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Journal articles on the topic 'Heterogeneous data center'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Garg, Siddharth, Shreyas Sundaram, and Hiren D. Patel. "Robust heterogeneous data center design." ACM SIGMETRICS Performance Evaluation Review 39, no. 3 (2011): 28–30. http://dx.doi.org/10.1145/2160803.2160850.

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Yang, Jing Bo, Shu Huang, and Pan Jiang. "Research on Distributed Heterogeneous Data Storage Algorithm in Cloud Computing Data Center." Applied Mechanics and Materials 624 (August 2014): 553–56. http://dx.doi.org/10.4028/www.scientific.net/amm.624.553.

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With the development of cloud computing, data center is also improved. cloud computing data center contains hundreds, even million of servers or PCs. It has many heterogeneous resources. Data center is a key to promise high scalability and resource usage of cloud computing. In addition, replica is introduced into data center, which is an important method to improve availability and performance. In this paper, the research on distributed storage algorithm based on the cloud computing. This algorithm uses the design of system storage level indicators within classification of massive data storage

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Malebary, Sharaf, Sami Alesawi, and Hao Che. "Tail Prediction for Heterogeneous Data Center Clusters." Processes 11, no. 2 (2023): 407. http://dx.doi.org/10.3390/pr11020407.

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Service providers need to meet their service level objectives (SLOs) to ensure better client experiences. Predicting tail sojourn times of applications is an essential step to combat long tail latency. Therefore, as an attempt to further unravel the power of our prediction model, new study scenarios for heterogeneous environments will be introduced in this research by using either of two methods: white- or black-box solutions. This research presents several techniques for modeling clusters of inhomogeneous nodes. Those techniques are recognized as heterogeneous fork-join queuing networks (HFJQ

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Bai, Wei-Hua, Jian-Qing Xi, Jia-Xian Zhu, and Shao-Wei Huang. "Performance Analysis of Heterogeneous Data Centers in Cloud Computing Using a Complex Queuing Model." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/980945.

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Performance evaluation of modern cloud data centers has attracted considerable research attention among both cloud providers and cloud customers. In this paper, we investigate the heterogeneity of modern data centers and the service process used in these heterogeneous data centers. Using queuing theory, we construct a complex queuing model composed of two concatenated queuing systems and present this as an analytical model for evaluating the performance of heterogeneous data centers. Based on this complex queuing model, we analyze the mean response time, the mean waiting time, and other import

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Zhang, Qian, Jingyao Li, Hongyao Zhao, et al. "Efficient Distributed Transaction Processing in Heterogeneous Networks." Proceedings of the VLDB Endowment 16, no. 6 (2023): 1372–85. http://dx.doi.org/10.14778/3583140.3583153.

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Countrywide and worldwide business, like gaming and social networks, drives the popularity of inter-data-center transactions. To support inter-data-center transaction processing and data center fault tolerance simultaneously, existing protocols suffer from significant performance degradation due to high-latency and unstable networks. In this paper, we propose RedT, a novel distributed transaction processing protocol that works in heterogeneous networks. In detail, nodes within a data center are inter-connected via the RDMA-capable network and nodes across data centers are inter-connected via T

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Li, Xin, Liangyuan Wang, Jemal H. Abawajy, Xiaolin Qin, Giovanni Pau, and Ilsun You. "Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System." Energies 13, no. 17 (2020): 4508. http://dx.doi.org/10.3390/en13174508.

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Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the

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Lin, Wei-Wei, Liang Tan, and James Z. Wang. "Novel Resource Allocation Algorithm for Energy-Efficient Cloud Computing in Heterogeneous Environment." International Journal of Grid and High Performance Computing 6, no. 1 (2014): 63–76. http://dx.doi.org/10.4018/ijghpc.2014010104.

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Energy efficiency is one of the most important design considerations for a cloud data center. Recent approaches to the energy-efficient resource management for data centers usually model the problem as a bin packing problem with the goal of minimizing the number of physical machines (PMs) employed. However, minimizing the number of PMs may not necessarily minimize the energy consumption in a heterogeneous cloud environment. To address the problem, this paper models the resource allocation problem in a heterogeneous cloud data center as a constraint satisfaction problem (CSP). By solving this c

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Akbari, Abbas, Ahmad Khonsari, and Seyed Mohammad Ghoreyshi. "Thermal-Aware Virtual Machine Allocation for Heterogeneous Cloud Data Centers." Energies 13, no. 11 (2020): 2880. http://dx.doi.org/10.3390/en13112880.

