Data Center Research Articles

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

Free cooling is regarded as the most effective way to reduce the energy consumption of the cooling system in data centers. This study proposes a novel water-medium indirect evaporative cooling (IEC) chiller used in a data center to achieve free cooling throughout the year as an alternative to electrical chillers. Field tests and numerical modeling of the IEC water chillers are both reported to evaluate the performance and efficiency. This paper first-time reports on the application of IEC water chillers to deliver year-round free cooling for a data center in Changji, China. The wet-bulb efficiency of the tested chiller is measured as 120% to 152.8% and the dew-point efficiency is 27% to 62.7%. A numerical model is established to verify the operating conditions, and some common operational optimizations are pointed out in this test for energy savings. PUE can be reduced to 1.212 after the optimization. More future performance predictions of the IEC water chillers are studied when the data center load rate increases and when the local weather changes in a year to discuss the year-round availability. For further study, the performance of IEC water chillers in different climate regions is also forecasted. The results indicate that it’s an effective way for IEC to achieve free cooling in regions where natural cold resources are abundant. In this study, the field test performance and prediction of IEC water chillers are provided as a typical and practical application as the natural cooling source in data centers in hot and dry areas.

The impacts on population health by China's regional health data centers and the potential mechanism of influence.

China recently established a series of pilot regional health data centers with a mandate to acquire, consolidate, analyze, and translate data into evidence for health policy decision-making. This experiment with "big data" has the potential to influence population health and is the focus of this study. This study used national longitudinal survey data from the China Family Panel Studies over the period 2014-2020 to empirically assess the impact of China's establishment of pilot regional health data centers on population health and health inequality. A difference-in-differences model was employed to investigate the policy effect on population health, with additional exploration of possible mechanisms of influence. The corrected concentration index was used to measure health inequality, while Wagstaff decomposition method was applied to examine the marginal influence of the policy effect on health inequality. Overall health status of local residents has improved after the establishment of the pilot regional health data centers. Using mechanism analysis, the findings demonstrated that improvements to population health were driven by promoting healthy lifestyles and innovations in medical practices. Furthermore, due to differences in individual e-health literacy, the pilot centers produced "pro-rich" health inequality where high-income groups benefited more from the establishment of the pilot centers in terms of health than low-income groups. This study has highlighted the potential to improve population health, in general, with the advent of big data centers, but for these benefits be unevenly distributed among the resident population.

The Impact of Service Demand Variability on Data Center Performance

The Impact of Service Demand Variability on Data Center Performance

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

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

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

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

Intelligent terminal structure response on anchorage foundation stability of Undersea Data Cabins affected by turbidity flow

The construction of Undersea Data Center (UDC) infrastructure will be a crucial choice for countries worldwide in developing the numerical economy. The support structure foundation of Undersea Data Cabins serves as the safety bedrock of the entire underwater infrastructure project, This paper proposes a monitoring method that combines the support structure foundation and anchoring of UDC, develops an anchorage foundation and its intelligent terminal structure (ITS) for the UDC support structure, and conducts a physical similarity model experiment on the ITS perception of the UDC support structure anchorage foundation under extreme turbidity flow erosion. The results verify the adaptability of the ITS components installed on the UDC anchorage foundation in monitoring axial load loss. It was discovered that the axial load of the UDC anchorage foundation and the evolution of turbidity flow density under turbidity flow erosion exhibit a cubic polynomial function relationship, which can provide a quantitative relational criterion for the stability monitoring and early warning of the UDC anchorage foundation. This has significant safety implications for the development of UDC industrial clusters and commercial promotion.

