Multivariate regressive deep stochastic artificial learning for energy and cost efficient 6G communication

High energy consumption in data center has become a key problem, and the Carbon (CO $$_2$$ ) emissions from data center have serious impacts on environment. To improve traditional performance-energy model and minimize energy consumption in cloud data center, an energy-efficient and QoS-aware (EEQoS) model is developed for virtual resources consolidation. The EEQoS model is designed to investigate the trade-off between energy and QoS. The user’s satisfaction degree and Euclidean distance of resources are also respectively applied to the QoS model and power model. In addition, this EEQoS model is combined with the particle swarm optimization algorithm by setting the power consumption per QoS value as the objective function to consolidate virtual resources in data center. Compared with traditional power model, the proposed model reduces response time by 27.2%, cost by 31.4%, SLA violations by 40.5% and improves throughput by 13.5% on average while increasing less than 3.8% energy consumption during the whole test period.

In the presence of latency sensitive geo-distributed applications, users require fast service for their queries. Cloud computing provides physical servers from its data center in order to process user requests. The cloud data center consumes a huge amount of energy due to lack of management of the data center servers as the container-based service consolidation is a nontrivial task. Since the containers require less resource footprint, consolidating it in servers might make resource availability sparse. In order to reduce the energy consumption of the cloud data center, we have proposed a green container-based consolidation of the services so that the maximum number of servers can be put into idle mode without affecting the application quality of experience. The service consolidation problem has been formulated as an optimization problem considering minimization of total energy consumption of the data center as the objective, and an algorithm named Energy Aware Service consolidation using baYesian optimization (EASY) has been proposed to solve the optimization. We have evaluated the EASY algorithm in simulation using python. The experimental results have shown that EASY improves the total energy consumption of the data centers. This improvement comes at the cost of a small increase of service response time as there exists a trade-off between energy consumption and service response time.

Cloud computing is the combination of grid computing, distributed computing and utility computing. Cloud computing provides various types of services (servers and storage facility) in the on demand basis. The main goal of the cloud computing is to preserve and organize the very huge data center or data forms. The data forms are composed of thousands of servers that absorb the giant (ample) amount of electricity in the word of energy. The decreasing the energy consumption in datacenter is the major challenge in cloud computing now a day. This research article is going to address the problem of high energy consumption at datacenter. Concentrate on virtual machine scheduling in cloud datacenter with Dynamic Voltage Frequency Scaling (DVFS) approach. We have combined shortest job first and Round Robin algorithms with Vibrant Quantum. This combination of algorithm is considered as shortest round vibrant queue (SRVQ) algorithm. SRVQ reduces the waiting time of the scheduling process and minimize the starvation. The DVFS and SRVQ worked together and produced fruitful results in the final experiments. This work reduced the server’s energy consumption in the cloud data center. In the final results, our proposed framework exhibits 45% of energy efficiency compare to other previously proposed algorithms. 33% of QoS performance were enhanced by our framework.

The growth of energy consumption in data centers is becoming one of the significant problems all over the world. The power usage of IT devices and air-conditioning (AC) units accounts for up to three-quarters of total energy consumption in typical data centers. This study discusses the effectiveness of using shutter-controlled air flow management to control cold air flow into the servers. It can impact the power consumption of both, the servers and the AC units. A raised floor data center has two types of aisles - cold and hot. In most cases, excessive volumes of cold air are pumped into the servers using cold aisles. We are proposing the utilization of a shutter to control the air flow and reduce wastage while preventing damage due to increased CPU temperature. The shutter is intended to control the air flow and maintain the temperature appropriately according to the workload of the servers. We evaluated the proposed system for controlling the flow of air into the servers. The results show that cold air volumes can be reduced by up to 92% while maintaining appropriate control of CPU temperature.

Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers

Research and Technologies for next-generation high-temperature data centers – State-of-the-arts and future perspectives

Data centers are recognized as one of the most important aspects of the fourth industrial revolution since conventional data centers are inefficient and have dependency on high energy consumption, in which the cooling is responsible for 40% of the usage. Therefore, this research proposes the immersion cooling method to solving the high energy consumption of data centers by cooling its component using two types of dielectric fluids. Four stages of experimental methods are used, such as fluid types, cooling effectiveness, optimization, and durability. Furthermore, benchmark software is used to measure the CPU maximum work with the temperature data performed for 24 h. The results of this study show that the immersion cooling reduces 13°C lower temperature than the conventional cooling method which means it saves more energy consumption in the data center. The most optimum variable used to decrease the temperature is 1.5 lpm of flow rate and 800 rpm of fan rotation. Furthermore, the cooling performance of the dielectric fluids shows that the mineral oil (MO) is better than the virgin coconut oil (VCO). In durability experiment, there are no components damage after five months immersed in the fluid.

Using Model Predictive Control in Data Centers for Dynamic Server Provisioning

The 2007 US EPA report to Congress (US EPA, 2007) on the state of energy consumption in data centers brought to light the true energy inefficiencies built into today’s data centers. Marquez et al. (2008) conducted an initial analysis on the productivity of a Pacific Northwest National Lab computer using The Green Grid’s Data Center Energy Productivity metric (The Green Grid, 2008). Their study highlights how the Top500 ranking of computers disguises the serious energy inefficiency of today’s High Performance Computing data centers. In the rapidly expanding Cloud Computing space, the race will be won by the providers that deliver the lowest cost of computing — such cost is heavily influenced by the operational costs incurred by data centers. As a means to address the urgent need to lower the cost of computing, solution providers have been intensely focusing on real-time monitoring, visualization, and control/management of data centers. The monitoring aspect involves the widespread use of networks of sensors that are used to monitor key data center environmental variables such as temperature, relative humidity, air flow rate, pressure, and energy consumption. Such data is then used to visualize and analyze data center problem areas (e.g., hotspots), which is then followed by control/management actions designed to alleviate such problem areas. The authors have been researching the operational benefits of a network of sensors tied in to a software package that uses the data to visualize, analyze, and control/manage the data center cooling system and IT Equipment for maximum operational efficiency. The research is being conducted in a corporate production data center that is networked in to the authors’ company’s global network of data centers. Results will be presented that highlight the operational benefits that are realizable through real-time monitoring and visualization.

The data center Energy Consumption (EC) and the user Quality of Service (QoS) are two important influencing factors, which the cloud service provider must balance in the process of providing virtual cloud services resources (VCSR). For the VCSR over-deployment phenomenon happening in cloud computing Infrastructure as a Service (IAAS), a user-oriented deployment constraint of VCSR is proposed, and the definition of user's Service Level Agreements (SLA) and the energy consumption of data center are also suggested. On the basis of that, the model of dynamic migration of the virtual nodes is given, solved by simulated annealing algorithm. In the experiments, different strategies are compared using CloudSim. The results show that the suggested strategy has advantages on balancing the energy consumption of data center and user's QoS.

Large data centers consume large amounts of electricity. Estimating the energy consumption in a data center can be of great importance to data centers administrators in order to know the energy-consuming tasks and take actions for reducing the total energy consumption. Smart workflow mechanisms can be built to reduce the energy consumption of data centers significantly. In this paper, we are investigating the factors that affect the energy consumption of scientific applications in data centers. We also use eight machine learning methods to estimate the energy consumption of multi-threaded scientific applications. Extensive computational results on a computer with 20 cores show that the CPU usage is the most important parameter in the power consumed by an application. However, better results can be obtained when the CPU utilization is combined with other parameters. We generate various regression models that predict the energy consumption of an application with an average accuracy of 99%. Simpler models with one and two parameters can achieve comparable accuracy with more complex models. We also compare various machine learning methods for their ability to obtain accurate predictions using as few parameters as possible.

