Data Center Management Research Articles

Taxonomy of optimization algorithms combined with CNN for optimal placement of virtual machines within physical machines in data centers

Energy management in datacenters is a major challenge today due to the environmental and economic impact of increasing energy consumption. Efficient placement of virtual machines in physical machines within modern datacenters is crucial for their effective management. In this context, five algorithms named CNN-GA, CNN-greedy, CNN-ABC, CNN-ACO and CNN-PSO, have been developed to minimize hosts’ power consumption and ensure service quality with relatively low response times. We propose a comparative approach between the developed algorithms and other existing methods for virtual machine placement. The algorithms use optimization algorithms combined with Convolutional Neural Networks to build predictive models of virtual machine placement. The models were evaluated based on their accuracy and complexity to select the optimal solution. The necessary data is collected using the CloudSim Plus simulator, and the prediction results were used to allocate virtual machines according to the predictions of the models. The main objective of this research is to optimize the management of Information Technology resources within datacenters. This is achieved by seeking a virtual machine placement policy that minimizes hosts’ power consumption and ensures an appropriate level of service for users' needs. It considers the imperatives of sustainability, performance, and availability by reducing energy consumption and response times. We studied six scenarios under specific constraints to determine the best model for virtual machines’ placement. This approach aims to address current challenges in energy management and operational efficiency.

Leveraging survival analysis in cost-aware deepnet for efficient hard drive failure prediction

AbstractMany real-world datasets, such as those used for failure and anomaly detection, are severely imbalanced, with a relatively small number of failed instances compared to the number of normal instances. To address these issues, this paper leverages the Backblaze hard disk drives (HDDs) data and makes several contributions to hard drive failure prediction. This research explores 1D convolutional neural networks (CNN) to utilize the sequential nature of hard drive sensor data. The performance of 1D CNN models is compared to traditional machine learning (ML) algorithms, such as the synthetic minority over-sampling technique (SMOTE) and weighted logistic regression (WLR), demonstrating superior results, suggesting the potential effectiveness of the proposed approaches. In addition to these efforts, this paper aims to provide a comprehensive understanding of hard drive longevity and the critical factors contributing to their eventual failure through survival analysis. The 1D CNN models employ weighted binary cross-entropy (WCE) loss and modified focal loss (MFL) functions to manage class imbalanced issues commonly observed in hard drive data. The findings suggest that 1D CNN models outperform traditional ML models, with regularization techniques like dropout and early stopping proving effective in controlling overfitting. Notably, the 1D CNN model with WCE loss demonstrated the best overall performance with a $$G_{mean}$$ G mean of 0.692, successfully maximizing the FDR without increasing the FAR. In parallel, the research also employs Cox regression to identify key SMART parameters influencing drive failure. The high concordance index (c-index) of the Cox model (0.958) adds confidence to the insights derived. The research thus sets a solid groundwork for data center management strategies, with a future focus on practical implementation and evaluation of these findings.

Energy efficient resource management in data centers using imitation-based optimization

Cloud computing is the paradigm for delivering streaming content, office applications, software functions, computing power, storage, and more as services over the Internet. It offers elasticity and scalability to the service consumer and profit to the provider. The success of such a paradigm has resulted in a constant increase in the providers’ infrastructure, most notably data centers. Data centers are energy-intensive installations that require power for the operation of the hardware and networking devices and their cooling. To serve cloud computing needs, the data center organizes work as virtual machines placed on physical servers. The policy chosen for the placement of virtual machines over servers is critical for managing the data center resources, and the variability of workloads needs to be considered. Inefficient placement leads to resource waste, excessive power consumption, and increased communication costs. In the present work, we address the virtual machine placement problem and propose an Imitation-Based Optimization (IBO) method inspired by human imitation for dynamic placement. To understand the implications of the proposed approach, we present a comparative analysis with state-of-the-art methods. The results show that, with the proposed IBO, the energy consumption decreases at an average of 7%, 10%, 11%, 28%, 17%, and 35% compared to Hybrid meta-heuristic, Extended particle swarm optimization, particle swarm optimization, Genetic Algorithm, Integer Linear Programming, and Hybrid Best-Fit, respectively. With growing workloads, the proposed approach can achieve monthly cost savings of €201.4 euro and CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CO}_2$$\\end{document} Savings of 460.92 lbs CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {CO}_2$$\\end{document}/month.

