Energy Consumption Of Data Centers Research Articles

Virtual Machine Placement in Cloud Computing: Challenges, Research Gaps, and Future

Cloud computing provides various types of services to users. The goal of virtual machine placement (VMP) is to map the best physical machine to a virtual machine. With the help of Virtual Machine Placement, we can reduce cost, maximize resource utilization, reduced energy consumption of data centers in cloud environments. The focus of Virtual Machine Placement is to saving of power, quality of service. In this paper, we have reviewed various placements techniques used in cloud computing. At last, we have also studied various challenges for virtual machine placement in cloud computing. The main motive of various types of Virtual Machine Placement algorithms have to reduced energy consumption and minimize cost by maximizing utilization of various resources in the cloud platform. For further study, the researcher should focus on these challenges for the best virtual machine placement in a cloud environment. In this paper, we critically examine the techniques, challenges, and research gaps in virtual placements in cotext with Cloud Computing. Cloud computing, placement of virtual machines becomes major problems. For finding the solution to the problem we can use the various virtual machine placement algorithms. The main motive is to reduce consumption of energy, maximum resource utilization, minimizing cost factors used for virtual to the physical machine mapping in the cloud environment. For selecting the best algorithm various optimization methods are used. With these different optimization methods, we can analyze different algorithms. There is a great scope of improvement in existing systems of virtual placements to make them more energy-efficient, more reliable, and fault-tolerant. Redundancy in cloud downloading can be made more intelligent and minimized for duplicate data while downloading and uploading.

BHyPreC: A Novel Bi-LSTM Based Hybrid Recurrent Neural Network Model to Predict the CPU Workload of Cloud Virtual Machine

With the advancement of cloud computing technologies, there is an ever-increasing demand for the maximum utilization of cloud resources. It increases the computing power consumption of the cloud’s systems. Consolidation of cloud’s Virtual Machines (VMs) provides a pragmatic approach to reduce the energy consumption of cloud Data Centers (DC). Effective VM consolidation and VM migration without breaching Service Level Agreement (SLA) can be attained by taking proactive decisions based on cloud’s future workload prediction. Effective task scheduling, another major issue of cloud computing also relies on accurate forecasting of resource usage. Cloud workload traces exhibit both periodic and non-periodic patterns with the sudden peak of load. As a result, it is very challenging for the prediction models to precisely forecast future workload. This prompted us to propose a hybrid Recurrent Neural Network (RNN) based prediction model named BHyPreC. BHyPreC architecture includes Bidirectional Long Short-Term Memory (Bi-LSTM) on top of the stacked Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). Here, BHyPreC is used to predict future CPU usage workload of cloud’s VM. Our proposed model enhances the non-linear data analysis capability of Bi-LSTM, LSTM, and GRU models separately and demonstrates better accuracy compared to other statistical models. The effect of variation of historical window size and training-testing data size on these models is observed. The experimental result shows that our model gives higher accuracy and performs better in comparison to Autoregressive Integrated Moving Average (ARIMA), LSTM, GRU, and Bi-LSTM model for both short-term ahead and long-term ahead prediction.

The Electricity- and CO2-Saving Potentials Offered by Regulation of European Video-Streaming Services

Massive increases in Internet data traffic over the last years have led to rapidly rising electricity demand and CO2 emissions, giving rise to environmental externalities and network congestion costs. One particular concern is the rise in data traffic generated by video-streaming services. We analyze the electricity-saving potential related to video streaming in Europe from 2020 to 2030. To this end, three trend scenarios (Business-as-usual, Gray, and Green) are considered and modeled bottom-up, taking specific energy consumption (and trends) of data transmission networks, end-use devices, and data centers into account. Using these scenarios, we examine in more detail the approximate energy-saving impact that regulatory interventions and technical standards can have on the electricity consumption of end-users, network operators, and data centers. The model results reveal that regulatory intervention can have a significant impact on energy consumption and CO2 emissions. As technical regulation carries the risk of stymieing innovation and dynamic efficiency, we propose economic regulation in terms of a mandatory transit fee as a long-term solution.