The energy consumption of Data Centers (DCs) is a very important figure for the telecommunications operators, not only in terms of cost, but also in terms of operational reliability. A relation between the energy consumption and the weather conditions would indicate that weather forecast models could be used for predicting energy consumption of DCs. A reliable forecast would result in a more efficient management of the available energy and would make it easier to take advantage of the modern types of power-grid based on renewable energy resources. In this ,paper, we exploit the capabilities provided by the FIESTA-IoT platform in order to investigate the correlation between the weather conditions and the energy consumption in DCs. Then, by using multi-variable linear regression process, we model this correlation between the energy consumption and the dominant weather conditions parameters in order to effectively forecast the energy consumption based on the weather forecast. We have validated our results through live measurements from the RealDC testbed. Results from our proposed approach indicate that forecasting of energy consumption based on weather conditions could help not only DC operators in managing their cooling systems and power usage, but also electricity companies in optimizing their power distribution systems.

Data centers are serious, energy-hungry infrastructures that can run large scale Internet based services. Energy ingesting representations are essential in designing and improving energy-efficient operations to reduce excessive energy consumption in data centers. This paper presents a survey on Energy efficiency in data centers, importance of energy efficiency. It also describes the increasing demands for data center in worldwide and the reasons for data centers energy inefficient? In this paper we define the challenges for implementing changes in data centers and explain why and how the energy requirements of data centers are growing. After that we compare the German data center market at international level and we see the energy consumption of data centers and servers in Germany from 2010 -2016.

With the application of cloud computing services in more and more fields, it will undertake more computing tasks and storage tasks. The problem of high energy consumption in data centers will become more serious. Virtualization technology is very important in cloud computing, which can improve the utilization rate of resources. At the same time, it has flexibility in resource scheduling and can integrate multiple virtual machines to achieve power efficiency. Using online virtual machine migration technology for energy-saving planning in cloud environment is a hot research topic in academic circles. The scheduling strategy proposed in this paper can reduce the server downtime and the number of server hosts, so as to achieve the maximum use of resources. The main research work of this paper includes the following aspects: Firstly, the energy consumption in the process of virtual machine energy-saving design is modeled, and the relationship between energy consumption and resource usage under different load conditions is analyzed, and the problems are abstracted, e.g., packaging box problem. Secondly, this paper uses genetic algorithm to solve the problem of high energy consumption. Finally, based on the target allocation scheme of virtual machines obtained by the above method, the migration problem of virtual machines is abstracted as the problem of finding the maximum weighted independent set of graphs. And a greedy algorithm is designed to solve this problem. In this paper, CloudSim simulation platform is used to verify the effectiveness of the proposed algorithm. Experiments show that the proposed algorithm can reduce data energy consumption and avoid frequent migration of virtual machines.

Energy consumption in data center is currently the main focus of many large-scale enterprises and cloud service providers. Dynamic virtual machine (VM) consolidation technologies are widely used to improve resource utilization of data centers and reduce energy. They try to identify the poorly utilized physical hosts and make the most of the resources. Then idle hosts can be switched to sleep or active mode, meanwhile considering the real-time fluctuation of service workload. In this paper, we propose a Reinforcement Learning (RL) based Virtual Machine Consolidation (RL-VMC) framework with an application to the cloud data center operation. The RL-VMC method uses an agent corresponding to a VM planner to interact with the environment, which encapsulates the data center running state, and learns the optimal policy to determine the migration mapping from VMs to physical hosts. Specifically, we adopt the on-policy method State-Action-Reward-State-Action (SARSA) in RL-VMC, which learns from experience to get the optimal VMs migration strategy and manage the host power mode as well. Additionally, we use the PBRS technology to speed up the convergence of the RL-VMC. Experimental results show that the RL-VMC method can adapt to the dynamic workload maintaining an improved balance between energy consumption and Service Level Agreements (SLA).