Energy Consumption Of Infrastructure Research Articles

CoLocateMe: Aggregation-Based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users’ costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this article, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users’ costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 9.61% more energy and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users’ costs.

EpcAware: A Game-Based, Energy, Performance and Cost-Efficient Resource Management Technique for Multi-Access Edge Computing

Internet of Things (IoT) is producing an extraordinary volume of data daily, and it is possible that the data may become useless while on its way to the cloud, due to long distances. Fog/edge computing is a new model for analysing and acting on time-sensitive data, adjacent to where it is produced. Further, cloud services provided by large companies such as Google, can also be localised to improve response time and service agility. This is accomplished through deploying small-scale datacentres in various locations, where needed in proximity of users; and connected to a centralised cloud that establish a multi-access edge computing (MEC). The MEC setup involves three parties, i.e., service providers (IaaS), application providers (SaaS), network providers (NaaS); which might have different goals, therefore, making resource management difficult. Unlike existing literature, we consider resource management with respect to all parties; and suggest game-theoretic resource management techniques to minimise infrastructure energy consumption and costs while ensuring applications’ performance. Our empirical evaluation, using Google’s workload traces, suggests that our approach could reduce up to 11.95 percent energy consumption, and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 17.86% user costs with negligible loss in performance. Moreover, IaaS can reduce up to 20.27 percent energy bills and NaaS can increase their costs-savings up to 18.52 percent as compared to other methods.

A Scientometric Review of System Dynamics Applications in Construction Management Research

Construction management can be regarded as a complex and dynamic system. In recent years, system dynamics (SD) has been widely applied to solve the complex and dynamic problems in the construction management. However, there is a lack of a scientometric analysis to investigate SD applications in construction management from an objective perspective. To fill out this research gap, this study retrieved a total of 222 relevant articles from the Scopus database. Then, VOSviewer was employed to analyze the collected literature from five aspects (i.e., co-authorship, published journals, co-occurring keywords, article citations, and regions). Based on the analysis results, four mainstream research themes were identified and discussed, including “risk management”, “waste management”, “energy management”, and “construction productivity”. In addition, future research directions, such as “construction risk allocation in PPP projects”, “evaluating the economic feasibility of construction waste landfilling centers”, “identifying the variables affecting lighting infrastructure energy consumption”, and “assessing construction productivity for technology-intensive activities”, were proposed. The contribution of this study lies in that it helps both scholars and practitioners to solve the complex and dynamic problems in construction management.

Green Cloud Computing: Efficient Energy-Aware and Dynamic Resources Management in Data Centers

The uses of Cloud computing over the last years are constantly increasing since it has become a very important technology in the computing landscape. It provides to client decentralized services and a pay-as-you-go model for consuming resources. The growing need for the cloud services oblige the providers to adopt an enlarged sized data center infrastructure which runs thousands of hosts and servers to store and process data. As result, these large servers engender a lot of heat with visual carbon emission in the air, as well as important energy consumption and higher operating cost. This is why researches in energy economics continue to progress including energy saving techniques in servers, in the network, cooling, and renewable energies, etc. In this paper, we tackled the existing energy efficient methods in the green cloud computing fields and we put forward our green cloud solution for data center dynamic resource management. Our proposed approach aims to reduce the infrastructure energy consumption and maintain the required performances.

Quantification of CO2 emissions of macro-infrastructure in China with simplified life cycle assessment

The main policies of China have promoted the development of the urbanization and the industrialization. The rise of infrastructure energy consumption shows the process of modernization in our country; however, despite there are a lot of researches about the energy consumption in the field of construction, transportation and other infrastructure, in academia, there are less statistics about energy consumption for the infrastructure as a whole, and its proportion in total energy consumption in the whole country. Thus, this paper tries to characterize infrastructures of China, which include construction, transportation, energy, water supply and drainage, post and telecommunication system by establishing an estimation model of infrastructure system CO2 emissions through national official macro-level statistical data. We employ the simplified LCCO2A method to analyze the activities in each stage and calculate associated CO2 emissions throughout infrastructures’ whole life cycle in China from 1993 to 2012. The results show that CO2 emissions from the infrastructure have rapidly grown over the past decades. The total emissions of the standard coal were 888.16 million tons in 1993, up to about 7.76 billion tons in 2012. The operation stage emissions dominate over the infrastructure emissions, accounting for 80–90 % of the impact. In this paper, on the one hand, from the perspective of the overall environmental benefits, we provide a more comprehensive environmental assessment analysis method for the policy makers. On the other hand, the infrastructure construction of China has periodic characteristic. We analyze the specific features of the infrastructure CO2 emissions in different stages, and put forward emission-reduction measures in consideration of the current conditions, which reflects that the key of balancing the relationship between modernization and environment is adjusting emission reduction policy according to different stages to adapt to the “new normal”. In addition, the method development is also important for future quantifications of CO2 emissions of other sectors in China and beyond.

A novel cost based model for energy consumption in cloud computing.

Cloud data centers consume enormous amounts of electrical energy. To support green cloud computing, providers also need to minimize cloud infrastructure energy consumption while conducting the QoS. In this study, for cloud environments an energy consumption model is proposed for time-shared policy in virtualization layer. The cost and energy usage of time-shared policy were modeled in the CloudSim simulator based upon the results obtained from the real system and then proposed model was evaluated by different scenarios. In the proposed model, the cache interference costs were considered. These costs were based upon the size of data. The proposed model was implemented in the CloudSim simulator and the related simulation results indicate that the energy consumption may be considerable and that it can vary with different parameters such as the quantum parameter, data size, and the number of VMs on a host. Measured results validate the model and demonstrate that there is a tradeoff between energy consumption and QoS in the cloud environment. Also, measured results validate the model and demonstrate that there is a tradeoff between energy consumption and QoS in the cloud environment.