A Review of Data Centers Energy Consumption and Reliability Modeling

Enhancing the efficiency and the reliability of the data center are the technical challenges for maintaining the quality of services for the end-users in the data center operation. The energy consumption models of the data center components are pivotal for ensuring the optimal design of the internal facilities and limiting the energy consumption of the data center. The reliability modeling of the data center is also important since the end-user’s satisfaction depends on the availability of the data center services. In this review, the state-of-the-art and the research gaps of data center energy consumption and reliability modeling are identified, which could be beneficial for future research on data center design, planning, and operation. The energy consumption models of the data center components in major load sections i.e., information technology (IT), internal power conditioning system (IPCS), and cooling load section are systematically reviewed and classified, which reveals the advantages and disadvantages of the models for different applications. Based on this analysis and related findings it is concluded that the availability of the model parameters and variables are more important than the accuracy, and the energy consumption models are often necessary for data center reliability studies. Additionally, the lack of research on the IPCS consumption modeling is identified, while the IPCS power losses could cause reliability issues and should be considered with importance for designing the data center. The absence of a review on data center reliability analysis is identified that leads this paper to review the data center reliability assessment aspects, which is needed for ensuring the adaptation of new technologies and equipment in the data center. The state-of-the-art of the reliability indices, reliability models, and methodologies are systematically reviewed in this paper for the first time, where the methodologies are divided into two groups i.e., analytical and simulation-based approaches. There is a lack of research on the data center cooling section reliability analysis and the data center components’ failure data, which are identified as research gaps. In addition, the dependency of different load sections for reliability analysis of the data center is also included that shows the service reliability of the data center is impacted by the IPCS and the cooling section.

Energy efficiency in cloud data centres using parallel hybrid Jaya algorithm

PurposeData centres evolve constantly in size, complexity and power consumption. Energy-efficient scheduling in a cloud data centre is a critical and challenging research problem. It becomes essential to minimize the overall operational costs as well as environmental impact and to guarantee the service-level agreements for the services provided by the cloud data centres. Resource scheduling in cloud data centres is NP-hard and often requires substantial computational resources.Design/methodology/approachTo overcome these problems, the authors propose a novel model that leads to nominal operational cost and energy consumption in cloud data centres. The authors propose an effective approach, parallel hybrid Jaya algorithm, that performs parallel processing of Jaya algorithm and genetic algorithm using multi-threading and shared memory for interchanging the information to enhance convergence premature rate and global exploration.FindingsExperimental results reveal that the proposed approach reduces the power consumption in cloud data centres up to 38% and premature convergence rate up to 60% compared to other algorithms.Originality/valueExperimental results reveals that our proposed approach reduces the power consumption in cloud data centres up to 38% and premature convergence rate up to 60% compared to other algorithms.

An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

The high energy consumption of cloud data centers is one of the key issues restricting the future development of the cloud computing industry. For two-tier virtualized data centers in which containers are deployed on VMs, this paper conducts an in-depth study of this problem from the aspect of resource management. First, the working model of two-tier virtualized data center is defined. By mapping tasks to containers and using VMs to isolate different jobs, this model improves the resource utilization of hosts while ensuring the isolation and security of jobs. Then, an energy-aware host resource management framework is constructed, which includes two algorithms. The initial static placement is a two-tier scheduling algorithm, including the load balancing alternate placement and the two-sided matching methods to complete the placement of containers to VMs and VMs to hosts respectively. The runtime dynamic consolidation algorithm takes the initial placement scheme as input, and utilizes the dynamic consolidation method to use the least active hosts to meet the real-time resource requirements of containers. Finally, simulation experiments compared with related algorithms are conducted using real workload traces. The results show that the proposed two algorithms have better performance in host resource utilization, number of active hosts, number of container migrations and SLA metric. And the entire framework achieves an energy-saving effect of 13.8% at least.

A Deep Reinforcement Learning-Based Power Resource Management for Fuel Cell Powered Data Centers

With the increase of data storage demands, the energy consumption of data centers is also increasing. Energy saving and use of power resources are two key problems to be solved. In this paper, we introduce the fuel cells as the energy supply and study power resource use in data center power grids. By considering the limited load following of fuel cells and power budget fragmentation phenomenon, we transform the main two objectives into the optimization of workload distribution problem and use a deep reinforcement learning-based method to solve it. The evaluations with real-world traces demonstrate the better performance of this work over state-of-art approaches.

Role of green data center in human resources development model

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

Investigating the Sustainability of Energy Production and Consumption in Data Centers with the Design and Morphology Approach of the Building

Investigating the Sustainability of Energy Production and Consumption in Data Centers with the Design and Morphology Approach of the Building

