Power Consumption Of Data Centers Research Articles

Assessing anthropogenic heat flux of public cloud data centers: current and future trends.

Global average temperature had been significantly increasing during the past century, mainly due to the growing rates of greenhouse gas (GHG) emissions, leading to a global warming problem. Many research works indicated other causes of this problem, such as the anthropogenic heat flux (AHF). Cloud computing (CC) data centers (DCs), for example, perform massive computational tasks for end users, leading to emit huge amounts of waste heat towards the surrounding (local) atmosphere in the form of AHF. Out of the total power consumption of a public cloud DC, nearly 10% is wasted in the form of heat. In this paper, we quantitatively and qualitatively analyze the current state of AHF emissions of the top three cloud service providers (i.e., Google, Azure and Amazon) according to their average energy consumption and the global distribution of their DCs. In this study, we found that Microsoft Azure DCs emit the highest amounts of AHF, followed by Amazon and Google, respectively. We also found that Europe is the most negatively affected by AHF of public DCs, due to its small area relative to other continents and the large number of cloud DCs within. Accordingly, we present mean estimations of continental AHF density per square meter. Following our results, we found that the top three clouds (with waste heat at a rate of 1,720.512 MW) contribute an average of more than 2.8% out of averaged continental AHF emissions. Using this percentage, we provide future trends estimations of AHF densities in the period [2020–2100]. In one of the presented scenarios, our estimations predict that by 2100, AHF of public clouds DCs will reach 0.01 Wm−2.

A Synthetic Approach for Datacenter Power Consumption Regulation towards Specific Targets in Smart Grid Environment

With the large-scale grid connection of renewable energy sources, the frequency stability problem of the power system has become increasingly prominent. At the same time, the development of cloud computing and its applications has attracted people’s attention to the high energy consumption characteristics of datacenters. Therefore, it was proposed to use the characteristics of the high power consumption and high flexibility of datacenters to respond to the demand response signal of the smart grid to maintain the stability of the power system. Specifically, this paper establishes a synthetic model that integrates multiple methods to precisely control and regulate the power consumption of the datacenter while minimizing the total adjustment cost. First, according to the overall characteristics of the datacenter, the power consumption models of servers and cooling systems were established. Secondly, by controlling the temperature, different kinds of energy storage devices, load characteristics and server characteristics, the working process of various regulation methods and the corresponding adjustment cost models were obtained. Then, the cost and penalty of each power regulation method were incorporated. Finally, the proposed dynamic synthetic approach was used to achieve the goal of accurately adjusting the power consumption of the datacenter with least adjustment cost. Through comparative analysis of evaluation experiment results, it can be observed that the proposed approach can better regulate the power consumption of the datacenter with lower adjustment cost than other alternative methods.

Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning

The problem of high power consumption in data centers is becoming more and more prominent. In order to improve the energy efficiency of data centers, cooperatively optimizing the energy of IT systems and cooling systems has become an effective way. In this paper, a model-free deep reinforcement learning (DRL)-based joint optimization method MAD3C is developed to overcome the high-dimensional state and action space problems of the data center energy optimization. A hybrid AC-DDPG cooperative multi-agent framework is devised for the improvement of the cooperation between the IT and cooling systems for further energy efficiency improvement. In the framework, a scheduling baseline comparison method is presented to enhance the stability of the framework. Meanwhile, an adaptive score is designed for the architecture in consideration of multi-dimensional resources and resource utilization improvement. Experiments show that our proposed approach can effectively reduce energy for data centers through the cooperative optimization while guaranteeing training stability and improving resource utilization.

A 3-Phase Resonant Switched-Capacitor Converter for Data Center 48-V Rack Power Distribution

Since the power consumption of data centers keeps increasing, a 48-V rack power distribution system replaces the conventional 12-V power bus to reduce the power delivery IR losses. Meanwhile, the 48 V needs conversion into 1 V or lower at the point-of-load for microprocessors. A 48V-to-12V DC-DC converter can serve as a first stage between the huge gap of 48V-to-1V, in a two-stage voltage regulator module (VRM). Thus, this paper presents a 3-phase resonant switched-capacitor ( 3Φ-ReSC) converter as the first stage of the VRM. Different from the reported solutions, 3-phase resonant operation reduces the current stress across each small-size GaN switch, and thus improves the power conversion efficiency. In addition, we also present the theoretical analysis and design procedures for the 3Φ-ReSC converter. We demonstrated the proposed 3Φ-ReSC converter with a 98% peak power efficiency at an output current of about 1 A, with a maximum output current of 10 A.