Novel resource allocation algorithms to performance and energy efficiency in cloud computing

The rapid growth in demand for computational power has led to a shift to the cloud computing model established by large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy. Cloud providers must ensure that their service delivery is flexible to meet various consumer requirements. However, to support green computing, cloud providers also need to minimize the cloud infrastructure energy consumption while conducting the service delivery. In this paper, for cloud environments, a novel QoS-aware VMs consolidation approach is proposed that adopts a method based on resource utilization history of virtual machines. Proposed algorithms have been implemented and evaluated using CloudSim simulator. Simulation results show improvement in QoS metrics and energy consumption as well as demonstrate that there is a trade-off between energy consumption and quality of service in the cloud environment.

Energy Consumption Effects of WiFi Off-Loading Access in 3G or LTE Public Wireless Networks

As 3G, HSPDA and already now LTE wireless networks become ever more pervasive, especially for wireless high data rate and Internet traffic (&gt;100 Mbps), increasing focus is given on ways to offload access by re-utilizing WiFi access points available in-doors (offices, homes), or installing such access points outdoors in/alongside high demand density public areas (hot spots, public areas, road traffic lanes, etc..). In view of the relative much higher WiFi access node power consumption and much smaller coverage compatible with interference reduction, the WiFi off-loading access may have a significant negative impact on energy consumption and emissions per user. The paper builds on earlier extensive work on the modeling of 3G or LTE wireless infrastructure energy consumption on an incremental basis per new user. It addresses the questions of the best mix between LTE cellular base stations and WiFi off-load access nodes from the energy/emissions perspective. Detailed sub-system model and design improvements are carried out on a continuous basis in collaboration with industry.

Energy Consumption Effects of WiFi Off-Loading Access in 3G/LTE Public Wireless Networks

As 3G, HSPDA and already now LTE wireless networks become ever more pervasive, especially for wireless high data rate data and Internet traffic (>100 Mbps), increasing focus is given on ways to offload access by re-utilizing WiFi access points available in-doors (offices, homes), or installing such access points in/alongside high density of demand public areas (hot spots, public areas, road traffic lanes, etc. In view of the relative much higher WiFi access node power consumption and much smaller coverage compatible with interference reduction, the WiFi off-loading access may have a significant negative impact on energy consumption and emissions per user. Building on earlier extensive work on the modeling of 3G/LTE wireless infrastructure energy consumption on an incremental basis, this paper addresses the questions of the best mix between LTE cellular base stations and WiFi off-load access nodes from the energy/emissions perspective. Detailed sub-system model and design improvements are carried out on a continuous basis in collaboration with industry.

Optimization of Enclosed Aisle Data Centers Using Bypass Recirculation

The work presented in this paper describes a simplified thermodynamic model that can be used for exploring optimization possibilities in air-cooled data centers. The model is used to evaluate parametrically the total energy consumption of the data center cooling infrastructure for data centers that utilize aisle containment. The analysis highlights the importance of reducing the total power required for moving the air within the computer room air conditioners (CRACs), the plenum, and the servers, rather than focusing primarily or exclusively on reducing the refrigeration system’s power consumption. In addition, the benefits of introducing a bypass recirculation branch in enclosed aisle configurations are shown. The analysis shows a potential for as much as a 60% savings in cooling infrastructure energy consumption by utilizing an optimized enclosed aisle configuration with bypass recirculation, instead of a traditional enclosed aisle in which all the data center exhaust is forced to flow through the CRACs. Furthermore, computational fluid dynamics is used to evaluate practical arrangements for implementing bypass recirculation in raised floor data centers. A configuration where bypass tiles, with controllable low-lift fans, are placed close to the discharge of CRACs results in increased mixing and is shown to be a suitable method for providing nearly thermally uniform conditions to the inlet of the servers in an enclosed cold aisle. Other configurations of bypass implementation are also discussed and explored.

Energy Optimization of Air-Cooled Data Centers

Abstract The work presented in this paper describes a simplified thermodynamic model that can be used for exploring optimization possibilities in air-cooled data centers. The model has been used to identify optimal, energy-efficient designs, operating scenarios, and operating parameters such as flow rates and air supply temperatures. The results of this analysis highlight the important features that need to be considered when optimizing the operation of air-cooled data centers, especially the trade-off between low air supply temperature and increased air flow rate. The model was shown to be especially valuable in defining the optimal operating strategies for enclosed aisle configurations with fixed and variable server flows, and to elucidate the deleterious effect of temperature nonuniformity at the inlet of the racks on the data center cooling infrastructure power consumption. The analysis shows a potential for as much as an ∼58% savings in cooling infrastructure energy consumption by utilizing an optimized enclosed aisle configuration with bypass recirculation, instead of a traditional enclosed aisle, where all the data center exhaust is forced to flow through the computer room air conditioners. The analysis of open-aisle data centers shows that as the temperature at the inlet of the racks becomes more nonuniform, optimal operation tends toward lower recirculation and higher power consumption; again, stressing the importance of providing as uniform a temperature to the racks as possible. It is also revealed that servers with a modest temperature rise (∼10°C) have a wider latitude for cooling infrastructure optimization than those with a high temperature rise (≥20°C), which tend to consume less cooling power when the aisles are enclosed.