MILP optimization of the multi-heat pump waste heat recovery system integrated with full-free cooling data center through lake water

MILP optimization of the multi-heat pump waste heat recovery system integrated with full-free cooling data center through lake water

Software-Defined Networking Challenges and Research Opportunities for Future Interest

Software Defined Network (SDN) has become one of the most preferred solutions for the management of large-scale complex networks. The network policies in the large-scale network are difficult to embed on entire network devices simultaneously, whereas in SDN these policies can be embedded on the top of the network. The SDN network is divided into two parts which are vertically integrated to form the entire network. Currently many aspects of the classical architecture of the Internet are etched in stone – a so-called ossification of the Internet – which has led to major obstacles in IPv6 deployment and difficulty in using IP multicast services. Yet, there exist many reasons to extend the Internet, e.g., for improving intra-domain and inter- domain routing for high availability of the network, providing end-to-end connectivity for users, and allowing dynamic management of network resources for new applications, such as data center, cloud computing, and network virtualization. To address these requirements, the next- generation architecture for the Future Internet has introduced the concept of Software-Defined Networking (SDN). At the core of this emerging paradigm is the separation and centralization of the control plane from the forwarding elements in the network as opposed to the distributed control plane of existing networks. With the advent of cloud computing, many ecosystem and business paradigms are encountering potential changes and may be able to eliminate their IT infrastructure maintenance processes. Real-time performance and high availability requirements have induced telecom networks to adopt the new concepts of the cloud model: software-defined networking (SDN) and network function virtualization (NFV). NFV introduces and deploys new network functions in an open and standardized IT environment, while SDN aims to transform the way network’s function. SDN and NFV are complementary technologies; they do not depend on each other. However, both concepts can be merged and have the potential to mitigate the challenges of legacy networks. In this paper, our aim is to describe the benefits of using SDN in a multitude of environments such as in data centers, data center networks, and Network as service offerings.

Digital Waste Mitigation in AI and Cloud Computing: A Comprehensive Framework for Environmental Sustainability

This article examines the growing environmental impact of digital waste generated by artificial intelligence systems and cloud computing infrastructure. It presents a comprehensive article analysis of the environmental footprint associated with data centers, AI model training, and cloud-based operations. The article introduces a framework for identifying and measuring digital waste across computing environments, followed by an evaluation of current mitigation strategies including smart data management, deduplication techniques, and energy-efficient algorithm design. Through case studies and empirical analysis, the article demonstrates the effectiveness of combining technical solutions with organizational policies to reduce digital waste. The article highlights the crucial role of edge computing and renewable energy adoption in minimizing environmental impact. Additionally, the article proposes a set of best practices for organizations to integrate sustainability into their digital transformation initiatives. This article contributes to the growing body of literature on sustainable computing by providing actionable strategies for balancing technological advancement with environmental preservation. The conclusions emphasize the importance of industry-wide collaboration in establishing standards for measuring and reducing digital waste, while highlighting areas for future research in green computing technologies.

Trends in Reports of Statin-Associated Immune-Mediated Necrotizing Myopathy Using the Japanese Adverse Drug Event Report Database.

In October 2016, precautions of the package inserts for ethical drugs containing statins were revised for immune-mediated necrotizing myopathy (IMNM). Our objective was to assess the trend in statin-associated IMNM reported before and after the release of the revised precautions in Japan. We investigated the number of case reports and estimated annual incidence rate of statin-associated IMNM using the Japanese Adverse Drug Event Report and Japan Medical Data Center (JMDC) databases, respectively. To identify IMNM case reports, we used the preferred term "immune-mediated myositis" (MedDRA version 27.1). We identified 172 statin-associated IMNMs in 145 case reports of patients between 1 April 2004 and 31 March 2023. The most common suspected statin administered to the patients was rosuvastatin (34.3%), followed by pitavastatin (25.0%) and atorvastatin (22.1%). No statin-associated IMNM was reported in patients who were treated with combination agents containing statins. The number of reported statin-associated IMNMs increased from 3 in 2015 to a peak of 51 in 2019, after which it was 22 (2020), 17 (2021), and 21 (2022) in the following years. The estimated annual incidence rate did not differ with statins, it rarely exceeded 5 per 1,000,000 patients. There was an increasing trend in the number of statin-associated IMNM after the revised precautions of package inserts for statins were released.