A Robust Optimization Technique for Energy Cost Minimization of Cloud Data Centers

Power consumption cost constitutes significant portion of a data center's total operational cost. Variations in parameters, such as power demand, electricity price, and renewable power generation effects the data center's power consumption cost. Therefore, in this paper, a smart power management system based on a robust energy cost optimization algorithm is proposed for the data center. The designed robust optimization algorithm coordinates data center workload, battery bank, diesel generators, renewable power, and trade electricity price in real-time and day-ahead power market to reduce expected energy consumption cost. The uncertain parameters, such as data center workload and renewable power are computed using forecasting algorithms. The optimization algorithm is used to limit unbalance power purchase from real-time power market due to uncertainty of real-time electricity price. The algorithm formulation is obtained using mixed-integer linear programming. Moreover, a model to calculate prices to be used in service level agreements with data centers' clients for on-demand cloud services is designed considering operational cost of the data center. Simulations are performed on actual data center workload, weather parameters, and electricity price. The results have shown that the proposed methodology is an effective tool to minimize data center operational cost.

Energy Efficient Placement of Workloads in Composable Data Center Networks

This paper studies the energy efficiency of composable data center (DC) infrastructures over network topologies. Using a mixed integer linear programming (MILP) model, we compare the performance of disaggregation at rack-scale and pod-scale over selected electrical, optical and hybrid network topologies relative to a traditional DC. Relative to a pod-scale DC, the results show that physical disaggregation at rack-scale is sufficient for optimal efficiency when the optical network topology is adopted, and resource components are allocated in a suitable manner. The optical network topology also enables optimal energy efficiency in composable DCs. The paper also studies logical disaggregation of traditional DC servers over an optical network topology. Relative to physical disaggregation at rack-scale, logical disaggregation of server resources within each rack enables marginal fall in the total DC power consumption (TDPC) due to improved resource demands placement. Hence, an adaptable composable infrastructure that can support both in memory (access) latency sensitive and insensitive workloads is enabled. We also conduct a study of the adoption of micro-service architecture in both traditional and composable DCs. Our results show that increasing the modularity of workloads improves the energy efficiency in traditional DCs, but disproportionate utilization of DC resources persists. A combination of disaggregation and micro-services achieved up to 23% reduction in the TDPC of the traditional DC by enabling optimal resources utilization and energy efficiencies. Finally, we propose a heuristic for energy efficient placement of workloads in composable DCs which replicates the trends produced by the MILP model formulated in this paper.

A multi-objective Monarch Butterfly Algorithm for virtual machine placement in cloud computing

The growing demand for cloud computing adoption presents more challenges for researchers to make cloud computing more efficient and affordable for infrastructure providers and end users. The management of cloud computing involves investing in IT infrastructure in the first phase and investing in energy, maintenance and space costs in the second phase. However, energy costs account for a large portion of cloud management costs, and saving energy consumption can significantly reduce overall cloud management costs. Server consolidation is a strategy to improve data center energy efficiency and resource utilization. Virtual machine (VM) placement is considered one of the main problems with VM consolidation. The VM placement problem aims to reduce the number of active physical machines in data center to reduce data center power consumption and maintenance costs. However, the waste of data center resources has a significant impact on the energy efficiency of the data center, so it should be considered in the VM placement strategy. This paper proposes a new method based on the Monarch Butterfly Optimization algorithm (MBO) called MBO-VM for new virtual machine placement, designed to maximize packaging efficiency and reduce the number of active physical servers. CloudSim toolkit is used to test the efficiency of the proposed MBO-VM approach under real cloud workloads as well as synthetic workloads. Simulation results show that MBO-VM produces significantly better results compared with known VM placement techniques. The proposed MBO-VM can reduce the number of active servers more effectively and maximize the packaging efficiency.

Power-Efficient Software-Defined Data Center Network

The energy consumed by data centers has been growing rapidly in recent years. Among all the major contributors to the power consumption of entire data centers, data center network (DCN) can account for up to 20% of the total power consumption. In this article, we first devise a power-efficient software-defined DCN (PESD-DCN) framework, which can achieve desirable power efficiency, avoid potential link congestion, and reduce frequent device state transition. Then, we formulate the optimization problem of maximizing the radio full-utilized devices to all devices. To solve it, we propose correlation-aware flow routing (CFR) algorithm, which leverages correlation-aware flow consolidation (CFC) technique to improve energy efficiency, avoid the potential link congestion, and reduce frequent device state transition. Moreover, to further improve the DCN energy efficiency, we propose flow rerouting, link rate adaptation, and device sleeping (FLD) algorithm. Finally, simulation results demonstrate that PESD-DCN can achieve a good performance. More specifically, in comparison to the other baseline algorithms, PESD-DCN can achieve up to 79.19% energy efficiency, 67.1% decrease in switch state transition (SST), and 55.4% decrease in link state transition (LST).