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In recent years, a large and growing body of literature has addressed the energy-efficient resource management problem in data centers. Due to the fact that cooling costs still remain the major portion of the total data center energy cost, thermal-aware resource management techniques have been employed to make additional energy savings. In this paper, we formulate the problem of minimizing the total energy consumption of a heterogeneous data center (MITEC) as a non-linear integer optimization problem. We consider both computing and cooling energy consumption and provide a thermal-aware Virtual

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Jiang, Tao, Huaxi Gu, Kun Wang, Xiaoshan Yu, and Yunfeng Lu. "BHyberCube: A MapReduce aware heterogeneous architecture for data center." Computer Science and Information Systems 14, no. 3 (2017): 611–27. http://dx.doi.org/10.2298/csis170202019t.

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Some applications, like MapReduce, ask for heterogeneous network in data center network. However, the traditional network topologies, like fat tree and BCube, are homogeneous. MapReduce is a distributed data processing application. In this paper, we propose a BHyberCube network (BHC), which is a new heterogeneous network for MapReduce. Heterogeneous nodes and scalability issues are addressed considering the implementation of MapReduce in the existing topologies. Mathematical model is established to demonstrate the procedure of building a BHC. Comparisons of BHC and other topologies show the go

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Li, Dan, Jing Zhu, Jianping Wu, Junjie Guan, and Ying Zhang. "Guaranteeing Heterogeneous Bandwidth Demand in Multitenant Data Center Networks." IEEE/ACM Transactions on Networking 23, no. 5 (2015): 1648–60. http://dx.doi.org/10.1109/tnet.2014.2341246.

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Hao, Ping, and Bin Li Lu. "Research on Abnormal Data Processing Method in Intelligent Data Adapter Based on Bayesian Network." Advanced Materials Research 765-767 (September 2013): 1190–95. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1190.

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This paper put forward a bayesian network junction tree reasoning and rule reasoning hybrid algorithm to solve the adaptation problem in Public Data Center of heterogeneous system, based on the research in intelligent data processing method. Using adapters dynamic data monitoring function, first, pick out all abnormal data. then, apply the hybrid algorithm on the abnormal data. finally, recover the abnormal data and report abnormal processing result. This method has been applied to many domestic universitiess intelligent data exchange system in the Public Data Center. Through practice, this al

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Pan, Yicheng, Yang Zhang, Tingzhu Bi, et al. "HEAL: Performance Troubleshooting Deep inside Data Center Hosts." ACM SIGMETRICS Performance Evaluation Review 52, no. 1 (2024): 41–42. http://dx.doi.org/10.1145/3673660.3655058.

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This study demonstrates the salient facts and challenges of host failure operations in hyperscale data centers. A host incident can involve hundreds of distinct host-level metrics. The faulting mechanism inside the host connects these heterogeneous metrics through direct and indirect correlation, making it extremely difficult to sort out the propagation procedures and the root cause from these intertwined indicators. To deeply understand the failure mechanism inside the host, we develop HEAL -- a novel host metrics analysis toolkit. HEAL discovers dynamic causality in sparse heterogeneous host

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An, Yin. "Research of Heterogeneous Data Integration in Ship Design." Advanced Engineering Forum 6-7 (September 2012): 221–25. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.221.

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The China Ship Development and Design Center has built several separate database systems in the design center and the working groups. However, the data cannot be shared and exchanged due to no network connection as the secrecy provision limited. The paper presents the data integration method based on the XML to implement the data integration between the heterogeneous databases, which makes the data interactive convenient. The key technologies included the mapping from database to XML documents, the XML documents importing to database, querying and displaying XML documents were analyzed. The SQ

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Pan, Yicheng, Yang Zhang, Tingzhu Bi, et al. "HEAL: Performance Troubleshooting Deep inside Data Center Hosts." Proceedings of the ACM on Measurement and Analysis of Computing Systems 7, no. 3 (2023): 1–24. http://dx.doi.org/10.1145/3626785.

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This study demonstrates the salient facts and challenges of host failure operations in hyperscale data centers. A host incident can involve hundreds of distinct host-level metrics, covering broad aspects. The faulting mechanism inside the host connects these heterogeneous metrics through direct and indirect correlation, making it extremely difficult to sort out the propagation procedures and the root cause from these intertwined indicators. To deeply understand the failure mechanism inside the host, we develop HEAL -- a novel host metrics analysis toolkit. HEAL synergistically discovers dynami

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Lanot, Antoine, Clémence Bechade, Christian Verger, Emmanuel Fabre, Isabelle Vernier, and Thierry Lobbedez. "Clusters of Practice in Peritoneal Dialysis in France: Data from the Catheter Section of the RDPLF." Peritoneal Dialysis International: Journal of the International Society for Peritoneal Dialysis 38, no. 2 (2017): 89–97. http://dx.doi.org/10.3747/pdi.2017.00135.