Enhancing Healthcare Information Systems in Ethiopian Hospitals: Exploring Challenges and Prospects of a Cloud-based Model for Smart and Sustainable Information Services

Hospitals are the primary hubs for healthcare service providers in Ethiopia; however, hospitals face significant challenges in adopting digital health information systems solutions due to disparate, non-interoperable systems and limited access. Information technology, especially via cloud computing, is crucial in healthcare for efficient data management, secure storage, real-time access to critical information, seamless provider communication, enhanced collaboration, and scalable IT infrastructure. This study investigated the challenges to standardizing smart and green healthcare information services and proposed a cloud-based model for overcoming them. We conducted a mixed-methods study in 11 public hospitals, employing quantitative and qualitative approaches with diverse stakeholders (N = 103). The data was collected through surveys, interviews, and technical observations by purposive quota sampling with the Raosoft platform and analyzed using IBM SPSS. Findings revealed several shortcomings in existing information systems, including limited storage, scalability, and security; impaired data sharing and collaboration; accessibility issues; no interoperability; ownership ambiguity; unreliable data recovery; environmental concerns; affordability challenges; and inadequate policy enforcement. Notably, hospitals lacked a centralized data management system, cloud-enabled systems for remote access, and modern data recovery strategies. Despite these challenges, 90.3% of respondents expressed interest in adopting cloud-enabled data recovery systems. However, infrastructure limitations, inadequate cloud computing/IT knowledge, lack of top management support, digital illiteracy, limited innovation, and data security concerns were identified as challenges to cloud adoption. The study further identified three existing healthcare information systems: paper-based methods, electronic medical catalog systems, and district health information systems2. Limitations of the paper-based method include error-proneness, significant cost, data fragmentation, and restricted remote access. Growing hospital congestion and carbon footprint highlighted the need for sustainable solutions. Based on these findings, we proposed a cloud-based model tailored to the Ethiopian context. This six-layered model, delivered as a Software-as-a-Service within a community cloud deployment, aims to improve healthcare services through instant access, unified data management, and evidence-based medical practices. The model demonstrates high acceptability and potential for improving healthcare delivery, and implementation recommendations are suggested based on the proposed model.

Technology modification, development, and demonstrations for future spaceflight medical systems at NASA

Throughout the history of human spaceflight, spacefarers have experienced and reported the occurrence of medical conditions, including various illnesses and injuries. Therefore, future spaceflight missions to the Moon and Mars will require the capabilities necessary for maintaining the health of these new space travelers. Mass, power, and volume available in the space vehicles used for these missions will be severely constrained. The ability to resupply or evacuate to Earth will be limited or non-existent, and ground-based support will no longer be immediate due to communication latencies and blackouts. These vehicle and mission constraints will necessitate healthcare be provided from an efficiently planned medical system. To provide the necessary care, these medical systems will need to include at a minimum, several different types of medical devices, consumable resources, centralized data management, procedural guidance, and decision support technologies. Medical devices needed for diagnosing and treating medical conditions that are expected to occur during future spaceflight missions may include real-time health monitoring, medical imaging capabilities, as well as blood and urine analysis. Novel methods for interacting with onboard patient medical records will be necessary, as will resource tracking. Terrestrial medicine shares many of these same needs, therefore a multitude of these required medical capabilities can likely be satisfied by currently available, Commercial-Off-The-Shelf (COTS) devices and methodologies; however, in some cases the unique space environment and increased mission durations will drive the need for modifications or customization of standard technologies and treatment procedures. This article will provide a review of medical devices and technologies that have been considered for inclusion within future spaceflight medical systems. It will also include a discussion about the modifications and customized development that have been performed, as well as descriptions of the technology demonstrations that have been conducted in analog and spaceflight environments.

Stochastic optimization for capacity configuration of data center microgrid thermal energy management equipment considering flexible resources

The rapid development of Internet and cloud computing technologies has led to the expansion of the scale of data centers, resulting in a continuous increase in the demand for data processing and storage. This not only results in higher energy consumption but also makes thermal management a key factor of energy management in data centers. A capacity configuration method is proposed for the core thermal management equipment of data center microgrids, electric boilers, cooling systems, and heat pumps. This method not only considers the uncertainty factors of the source and load but also effectively utilizes the characteristics of various flexible resources in data centers, such as the adjustable ability of different types of batch processing loads, air thermal inertia, and waste heat recovery, significantly improving the energy utilization efficiency and enhancing the system’s economy and flexibility. In addition, a wind power scenario generation method based on conditional least squares generative adversarial networks was proposed to improve the generation quality of wind power scenarios, and a random optimization model was constructed. Considering a certain province’s data center microgrid as a case study, the effectiveness of the model was verified, and a sensitivity analysis of the relevant parameters provided important references for microgrid planning. This study provides a new perspective and tool for the thermal management of data center microgrids and comprehensive utilization of renewable energy, which is of great significance for promoting the improvement of data center energy efficiency and sustainable development.

Scaling Power Management in Cloud Data Centers: A Multi-Level Continuous-Time MDP Approach

Scaling Power Management in Cloud Data Centers: A Multi-Level Continuous-Time MDP Approach

Design and Development of Oilfield Exploration and Development Data Exchange Management Platform

Design and Development of Oilfield Exploration and Development Data Exchange Management Platform

Improving Remote Leadership on PT. XYZ Business Development Expansion Team

This study analyzes and enhances the effectiveness of remote leadership within PT. XYZ’s Business Development Expansion team, crucial to the company’s growth. Despite PT. XYZ’s expansion beyond Jabodetabek, the remotely managed business development team’s performance has lagged compared to the directly managed Jabodetabek team. Using the DMAIC framework, this research identifies and addresses inefficiencies in remote leadership. Data was collected through interviews and company documents, employing methods such as fishbone analysis and the 5 Whys technique. Key findings indicate issues in leadership and management, tools and technology, and skills and impact. Problems include the absence of Standard Operating Procedures (SOPs), lack of Key Performance Indicators (KPIs), poor integration with support divisions, centralized data management, complex products, inadequate training, and lack of an integrated system for remote operations. The study recommends creating comprehensive SOPs, establishing specific KPIs, improving communication with support departments, decentralizing data management, simplifying product offerings, conducting regular training, and implementing an integrated management system to enhance remote leadership practices.

Green I.T and Datacenter: a Study of Environmental Management Indicators

Objective: Identify the environmental indicators from the ABNT ISO NBR 14031 standard applicable to data centers to improve energy efficiency and environmental management. Theoretical Framework: The study addresses concepts of Green IT, practices for data center efficiency, and the indicators from the ABNT NBR ISO 14031 standard, including environmental, managerial, operational, and condition performance indicators. Method: Bibliometric research to support the theme, through searches on the Scopus and IEEE platforms, with content analysis of the main references, including the NBR ISO 14031 standards. Results and Discussion: Operational indicators from the NBR ISO 14031 standard were identified and adapted for data centers, such as the quantity of reused materials, energy used and saved, and specific emissions. The practical application of these indicators was detailed. Research Implications: The research contributes to the application of Green IT practices and energy efficiency in data centers, providing a basis for the adoption of environmental management indicators. Originality/Value: Although there are various individual studies related to Green IT or data centers, there are few studies relating these themes together. Thus, this study fills this gap by promoting an integrated approach to Green IT and environmental management, and providing support for data center managers towards Green IT.

A contemporary survey of recent advances in federated learning: Taxonomies, applications, and challenges

The Internet of Things (IoT) has embedded itself in our daily lives, offering smart services and AI-driven applications. However, traditional AI methods face challenges due to centralized data management in vast IoT networks and privacy concerns. Federated Learning (FL) solves this by training AI directly on distributed IoT devices, avoiding extensive data sharing. This article provides interested researchers of FL with an in-depth and up-to-date exploration of the multifaceted of FL in a privacy-centric era. This study is structured with three principal objectives. Firstly, within the realm of taxonomies for FL, the study aims to identify and categorize the existing frameworks utilized in FL research. Furthermore, it seeks to provide a thorough overview of how FL is delineated and conceptualized in current literature. Secondly, the study endeavors to explore the applications of FL by scrutinizing and classifying various use cases across diverse domains and industries. This objective aims to illuminate the applications and industries benefitting from FL implementations. Thirdly, the study addresses the inherent challenges in FL by examining the associated limitations. The study strives to offer insights into the technical and practical obstacles encountered in FL scenarios, contributing to a comprehensive understanding of the challenges within this dynamic field. This survey serves as a roadmap towards establishing sustainable massive IoT networks. Additionally, it paves the way for interested researchers to pursue and advance the vision of FL's evolving role in IoT.

Efficient Resource Management in Cloud Environments: A Modified Feeding Birds Algorithm for VM Consolidation

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we introduce the Modified Feeding Birds Algorithm (ModAFBA) as an innovative solution for virtual machine (VM) consolidation in cloud environments. The primary objective is to enhance resource management and operational efficiency in cloud data centers. ModAFBA incorporates adaptive position update rules and strategies specifically designed to minimize VM migrations, addressing the unique challenges of VM consolidation. The experimental findings demonstrated substantial improvements in key performance metrics. Specifically, the ModAFBA method exhibited significant enhancements in energy usage, SLA compliance, and the number of VM migrations compared to benchmark algorithms such as TOPSIS, SVMP, and PVMP methods. Notably, the ModAFBA method achieved reductions in energy usage of 49.16%, 55.76%, and 65.13% compared to the TOPSIS, SVMP, and PVMP methods, respectively. Moreover, the ModAFBA method resulted in decreases of around 83.80%, 22.65%, and 89.82% in the quantity of VM migrations in contrast to the aforementioned benchmark techniques. The results demonstrate that ModAFBA outperforms these benchmarks by significantly reducing energy consumption, operational costs, and SLA violations. These findings highlight the effectiveness of ModAFBA in optimizing VM placement and consolidation, offering a robust and scalable approach to improving the performance and sustainability of cloud data centers.

Organizing visions for data-centric management: how Norwegian policy documents construe the use of data in health organizations

PurposeNorway, like other welfare states, seeks to leverage data to transform its pressured public healthcare system. While managers will be central to doing so, we lack knowledge about how specifically they would do so and what constraints and expectations they operate under. Public sources, like the Norwegian policy documents investigated here, provide important backdrops against which such managerial work emerges. This article therefore aims to analyze how key Norwegian policy documents construe data use in health management.Design/methodology/approachWe analyzed five notable policy documents using a “practice-oriented” framework, considering these as arenas for “organizing visions” (OVs) about managerial use of data in healthcare organizations. This framework considers documents as not just texts that comment on a topic but as discursive tools that formulate, negotiate and shape issues of national importance, such as expectations about data use in health management.FindingsThe OVs we identify anticipate a bold future for health management, where data use is supported through interconnected information systems that provide relevant information on demand. These OVs are similar to discourse on “evidence-based management,” but differ in important ways. Managers are consistently framed as key stakeholders that can benefit from using secondary data, but this requires better data integration across the health system. Despite forward-looking OVs, we find considerable ambiguity regarding the practical, social and epistemic dimensions of data use in health management. Our analysis calls for a reframing, by moving away from the hype of “data-driven” health management toward an empirically-oriented, “data-centric” approach that recognizes the situated and relational nature of managerial work on secondary data.Originality/valueBy exploring OVs in the Norwegian health policy landscape, this study adds to our growing understanding of expectations towards healthcare managers' use of data. Given Norway's highly digitized health system, our analysis has relevance for health services in other countries.

Multi-stakeholder, intentional outreach for improving representative recruitment in Pragmatica–Lung (SWOG S2302).