A discrete chaotic multi-objective SCA-ALO optimization algorithm for an optimal virtual machine placement in cloud data center

Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically disbursed different venders. Power efficiency in cloud data centers (DCs) has become an important topic in recent years as more and larger DCs have been established and the electricity cost has become a major expense for operating them. Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of DCs. Virtual machine (VM) assignment is the key in server consolidation. In the past few years, many methods to VM assignment have been proposed, but existing VM assignment approaches to the VM assignment problem consider the energy consumption by physical machines (PM). In current paper a new approach is proposed that using a combination of the sine cosine algorithm (SCA) and ant lion optimizer (ALO) as discrete multi-objective and chaotic functions for optimal VM assignment. First objective of our proposed model is minimizing the power consumption in cloud DCs by balancing the number of active PMs. Second objective is reducing the resources wastage by using optimal VM assignment on PMs in cloud DCs. Reducing SLA levels was another purpose of this research. By using the method, the number of increase of migration of VMs to PMs is prevented. In this paper, several performance metrics such as resource wastage, power consumption, overall memory utilization, overall CPU utilization, overall storage space, and overall bandwidth, a number of active PMs, a number of shutdowns, a number of migrations, and SLA are used. Ultimately, the results obtained from the proposed algorithm were compared with those of the algorithms used in this regard, including First Fit (FF), VMPACS and MGGA.

NASCon: Network-Aware Server Consolidation for server-centric wireless datacenters

Servers in datacenter are underutilized due to over-provisioning, which contributes heavily toward high-power consumption of the datacenters. Server consolidation can drastically reduce power consumption of datacenters by reducing the number of active servers. However, server consolidation alone can adversely affect network performance. Recently, wireless datacenters using millimeter-wave communications have been proposed and shown to drastically reduce power consumption of networking equipment. Therefore, server-consolidation strategies should leverage novel datacenter network-architectures to minimize power consumption with minimal impact on performance. We present network-aware bandwidth-constrained server consolidation algorithm called Network-Aware Server Consolidation (NASCon), for wireless datacenters that can reduce the power consumption up to 37%. In wireless datacenter, NASCon server consolidation consumes comparable power compared to consolidation optimized with exhaustive search, while being much less computationally expensive and also improving the network performance. However, due to arrival of new-tasks and completion of existing-tasks, consolidated utilization profile of servers change, which may have adverse effect on overall power consumption over time. To overcome this, NASCon algorithm needs to be executed periodically. We propose a mechanism to find optimal inter-consolidation time and derived a mathematical model for the same. The model reduces the computing overheads for determining the optimal inter-consolidation time enabling real-time implementation.

Power efficient virtual machine placement in cloud data centers with a discrete and chaotic hybrid optimization algorithm

Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services through the Internet to the users and organizations at all levels. In Cloud computing, the users pay for the services, which means a usage-based payment system is used in this technology. Using virtualization technology in computation resources enables the appropriate utilization of resources in cloud computing. One of the most significant challenging issues in virtualization technology is the placement of optimal virtual machines on physical machines in cloud data centers. The placement of virtual machines comprises a process wherein virtual machines are mapped onto physical machines in cloud data centers. Optimal deployment leads to the reduction in power consumption, optimal use of resources, traffic reduction in data centers, costs reduction, and efficiency enhancement of the data center in the cloud. The present article proposed a new approach using a combination of the Sine–Cosine Algorithm and Salp Swarm Algorithm as discrete multi-objective and chaotic functions for optimal virtual machine placement. The first goal of the proposed algorithm was to reduce the power consumption in cloud data centers by condensing the number of active physical machines. The second goal was to reduce the waste of resources and manage it by optimally virtual machine placement on physical machines in cloud data centers. The third objective was to minimize and reduce Service Level Agreement among the active physical machines in cloud data centers. The proposed method prevent the increase in the migration of virtual machines onto physical machines. Ultimately, the results obtained from the proposed algorithm were compared with those of previous akin algorithms in the literature, including First Fit, Virtual Machine Placement Ant Colony System, and Modified Best Fit Decreasing. The proposed scheme is tested using Amazon EC2 Instances and the result indicated that the proposed algorithm performs better than the existing algorithms for various performance metrics.