Security-aware dynamic VM consolidation

The explosive growth of cloud usage encourages several challenges, especially high energy consumption of Cloud Data Centers (CDCs), new security risks to Virtual Machines (VMs) resulting from co-residency with other risky VMs on the same Physical Machine (PM), and the Quality of Service (QoS) degradation due to sharing resources. Many recent studies have proposed Dynamic VM Consolidation (DVMC) to save energy with minimum degradation of the QoS. However, due to the lack of reliable security measurements and consolidating VMs without any awareness of their security risk degrees, the overall security risk of the CDC may be increased. To tackle these challenges, this study presents a Security-aware DVMC (SDVMC) that consists of a Security Monitoring Module (SMM) and a SDVMC module. The SMM utilizes a three-dimensional security assessment model, while in the SDVMC module we propose a novel VM placement algorithm called Minimum Risk Increase (MRI) with Risk Increase Threshold (RITH). The proposed MRI with RITH VM placement algorithm selects the host that leads to minimum risk increase to the overall security risk while maintaining the risk increase for each VM does not exceed the value of the proposed RITH constraint; which is set according to the aims of the cloud provider. Simulation results show that using our approach with RITH 0.8 results in security improvement, overall risk was decreased by 2% to 5%, without negative impact on energy consumption or QoS. Moreover, using our approach with RITH less than 0.8 enables the tradeoff between energy consumption and the overall security risk. The maximum overall risk decrease ranged from 10% up to 40%, according to the intensive of the communication overhead between the VMs, while the used energy in its maximum was less than half if we used a non-power-ware VM allocation policy.

Power Control Framework for Green Data Centers

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

Multi-objective Energy Aware Scheduling of Deadline Constrained Workflows in Clouds using Hybrid Approach

Cloud computing is undoubtedly one of the most significant advances in the domain of information technology. It facilitates elastic and on-demand provisioning of high performance computing capabilities employing pay-per-use model that has snowballed its adoption by scientists and engineers over the past few years. They often exploit workflows to represent their massive applications. Workflow scheduling in cloud has been devoted considerable investigation by researchers owing to its NP-complete nature of problem. Most of the previous studies targeted optimization of schedule length and execution cost within given deadlines/budget restrictions, or both. However, enormous energy consumption in the cloud data centers is not only negatively impacting the environment but also resulting in increased operational costs and thus cannot be ignored. Efficient scheduling strategies can significantly lessen the energy usage while complying with the user’s Quality of Service limitations. This research study proposes a Hybrid Approach for Energy aware scheduling of Deadline constrained workflows (HAED) using Intelligent Water Drops algorithm and Genetic Algorithm, which provides non-dominated solutions to the user. In particular, it focuses on multiple objectives i.e. reduction of schedule length, execution cost and energy usage within deadlines specified by the user. Its performance has been assessed on four scientific workflows from diverse domains using hypervolume and set coverage. The results achieved with the simulations demonstrate that the solutions produced by HAED are of better quality in terms of accuracy and diversity than non-dominated sorting genetic algorithm and hybrid particle swarm optimization.

Rough‐set and machine learning‐based approach for optimised virtual machine utilisation in cloud computing

To meet rising demands for computing resources, information technology service providers need to select cloud-based services for their vitality and elasticity. Enormous numbers of data centres are designed to meet customer needs. Burning up energy by data centre is very high with the large-scale deployment of cloud data centres. Virtual machine consolidation strategy implementation reduces the data centre energy consumption and guarantees service level agreements. This study proposes a machine learning-based method in cloud computing for the automated use of virtual machines. Machine learning-based virtual machine selection approach integrates the migration control mechanism that enhances selection strategy efficiency. The experiment is performed with various real machine workload circumstances to provide proof and effectiveness of the proposed method. The exploratory outcome shows that the proposed approach streamlines the utilisation of the virtual machine and diminishes the consumption of energy and improves infringement of service level agreements to accomplish better performance.

SDN-cloud: a power aware resource management system for efficient energy optimization

PurposeIn modern cloud services, resource provisioning and allocation are significant for assigning the available resources in efficient way. Resource management in cloud becomes challenging due to high energy consumption at data center (DC), virtual machine (VM) migration, high operational cost and overhead on DC.Design/methodology/approachIn this paper, the authors proposed software-defined networking (SDN)-enabled cloud for resource management to reduce energy consumption in DC. SDN-cloud comprises four phases: (1) user authentication, (2) service-level agreement (SLA) constraints, (3) cloud interceder and (4) SDN-controller.FindingsResource management is significant for reducing power consumption in CDs that is based on scheduling, VM placement, with Quality of Service (QoS) requirements.Research limitations/implicationsThe main goal is to utilize the resources energy effectively for reducing power consumption in cloud environment. This method effectively increases the user service rate and reduces the unnecessary migration process.Originality/valueAs a result, the authors show a significant reduction in energy consumption by 20 KWh as well as over 60% power consumption in the presence of 500 VMs. In future, the authors have planned to concentrate the issues on resource failure and also SLA violation rate with respect to number of resources will be decreased.