11019 Background: Studies show that <10% of patients with cancer participate in clinical trials. Pragmatica – Lung (SWOG S2302) utilizes a pragmatic approach for a registrational (FDA) trial that allows less data collection and broader eligibility, thus decreasing barriers to diverse enrollment. S2302 aims to improve overall and diverse accrual by using a novel trial design and multilevel community engagement. Methods: S2302 is a real-world randomized phase III registrational study comparing pembrolizumab + ramucirumab vs investigator-chosen standard therapy in 2nd line advanced/metastatic NSCLC. A multi-stakeholder recruitment plan was developed to improve diverse accrual (with initial focus on recruiting Black patients). The plan was vetted through SWOG communications and SWOG Lung Committee’s Working Group, DEI champion, patient advocate, and community engagement subcommittee. The DEI champion identified sites in the Southeast with high minority accrual in prior trials and completed directed informational visits. An external firm created culturally and linguistically appropriate patient education material, engaged sites with historically high accrual of Black and/or LatinX patients, leveraged advocacy partners to improve community awareness, and monitored enrollment by site. A monthly accrual report with demographic summaries (including age, sex, race, ethnicity) and site enrollment information is generated from SWOG Statistics and Data Management Center to monitor accrual rate and diversity. Results: From March through December 2023 (Table), the study accrued 37% of its goal and is enrolling above its target rate of 25 pts/mo averaging 36/mo over the last 5 months. Of enrolled pts, 58% are male, 13% are Black, and 3% are LatinX; from 59 academic, 67 community (13% rural), and 2 VA sites. Comparatively, LungMAP S1800A (the phase II precursor to S2302) accrued 7% Black and 1.5% LatinX pts with an average accrual of 9.2 pts/mo. Through November, the external firm contacted 24 site PIs (63% community-based), whose sites had collectively enrolled 16 pts, for a normalized pre-call rate of 0.1340 pts/mo. After contact and through November, these sites enrolled 23 pts, a rate of 0.3835 pts/mo – a 186% increase. Conclusions: The intentional, multi-pronged recruitment plan has exceeded historical overall, Black, and LatinX patient accrual rates. The data highlight novel approaches to trial design, recruitment strategies, and increased internal and external collaboration resulting in improved diversity of clinical trial enrollment and may be a potential toolkit for future trials. Support: NIH/NCI grants U10CA180888 and U10CA180819; and in part by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA, and Eli Lilly and Company. Clinical trial information: NCT05633602 . [Table: see text]

Unveiling Genetic Reinforcement Learning (GRLA) and Hybrid Attention-Enhanced Gated Recurrent Unit with Random Forest (HAGRU-RF) for Energy-Efficient Containerized Data Centers Empowered by Solar Energy and AI

The adoption of renewable energy sources has seen a significant rise in recent years across various industrial sectors, with solar energy standing out due to its eco-friendly characteristics. This shift from conventional fossil fuels to solar power is particularly noteworthy in energy-intensive environments such as cloud data centers. These centers, which operate continuously to support active servers via virtual instances, present a critical opportunity for the integration of sustainable energy solutions. In this study, we introduce two innovative approaches that substantially advance data center energy management. Firstly, we introduce the Genetic Reinforcement Learning Algorithm (GRLA) for energy-efficient container placement, representing a pioneering approach in data center management. Secondly, we propose the Hybrid Attention-enhanced GRU with Random Forest (HAGRU-RF) model for accurate solar energy prediction. This model combines GRU neural networks with Random Forest algorithms to forecast solar energy production reliably. Our primary focus is to evaluate the feasibility of solar energy in meeting the energy demands of cloud data centers that utilize containerization for virtualization, thereby promoting green cloud computing. Leveraging a robust German photovoltaic energy dataset, our study demonstrates the effectiveness and adaptability of these techniques across diverse environmental contexts. Furthermore, comparative analysis against traditional methods highlights the superior performance of our models, affirming the potential of solar-powered data centers as a sustainable and environmentally responsible solution.

Comprehensive Review of Smart Cities using IOT in Coud

The rapid growth of urbanization necessitates smarter and more sustainable city management. The convergence of Internet of Things (IoT) devices and cloud computing offers a transformative solution for building Smart Cities. This review explores the integration of IoT and cloud technologies in creating intelligent urban infrastructure. It examines how data collected from a network of sensors and devices across the city can be leveraged to improve various aspects of urban life, including traffic management, energy efficiency, public safety, waste management, and parking. The paper discusses the key benefits of cloud computing for Smart City IoT applications, such as scalability, centralized data management, cost-effectiveness, security, and advanced analytics capabilities. Additionally, it explores the challenges associated with implementing Smart City IoT solutions, such as data privacy concerns, cybersecurity risks, and the need for interoperability between different devices and platforms. Finally, the review concludes by highlighting the potential of Smart City IoT powered by cloud computing to create a more efficient, sustainable, and livable future for our cities.