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Background Peritonitis is a major cause of peritoneal dialysis (PD) failure. Recommendations for the prevention of peritonitis are available, but wide variations exist in the peritonitis rate among countries and PD units. The objective of this study was to describe the different pattern of practices in France. Methods This was a retrospective, multicenter study based on data from the French Language Peritoneal Dialysis Registry. Center practices were described and mapped. Clusters of practices were sought in a hierarchical analysis and centers belonging to the same clusters of practices were m

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Ma, Lisheng, Wei Su, Xiaozhou Li, Bin Wu, and Xiaohong Jiang. "Heterogeneous Data Backup Against Early Warning Disasters in Geo-Distributed Data Center Networks." Journal of Optical Communications and Networking 10, no. 4 (2018): 376. http://dx.doi.org/10.1364/jocn.10.000376.

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Mao, Li, De Yu Qi, Wei Wei Lin, Bo Liu, and Ye Da Li. "An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization." International Journal of Grid and High Performance Computing 8, no. 2 (2016): 43–57. http://dx.doi.org/10.4018/ijghpc.2016040103.

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With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the s

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Tang, Xiaochun, Ying Fu, and Xuefeng Fan. "Fine-Grained Allocation Algorithm for Sharing Heterogeneous Resources in Data Center." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 3 (2020): 589–95. http://dx.doi.org/10.1051/jnwpu/20203830589.

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Data in a data center are stored dispersively. The data-oriented task computing disperses big data analysis tasks to different computing nodes. The extensive use of graphics processing unit (GPU) makes it urgent and important to study how to reasonably assign heterogeneous resources to different computing frameworks. We investigate the existing big data computing framework and the GPU computing. Based on the existing cluster resource management model and the GPU management model, we propose a hybrid heterogeneous resource management model that combines CPU resources with GPU resources. The com

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Li, Keqin. "Optimal power allocation among multiple heterogeneous servers in a data center." Sustainable Computing: Informatics and Systems 2, no. 1 (2012): 13–22. http://dx.doi.org/10.1016/j.suscom.2011.11.002.

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Ndayikengurukiye, Aristide, Abderrahmane Ez-Zahout, and Fouzia Omary. "Optimizing Virtual Machines Placement in a Heterogeneous Cloud Data Center System." International Journal of Computer Networks and Applications 11, no. 1 (2024): 1. http://dx.doi.org/10.22247/ijcna/2024/224431.

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Liu, Jun, Longchuan Yan, Chengxu Yan, et al. "Escope: An Energy Efficiency Simulator for Internet Data Centers." Energies 16, no. 7 (2023): 3187. http://dx.doi.org/10.3390/en16073187.

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Contemporary megawatt-scale data centers have emerged to meet the increasing demand for online cloud services and big data analytics. However, in such large-scale data centers, servers of different generations are installed gradually year by year, making the data center heterogeneous in computing capability and energy efficiency. Furthermore, due to different processor architectures, complex and diverse load dynamic changing, business coupling, and other reasons, operators pay great attention to processor hardware power consumption and server aggregation energy efficiency. Therefore, the simul

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Ma, Jiang, Lei Gao, and Ai Hua Ding. "Research on the Heterogeneous Data Integration Platform Based on Extended ETL and Data Federate." Advanced Materials Research 171-172 (December 2010): 565–69. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.565.

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After analyzing the main way of data integration , a compound solution based on extended ETL and data federate is advanced to integrate heterogeneous data and build shared data center. Research shows that the new method can not only guarantee real-time property of data transmission、decrease spending, but also remove bottleneck as well as potential safety hazard and improve the overall performance.

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HU, Xiaoxuan, Peng Li, and Yanfei Sun. "Minimizing Energy Cost for Green Data Center by Exploring Heterogeneous Energy Resource." Journal of Modern Power Systems and Clean Energy 9, no. 1 (2021): 148–59. http://dx.doi.org/10.35833/mpce.2019.000052.

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Dong, Fang, Xiaolin Guo, Pengcheng Zhou, and Dian Shen. "Task-aware flow scheduling with heterogeneous utility characteristics for data center networks." Tsinghua Science and Technology 24, no. 4 (2019): 400–411. http://dx.doi.org/10.26599/tst.2018.9010122.

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Chang, Xiaolin, Ruofan Xia, Jogesh K. Muppala, Kishor S. Trivedi, and Jiqiang Liu. "Effective Modeling Approach for IaaS Data Center Performance Analysis under Heterogeneous Workload." IEEE Transactions on Cloud Computing 6, no. 4 (2018): 991–1003. http://dx.doi.org/10.1109/tcc.2016.2560158.

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Mishra, Vaibhawa, Joshua L. Benjamin, and Georgios Zervas. "MONet: heterogeneous Memory over Optical Network for large-scale data center resource disaggregation." Journal of Optical Communications and Networking 13, no. 5 (2021): 126. http://dx.doi.org/10.1364/jocn.419145.

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Wang, Jia, Xiaoping Li, Rubén Ruiz, