Regression Cloud Models and Their Applications in Energy Consumption of Data Center

With the rise of cloud computing technology, the whole information technology industry has undergone dramatic changes. Large-scale computing and the growing demand for computing power from large data have made it impossible for traditional data center network topology construction methods and network layer operation control methods with good stability to meet the requirements of technological development. The topology of a data center network is usually characterized as an undirected graph, and graph algorithms are used to implement communication patterns in complex network structures. However, the routing algorithms proposed earlier for graphs cannot be applied to all topological graphs. Based on the comprehensive interactive trust evaluation network security model, this study analyzes the advantages and disadvantages of relevant algorithms and proposes a fault-tolerant routing algorithm suitable for all networks. The algorithm can dynamically call different algorithms according to the number of current nodes and links. From the simulation results, it can be found that the algorithm not only improves the accuracy and fault tolerance but also effectively reduces the loss of time and memory.

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“…DCN [24,25] refers to an infrastructure of data center network, which uses high-speed links to connect with switches and servers.…”

Section: Preliminariesmentioning

confidence: 99%

Fault-Tolerant Secure Routing Based on Trust Evaluation Model in Data Center Networks

Liu

Fan

et al. 2022

Security and Communication Networks

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“…To predict any application of the broad range of energy-consuming HPC applications, you can build models using the decision trees method; it automatically picks the best suitable model for the running workload. In modeling energy consumption, linear regression is the model of choice, and to acquire multi-component metric models, multivariate linear regression has been used [28,68]. Modeling of node-level energy consumption, based on usage of main node components measurements, does not consider external causes (e.g., cooling) into account and relies on assigning tasks to the devices.…”

Section: Hpc and Energy Efficiencymentioning

confidence: 99%

Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

et al. 2020

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Power-consuming entities such as high performance computing (HPC) sites and large data centers are growing with the advance in information technology. In business, HPC is used to enhance the product delivery time, reduce the production cost, and decrease the time it takes to develop a new product. Today’s high level of computing power from supercomputers comes at the expense of consuming large amounts of electric power. It is necessary to consider reducing the energy required by the computing systems and the resources needed to operate these computing systems to minimize the energy utilized by HPC entities. The database could improve system energy efficiency by sampling all the components’ power consumption at regular intervals and the information contained in a database. The information stored in the database will serve as input data for energy-efficiency optimization. More so, device workload information and different usage metrics are stored in the database. There has been strong momentum in the area of artificial intelligence (AI) as a tool for optimizing and processing automation by leveraging on already existing information. This paper discusses ideas for improving energy efficiency for HPC using AI.

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“…At present, the main MPC methods are Linear Regression Algorithm (LRA) [32], Time Series Prediction Algorithm (TSPA) [33], and Artificial Neural Network Algorithm (ANNA) [34]. Zhou et al analyzed LRA and nonlinear regression energy models based on performance counters and system utilization [32].…”

Section: Algorithms Of Model Predictive Controlmentioning

confidence: 99%

Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization

2017

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Sustainable facilities management (SFM) opens the door of opportunity for companies to evaluate the quality of resources and environment management at their facilities. It enables the principles of sustainable development. There is still inefficiency in quantitative research of integrating environmental factors, particularly multi-source data, to monitor and control complicated systems in buildings. The objective of this research is to develop an effective method to dynamically optimize energy efficiency in SFM plans and strategies. The research question is: can the integrated proactive method reduce energy consumption with dynamically adjustable controls? This paper proposes a coordinated proactive control method using dynamic time-series prediction (PCM-DTSP) for SFM, which optimizes system controls by integrating the prediction results and monitored environmental-data. The results show that, after optimization, the temperature fluctuations are reduced to 33.3%. The average temperature and maximum temperature are reduced by 8% and 13.1%, respectively. The instantaneous power consumption was reduced by 0.17 KW per hour for each cooling system unit. The PCM-DTSP method can significantly optimize energy efficiency, which paves the way for long-term comprehensive energy management. The contribution of the research lies in its optimized control of energy consumption, temperature stabilization, and improvement of environmental comfort solutions, which can be generalized to various types of buildings.

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Regression Cloud Models and Their Applications in Energy Consumption of Data Center

Cited by 4 publications

References 18 publications

Fault-Tolerant Secure Routing Based on Trust Evaluation Model in Data Center Networks

Fault-Tolerant Secure Routing Based on Trust Evaluation Model in Data Center Networks

Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization

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