Contributions of microlayer to flow boiling heat transfer in the mini-channel

Flow boiling stands out as an efficient mode of heat transfer with significant potential for reducing carbon footprint. Based on a coupled Volume-of-Fluid and Level Set (VOSET) method, this numerical study reveals the microlayer and its evaporative heat flux distributions, as well as the effects of microlayer evaporation and depletion on bubble dynamic behavior, flow pattern, wall superheat distribution, and boiling crisis for the flow boiling in a mini-channel. These aspects are firstly comprehensively reported by the numerical method. Results indicate that the rapid evaporation of a thin microlayer positioned beneath elongated bubbles significantly contributes to heat dissipation during flow boiling in a mini-channel. The heat flux through the microlayer surpasses 300 kW/m2, accounting for approximately 42% of heat dissipation at 200 kW/m2. Furthermore, this contribution increases to over 70% as the heat flux exceeds 500 kW/m2. As a result, in comparison to scenarios without microlayers, the presence of microlayers results in an average heat transfer coefficient (HTC) that is 3.97 times larger within the range of 200–500 kW/m2. Additionally, microlayer evaporation delays the happening of flow boiling crisis. Without microlayers, the wall superheat exceeds 180 K at 500 kW/m2, whereas under microlayer influence, the maximum local wall superheat is only 37.09 K at 1000 kW/m2. With further increases in heat flux, the emergence of large dry patches resulting from microlayer depletion induces the beginning of heat transfer deterioration. Finally, the ongoing coalescence of elongated bubbles necessitates a prolonged period for rewetting dry patches at the exit of the mini-channel, triggering the flow boiling crisis at 1300 kW/m2. The study results provide crucial guidance for the development and application of the flow boiling heat transfer technology for the thermal management of data centers.

Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization

As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.

Joint Optimization of Measurement Point Intelligent Selection and End-to-End Network Traffic Calculation in Datacenters

The effective design and management of data centers needs to follow the end-to-end traffic characteristics of data center networks (DCNs). However, directly measuring the end-to-end traffic of the network requires huge software and hardware costs. Since the particularity of the structure of DCNs, the flow estimation method used in the traditional computer network cannot be applied to existing DCNs. In this paper, we study the end-to-end traffic calculation of cloud computing DCNs. We propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLS-TC</b> , which is an intelligent end-to-end traffic inference algorithm based on network tomography. Only using SNMP (simple network management protocol) data generally supported by switches, end-to-end traffic information can be calculated quickly and accurately. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLS-TC</b> first devices a network traffic measurement point intelligent selection scheme based on node weighting. It first assigns weight to nodes through node criticality, and then uses node weighted incidence matrix approximation algorithm to calculate initial solution. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLS-TC</b> then designs a network tomography method suitable for cloud computing network traffic calculation. It uses the time correlation of data center traffic to model the algorithm problem into a linear state space model, and finally calculates the traffic carried by each path through the improved Kalman filtering algorithm. Our evaluation and analysis demonstrate that <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLS-TC</b> can effectively use the extracted coarse-grained traffic characteristics, and greatly improve the accuracy of the calculation on the premise of ensuring the computational efficiency.

Optimizing Data Centre Energy Efficiency with Dynamic Resource Allocation and Intelligent Cooling Management through Machine Learning

Our research aims to improve energy efficiency in data centres by combining cloud computing infrastructure with machine learning techniques. We propose that dynamic resource assignment, combined with intelligent optimisation of cooling systems, can reduce power waste and operational costs. Real-time sensor data from various data centre components such as servers, cooling systems, and power distribution units is collected and fed into machine learning models for analysis. In this way, we can create power arrangements that are tailored to the resources and needs of various applications. The experimental results show that energy consumption has been reduced by an average of 30% compared to traditional methods. Furthermore, our machine learning models are quite accurate in predicting cooling system performance. For example, an Artificial Neural Network (ANN) has an accuracy rate of 98.78%. This result demonstrates the efficacy of our approach in promoting energy efficiency and operational performance in data centres: it not only provides a scalable, cost-effective solution to industry energy efficiency challenges, but it also improves day-to-day data centre operations by reducing electrical consumption. Our approach, which is based on dynamic allocation of computational resources and real-time data analysis for optimising cooling systems, not only saves energy but also improves operational efficiency. With energy in mind, we must all work towards a more sustainable and green approach to data centre management. Future research can look into other potential optimisations, as well as issues with scalability and application in real-world data centre environments.