Data Center Research Articles

Efficient Resource Allocation to Enhance the Quality of Service in Cloud Computing

Pay-as-you-go models are used to grant users access to cloud services. While using the cloud, an imbalance workload on data centre resources degrades quality of service metrics like makespan, storage, high failure rate, and energy consumption. Hence proposed the heuristic based hybrid GA to enhance the QoS with resource allocation in cloud computing. The population is first initialized using the Binary encoding sorts the tasks according to priority. After that, the Best Fit algorithm compares the Best Fit with iterations of each fitness value depending on the computation time to shorten the make span. Heuristic crossover approach and mutation are then used to update the probability of the existing population with the new population lowers the failure rate by using the fitness value. Therefore, the proposed heuristic-based hybrid GA technique balanced the load and allocate the resources effectively to improve QoS performances. The outcome reveals that the proposed method of QoS performances attained less makespan, energy consumption, failure rate and execution time with effectively allocated the resources of 1% to 39% when compared to the previous methods in cloud computing.

Trends and Perioperative Outcomes of Surgical Treatments for Benign Prostatic Hyperplasia in Germany: Results from the GRAND Study.

Real-world data exploring the perioperative outcomes of different surgical treatments for benign prostatic hyperplasia (BPH) are lacking. We aimed to assess the trends of BPH surgeries and compare their perioperative outcomes. We used the GeRmAn Nationwide inpatient Data (GRAND), provided by the Research Data Center of the Federal Bureau of Statistics (2005-2022), and performed multiple patient-level analyses. Our study included 1 355 845 surgical treatments for BPH. Of these, 1 084 650 (79.9%) were transurethral resection of the prostate (TURP), 90 735 (6.9%) simple prostatectomy, 64 325 (4.7%) holmium laser enucleation of the prostate (HoLEP), 58 406 (4.3%) laser vaporization, 25 747 (1.9%) electrovaporization, 15 241 (1.1%) thulium laser enucleation of the prostate (ThuLEP), 7873 (0.58%) prostate incision, 3298 (0.24%) water ablation, 2724 (0.2%) water vapor thermal therapy, 1235 (0.09%) prostate stent, 1100 (0.08%) transurethral needle ablation of the prostate, and 511 (0.03%) prostatic urethral lift. The use of HoLEP, ThuLEP, water ablation, and water vapor thermal therapy has increased exponentially in the last years. After adjusting for the major risk factors in the multivariate regression analysis, simple prostatectomy was associated with the worst outcomes compared with TURP, followed by electrovaporization. On the contrary, HoLEP and ThuLEP were associated with favorable outcomes, in terms of sepsis, ureteral stent placement, transfusion, admission to the intensive care unit, hospital stay, and mortality, compared with TURP. Similarly, minimally invasive surgical therapies were associated with high safety compared with TURP. Nevertheless, our findings are based on retrospective billing data and are prone to a selection bias. The trends and perioperative outcomes of BPH surgery should be taken into consideration to improve clinical decision-making and patient outcomes.

Optimizing Customer Communication Management (CCM) The Benefits of Migrating Systems to New Data Centers

Customer Communication Management (CCM) systems have become indispensable in modern businesses because they help enhance customer engagement and deliver personalized messaging. Hence, the issue of data handling optimization and infrastructure efficiency will hold an irreplaceably important place in the operation of CCM systems. With an escalation in the usage of CCM, companies are currently transitioning from one data center to another to meet business requirements. Migrations like this are conducted due to decreased expenditures, better performance, scalability, disaster recovery, security improvements, and compliance with regulatory requirements. This paper discusses the reasons behind data center migration and the steps CCM systems must take to complete the migration. It also discusses the advantages and challenges of changing a data center. It gives a deeper look into how companies can follow a migration strategy that guarantees frictionless communication and higher operational efficiency. Keywords Customer Communication Management (CCM), Datacenter, system improvement

Making Sense of Big Data in Intelligent Transportation Systems: Current Trends, Challenges and Future Directions

Intelligent Transportation Systems (ITS) generate massive amounts of Big Data through both sensory and non-sensory platforms. The data support batch processing as well as stream processing, which are essential for reliable operations on the roads and connected vehicles in ITS. Despite the immense potential of Big Data intelligence in ITS, autonomous vehicles are largely confined to testing and trial phases. The research community is working tirelessly to improve the reliability of ITS by designing new protocols, standards and connectivity paradigms. In the recent past, several surveys have been conducted that focus on Big Data Intelligence for ITS, yet none of them have comprehensively addressed the fundamental challenges hindering the widespread adoption of autonomous vehicles on the roads. Our survey aims to help readers better understand the technological advancements by delving deep into Big Data architecture, focusing on data acquisition, data storage and data visualization. We reviewed sensory and non-sensory platforms for data acquisition, data storage repositories for archival and retrieval of large datasets, and data visualization for presenting the processed data in an interactive and comprehensible format. To this end, we discussed the current research progress by comprehensively covering the literature and highlighting challenges that urgently require the attention of research community. Based on the concluding remarks, we argued that these challenges hinder the widespread presence of autonomous vehicles on the roads. Understanding these challenges is important for a more informed discussion on the future of self-driven technology. Moreover, we acknowledge that these challenges not only affect individual layers but also impact the functionality of subsequent layers. Finally, we outline our future work that explores how resolving these challenges could enable the realization of innovations such as smart charging systems on the roads and data centers on wheels.