An Ancillary Services Model for Data Centers and Power Systems

Enormous energy consumption of data centers has a major impact on power systems by significantly increasing the electrical load. Due to the increase in electrical load, power systems are facing demand and supply miss-management problems. Therefore, power systems require efficient and intelligent ancillary services to maintain robustness, reliability, and stability. Data centers can provide the computational capabilities to manage power systems; however, data centers consume a tremendous amount of energy, and energy price accounts for a significant portion of their operational cost. Power system jobs will make this situation even more critical for data centers. In our work, we seek an Ancillary Services Model (ASM) to service data centers and power systems. In ASM, we find an optimal job scheduling technique for executing power systems’ jobs on data centers in terms of low power consumption, reduced makespan, and fewer preempted jobs. The power systems’ jobs include Optimal Power Flow (OPF) calculation, transmission line importance index, and bus importance index. Moreover, a Service Level Agreement (SLA) between data centers and power systems is shown to provide mutual benefits.

A Cooperative Coevolution Genetic Programming Hyper-Heuristics Approach for On-Line Resource Allocation in Container-Based Clouds

Containers are lightweight and provide the potential to reduce more energy consumption of data centers than Virtual Machines (VMs) in container-based clouds. On-line resource allocation is the most common operation in clouds. However, the on-line <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Resource Allocation in Container-based clouds (RAC)</i> is new and challenging because of its two-level architecture, i.e., the allocations of containers to VMs and the allocation of VMs to physical machines. These two allocations interact with each other, and hence cannot be made separately. Since on-line container allocation requires a real-time response, most current allocation techniques rely on heuristics (e.g., First Fit and Best Fit), which do not consider the comprehensive information such as workload patterns and VM types. As a result, resources are not used efficiently and the energy consumption is not sufficiently optimized. We first propose a novel model of the on-line <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAC</i> problem with the consideration of VM overheads, VM types and an affinity constraint. Then, we design a Cooperative Coevolution Genetic Programming (CCGP) hyper-heuristic approach to solve the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAC</i> problem, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CCGP-RAC</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CCGP-RAC</i> can learn the workload patterns and VM types from historical workload traces and generate allocation rules. The experiments show significant improvement in energy consumption compared to the state-of-the-art algorithms.

Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System

Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the task execution for heterogeneous tasks respectively based on the data locality feature, which also indicate the relationship among the tasks, data blocks and servers. We propose a heterogeneous task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote task execution and data migration, which could improve the data locality and reduce task execution time. We conduct extensive simulations and the experimental results show that our algorithm has better performance than the traditional methods, and data migration actually works to reduce th overall task execution time. The algorithm also shows acceptable fairness for the heterogeneous tasks.

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.

High-temperature-resistant silicon-polymer hybrid modulator operating at up to 200\u2009Gbit\u2009s\u22121 for energy-efficient datacentres and harsh-environment applications

To reduce the ever-increasing energy consumption in datacenters, one of the effective approaches is to increase the ambient temperature, thus lowering the energy consumed in the cooling systems. However, this entails more stringent requirements for the reliability and durability of the optoelectronic components. Herein, we fabricate and demonstrate silicon-polymer hybrid modulators which support ultra-fast single-lane data rates up to 200 gigabits per second, and meanwhile feature excellent reliability with an exceptional signal fidelity retained at extremely-high ambient temperatures up to 110 °C and even after long-term exposure to high temperatures. This is achieved by taking advantage of the high electro-optic (EO) activities (in-device n3r33 = 1021 pm V−1), low dielectric constant, low propagation loss (α, 0.22 dB mm−1), and ultra-high glass transition temperature (Tg, 172 °C) of the developed side-chain EO polymers. The presented modulator simultaneously fulfils the requirements of bandwidth, EO efficiency, and thermal stability for EO modulators. It could provide ultra-fast and reliable interconnects for energy-hungry and harsh-environment applications such as datacentres, 5G/B5G, autonomous driving, and aviation systems, effectively addressing the energy consumption issue for the next-generation optical communication.

Energy efficiency optimization of an integrated heat pipe cooling system in data center based on genetic algorithm

The energy consumption of data centers has become an increasing concern in the whole world. Actually, the cooling system consumes the most energy among the auxiliary facilities. An integrated heat pipe cooling system has been proposed to reduce the energy consumption of cooling system by using natural cooling sources in our previous work. However, it still has huge energy-saving potential in the operating process. In this paper, the heat transfer model and energy consumption model of the integrated heat pipe cooling system was established to describe the relationship between operating parameters and energy efficiency. Based on genetic algorithm, energy efficiency optimization operation strategy was calculated at different load rates and outdoor ambient temperature. An experiment was carried out to verify the accuracy of the optimization algorithm. Compared with the previous work, the energy efficiency ratio of the integrated heat pipe cooling system can be improved by 2–3 times in natural cooling model and integrated cooling mode after energy efficiency optimization. The switching rules of the system’s operating mode was studied, and analyzed the energy-saving effects of application in different climate cities.