the grid, it is necessary to consider a variety of external and internal conditions to optimize the workload and energy management strategy. Aiming at this problem, we propose an optimal power regulation scheduling method (OPRS) to simultaneously manage workload and energy consumption in geo-distributed datacenters, which comprehensively considers various factors including variation of electricity prices, operating costs, renewable energy constraints, and DR requirements. On the basis of the conventional computer room air conditioner system, additional devices are also incorporated into the architecture for datacenter cooling, including a solar-powered absorption cooling system and a chilled liquid storage system. Based on such framework, the OPRS method not only uses local renewable energy to reduce the energy consumption of the datacenter, but also can reduce the operating cost of the geo-distributed datacenters by leveraging the difference of electricity prices between time and space. Second, the method increases the frequency of uninterruptible power supply (UPS) usage and thus enhances the flexibility of datacenter power regulation. Furthermore, the OPRS method manages the workload by jointly considering task reassignment and delayed scheduling according to specific DR programs, thereby helping to maintain the stability of the regional power grid system. Experimental evaluation results show that, compared with other alternative strategies, the OPRS method can reduce the total operating cost by 19.52 %–66.96 %. Furthermore, the detailed analysis of the power regulation process illustrates that the OPRS method can also achieve better management of datacenter power consumption in the demand response stage.