FPGA-Based Large-Scale Sorting with Optimized Bandwidth Utilization

Fast sorting of large-scale data is an essential task for data centers. In previous works, the existing computational model of sorting kernel still results in lower bandwidth utilization on the external memory bus. And the execution of merge operations in merge sort circuit on FPGAs depends on control commands from the host CPU. In this case, the merge sort circuit is not fully offloaded to hardware layer for acceleration, resulting in a performance loss. We design an on-chip merge sort controller to efficiently command the merge sort process. The proposed controller has the ability to schedule multiple on-chip computing kernels simultaneously in a more efficient mode, thus ensuring that the circuit has a better bandwidth utilization. Meanwhile, fundamental factors affecting the performance of merge sort are studied and analyzed, and we propose a high-performance merge sort architecture. Results show that using the proposed controller-centered architecture, an overall improvement of 20-30% in sorting throughput can be achieved. Compared with the state-of-the-art previous merge sorting implementation on FPGA, our circuit can achieve 1.22/1.46 \(\times\) speedup.

Chaos Key Update Using Constellation Selection and Clustering for Data Centers

Chaos Key Update Using Constellation Selection and Clustering for Data Centers

CloudSim 7G: An Integrated Toolkit for Modeling and Simulation of Future Generation Cloud Computing Environments

ABSTRACTBackgroundCloud Computing has established itself as an efficient and cost‐effective paradigm for the execution of web‐based applications, and scientific workloads, that need elasticity and on‐demand scalability capabilities. However, the evaluation of novel resource provisioning and management techniques is a major challenge due to the complexity of large‐scale data centers. Therefore, Cloud simulators are an essential tool for academic and industrial researchers, to investigate the effectiveness of novel algorithms and mechanisms in large‐scale scenarios.AimThis paper proposes CloudSim 7G, the seventh generation of CloudSim, which features a re‐engineered and generalized internal architecture to facilitate the integration of multiple CloudSim extensions within the same simulated environment.MethodsAs part of the new design, we introduced a set of standardized interfaces to abstract common functionalities and carried out extensive refactoring and refinement of the codebase.ResultsThe result is a substantial reduction in lines of code with no loss in functionality, significant improvements in run‐time performance and memory efficiency (up to 25\% less heap memory allocated), as well as increased flexibility, ease‐of‐use, and extensibility of the framework.ConclusionThese improvements benefit not only CloudSim developers but also researchers and practitioners using the framework for modeling and simulating next‐generation Cloud Computing environments.

Advancing Security and Efficiency in IoT Healthcare Applications with Challenges Benefits and Latency Reduction Techniques

With the expansion of the Internet of Things (IoT) and cloud computing, conventional reliance on centralized cloud data centres for the storage, analysis, and real-time processing of vast data volumes presents significant challenges. This is particularly applicable to the elevated latency and stringent security demands necessary for real-time IoT healthcare applications. Critical and time-sensitive applications, including e-healthcare, telemedicine, and robotic surgery, necessitate ultra-low latency and stringent security measures. Suboptimal processing, connectivity, and networks impede the performance of these applications. Moreover, conventional cloud designs frequently fail to provide the necessary Quality of Service (QoS) for IoT healthcare systems. Consequently, this essay also explores latency reduction and security improvement techniques in IoT healthcare focusing on the need for information transmission in the respective areas. It wishes to list the first principles for approaches that reduce latency and protect communications, and computational architectures that can operate with such systems. It also explores the features that are crucial for understanding latency and security, as well as comparing several methods of addressing latency reduction and improvement in security alongside their effectiveness. It critically assesses previous approaches, identifies gaps in the literature and emphasizes unanswered questions in the study that may be useful in other works in this field. This research incorporates these findings into propelling concepts relative to IoT device connection and enhancing general paradigms relating to the better employment of healthcare applications. These findings support the need to design future IoT healthcare systems that will be latent sensitive, security-enhanced, and high-performance systems due to emerging characteristics of IoT in healthcare.

High mortality rates and long-term complications in children with infectious brainstem encephalitis: A study of sixteen cases.