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

Summary As the share of variable renewable energy (VRE) grows in the electric grid, so does the risk of curtailment. While energy storage and hydrogen production have been proposed as solutions to the curtailment problem, they often pose technological and economic challenges. Here, we analyze the potential of data center load migration for mitigating curtailment and greenhouse gas (GHG) emissions. Using historical hourly electricity generation, curtailment, and typical data center server utilization data, we simulate the effect of migrating data center workloads from the fossil-fuel-heavy PJM to the renewable-heavy CAISO. The results show that load migration within the existing data center capacity during the curtailment hours in CAISO has the potential to reduce 113–239 KtCO2e per year of GHG emissions and absorb up to 62% of the total curtailment with negative abatement costs in 2019. Our study demonstrates the overlooked role that data centers can play for VRE integration and GHG emissions mitigation.

Fast energy estimation framework for long-running applications

The computation power in data center facilities is increasing significantly. This brings with it an increase of power consumption in data centers. Techniques such as power budgeting or resource management are used in data centers to increase energy efficiency. These techniques require to know beforehand the energy consumption throughout a full profiling of the applications. This is not feasible in scenarios with long-running applications that have long execution times. To tackle this problem we present a fast energy estimation framework for long-running applications. The framework is able to estimate the dynamic CPU and memory energy of the application without the need to perform a complete execution. For that purpose, we leverage the concept of application signature. The application signature is a reduced version, in terms of execution time, of the original application. Our fast energy estimation framework is validated with a set of long-running applications and obtains RMS values of 11.4% and 12.8% for the CPU and memory energy estimation errors, respectively. We define the concept of Compression Ratio as an indicator of the acceleration of the energy estimation process. Our framework is able to obtain Compression Ratio values in the range of 10.1 to 191.2.

Study of a Lab-Scale Organic Rankine Cycle for the Ultra-Low-Temperature Waste Heat Recovery Associated With Data Centers

AbstractThe ongoing growth in data center rack power density leads to an increased capability for waste heat recovery. Recent studies revealed the organic Rankine cycle (ORC) as a viable means for data center waste heat recovery since the ORC uses waste heat to generate on-site, low-cost electricity, which can produce economic benefits by reducing the overall data center power consumption. This paper describes the first experimental and theoretical study of a lab-scale ORC designed for ultralow grade (40–85 °C) waste heat conditions typical of a data center server rack, and it outlines the implementation of a similar ORC system for a data center. The experimental results show thermal efficiencies ranging from 1.9% at 43 °C to 4.6% at 81 °C. The largest contributors to ORC exergy destruction are the evaporator and condenser due to large fluid temperature differences in the heat exchangers. The average isentropic efficiency of the expander is 70%. A second-law analysis estimates a reduction of 4–8% in data center power requirements when ORC power is fed back into the servers at a waste heat temperature of 90 °C. The data from the lab-scale experiment, when complemented by the thermodynamic model, provide the necessary first step toward advancing this type of waste heat recovery for data centers (DCs).

Virtual CPU Scaling for Efficient Server Power Consumption in Cloud Data Center

Cloud computing is a fast-growing technology used by major corporations these days because of the flexibility framework it provides to consumers. Cloud technology requires large data centers consisting of multiple IT equipment and servers. One main problem with these data centers is the vast amount of power consumed during servers operation. This reduces financial benefit and increases the need to produce more energy to cover the needs of operating the cloud infrastructure. This paper proposes an approach for managing the virtual central processing unit (vCPU) of a virtual machine to improve server power efficiency. A framework is used to study the proposed approach while processing different types of workloads widely found in most general-purpose cloud computing applications. Results indicate an improvement in server power saving.

Power consumption model based on feature selection and deep learning in cloud computing scenarios

High power consumption of cloud data centres is a crucial challenge in modern cloud computing. To comply with the conceptions of green computing, power consumption prediction of the computing cluster has a major role to play in these energy conservation efforts. However, due to complexity and heterogeneity in cloud computing scenarios, it is difficult to accurately predict the power consumption using conventional approaches. To this end, this study presents a power consumption model based on feature selection and deep learning to powerfully cope with low energy efficiency. Different from other methods focusing on only a few performance attributes, the proposed method takes into account up to 12 energy-related features and introduces deep neural network architecture, aiming at making full use of massive data to train model completely. In particular, this approach is composed of three main phases including (i) performance monitoring and energy-related feature acquisition, (ii) essential feature selection, and (iii) model establishment and optimisation. Representative results of comprehensive experiments, in terms of the relative error, reveal that the proposed power consumption model can undoubtedly achieve state-of-the-art predictive capability when compared with other models in most cases.

ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets

Modern datacenters need to tackle efficiently the increasing demand for computing resources while minimizing energy usage and monetary costs. Power market operators have recently introduced emerging demand-response programs, in which electricity consumers regulate their power usage following provider requests to reduce monetary costs. Among different programs, regulation service (RS) reserves are particularly promising for datacenters due to the high credit gain possibilities and datacenters' flexibility in regulating their power consumption. Therefore, it is essential to develop bidding strategies for datacenters to participate in emerging power markets together with power management policies that are aware of power market requirements at runtime. In this paper we propose ECOGreen, a holistic strategy to jointly optimize the datacenter RS problem and virtual machine (VM) allocation that satisfies the hour-ahead power market constraints in the presence of electrical energy storage (EES) and renewable energy. We first find the best power and reserve bidding values as well as the number of active servers in a fast analytical way that works well in practice. Then, we present an online adaptive policy that modulates datacenter power consumption by controlling VMs CPU resource limits and efficiently utilizing demand-side EES and renewable power, while guaranteeing quality-of-service (QoS) constraints. Our results demonstrate that ECOGreen can provide 76 percent of the datacenter power consumption on average as reserves to the market, due to largely operating on renewable sources and EES. This translates into ECOGreen saving up to 71 percent electricity costs when compared to other state-of-the-art datacenter electricity cost minimization techniques that participate in the power market.

On the classification and quantification of server consolidation overheads

Virtualization technology is important for servers that compose cloud data centers. The current trend is to consolidate servers to manage them easily and reduce hardware and power consumption in data centers. However, performance degradation is inherent to virtualization technology and is caused by the hypervisor and overhead due to the consolidation of several virtual servers inside a physical server. Several ways exist to virtualize a server; these methods are based mainly on virtual machines and containers. In this paper, we propose a general method to estimate the value of the consolidation overhead classes, regardless of the virtualization platform, server characteristics and workload type. We conducted several experiments in different scenarios to illustrate the usefulness of the proposed method. The results show the applicability of the proposed method and indicate that these inherent overheads are not negligible in many cases depending on, first, the type of hypervisor and, second, the hardware resources features of the physical server.

RETRACTED ARTICLE: Dynamic resource allocation with optimized task scheduling and improved power management in cloud computing

Cloud computing is one among the emerging platforms in business, IT enterprise and mobile computing applications. Resources like Software, CPU, Memory and I/O devices etc. are utilized and charged as per the usage, instead of buying it. A Proper and efficient resource allocation in this dynamic cloud environment becomes the challenging task due to drastic increment in cloud usage. Various promising technologies have been developed to improve the efficiency of resource allocation process. But still there is some incompetency in terms of task scheduling and power consumption, when the system gets overloaded. So an energy efficient task scheduling algorithm is required to improve the efficiency of resource allocation process. In this paper an improved task scheduling and an optimal power minimization approach is proposed for efficient dynamic resource allocation process. Using prediction mechanism and dynamic resource table updating algorithm, efficiency of resource allocation in terms of task completion and response time is achieved. This framework brings an efficient result in terms of power reduction since it reduces the power consumption in data centers. The proposed approach gives accurate values for updating resource table. An efficient resource allocation is achieved by an improved task scheduling technique and reduced power consumption approach. The Simulation result gives 8% better results when comparing to other existing methods.

VMS Mean and FFD VM Allocation Algorithm for Cloud Datacenter

The datacenter power consumption is increasing speedily, because of the scalable and dynamic provisioning of resources to the remote consumers. Not efficiently utilizing the resource of host machine is the main reason for increase in power consumption. By efficiently placing the number of virtual machine in fewer active host and shutdown the inactive host could reduce the consumption of power in datacenter. In this paper VMs Mean and FFD Bin Packing Virtual Machine Placement Algorithm is proposed.FFD preprocess VM’s and Host’s by sorting all the VM’s by MIPS and Host’s by their power. To place the VM’s in host machine VMs Mean method is used which finds the lowest CPU utilizing VM. The main objective is to increase the host machine utilization by efficiently placing the VM’s into few active Host’s and also reduced the power consumption. To test this algorithm CloudSim Toolkit is been used. The parameter such as EC, SLAV, PDM, ESV, VM migration and SLATAH is used as a metric to evaluate the Algorithm efficiency.