Brainstem encephalitis (BE) can cause sudden death in children. Fewer studies have been conducted on the incidence, clinical manifestations, pathogens and post-infectious sequelae of pediatric infectious BE. Pediatric patients diagnosed with BE in our Medical Center from 01 January 2015 to 31 July 2024 were retrospectively reviewed. The clinical data of these children were obtained from the hospital's medical database on 15 August 2024. The number of outpatient and inpatient patients at our Medical Center during that period were provided by the hospital data center. Data analysis was conducted using Excel 2019. A total of twenty-eight cases were diagnosed with BE in our National Children's Medical Center over the past decade. Among them, 57.1% (16/28) cases were diagnosed with infectious BE. The incidence of infectious BE was estimated to be 16 cases per 30 million outpatient visits and 13 cases per 500,000 hospitalized patients. Fever, consciousness disorders and seizures were observed in 75.0% (12/16), 68.8% (11/16) and 62.5% (10/16) of the cases, respectively. Among them, 31.3% (5/16) cases were diagnosed as human enterovirus infections, 12.5% (2/16) cases were confirmed to be influenza B virus infections, while one case each was diagnosed with herpes simplex virus 1 and human herpesvirus 6 infection. The mortality rate during hospitalization was 12.5% (2/16). Among the surviving patients, 50.0% (7/14) of them had follow-up records, 85.7% (6/7) of the survivors suffered from sequelae such as motor disorders. Fever, consciousness disorders and seizures were the major clinical manifestations in patients with infectious BE visited our Medical Center. These rare cases exhibited a notably high mortality rate and a significant frequency of long-term complications.

Enhancing Cloud Data Center Security through Deep Learning: A Comparative Analysis of RNN, CNN, and LSTM Models for Anomaly and Intrusion Detection

Cloud data centers form the backbone of modern digital ecosystems, enabling critical operations for businesses, governments, and individuals around the world. However, their high connectivity and complexity make them prime targets for cyberattacks, leading to service disruptions and data breaches. This paper investigates the use of deep learning techniques, namely Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks, to enhance cloud data center security. By employing these models for anomaly detection and intrusion prevention, the study performs a comparative analysis. The results indicate that the LSTMs achieved the highest ROC AUC score (0.90), demonstrating better detection of persistent threats. These findings highlight the potential of deep learning to revolutionize cloud security by providing scalable, accurate, and proactive measures against evolving cyber threats.

Application and simulation of IEC water chillers for summer free cooling in data centers

Application and simulation of IEC water chillers for summer free cooling in data centers

A new framework for evaluating and enhancing the performance of district heating systems integrated with data centres using short-term thermal energy storage

A new framework for evaluating and enhancing the performance of district heating systems integrated with data centres using short-term thermal energy storage

TPMCD: A method to optimizing cost and throughput for clustering tasks and hybrid containers in the cloud data center

TPMCD: A method to optimizing cost and throughput for clustering tasks and hybrid containers in the cloud data center

Analysis of evolutionary multi-objective algorithms for data center electrical systems

Analysis of evolutionary multi-objective algorithms for data center electrical systems

The Impact of Service Demand Variability on Data Center Performance

The Impact of Service Demand Variability on Data Center Performance

Performance analysis and optimization of a hybrid mechanical and free cooling system with cold water storage for a data center

Performance analysis and optimization of a hybrid mechanical and free cooling system with cold water storage for a data center

Data center sustainability: The role of flexible fuel CCHP in mitigating grid emissions and power constraints

Data center sustainability: The role of flexible fuel CCHP in mitigating grid emissions and power constraints

Experimental study on improving thermal management of fan-wall data center by setting baffles in cold aisle

Experimental study on improving thermal management of fan-wall data center by setting baffles in cold aisle

A global gross primary productivity of sunlit and shaded canopies dataset from 2002 to 2020 via embedding random forest into two-leaf light use efficiency model.

Gross primary productivity (GPP) is crucial for understanding the carbon cycle and maintaining ecosystem balance under climate change. We attempt to generate a long-term global dataset for GPP of sunlit (GPPsu) and shaded leaves (GPPsh) by a hybrid model combining the random forest (RF) submodule with the two-leaf light use efficiency (TL-LUE) model. First, the TL-LUE model was optimized by considering the seasonal differences in the clumping index on a global scale (TL-CLUE). Then, we used the RF technique to integrate various environmental stress factors, including meteorological factors, hydrological variables, soil properties, and elevation, which originate from the NASA MERRA-2 dataset, ISRIC soil Grids, and USGS data center. Furthermore, the RF submodule was embedded into the TL-CLUE model to construct the hybrid model (TL-CRF), which was trained and evaluated based on global eddy covariance (EC) site data from the AmeriFlux and FLUXNET2015 datasets. We produced a global GPP, GPPsu, and GPPsh dataset with a spatial resolution of 0.05 × 0.05° over 2002-2020 by the TL-CRF model driven by the LP DACC leaf area index and land cover, NASA MERRA-2 incoming shortwave solar radiation, and the above environmental variables. This GPP product provides a data basis for improving our understanding of the dynamics of global vegetation productivity and its interactions with the changes in environmental conditions.

Seismic Resilience Assessment and Improvement of a Containerized Edge Data Center: A Quantitative Framework

Seismic Resilience Assessment and Improvement of a Containerized Edge Data Center: A Quantitative Framework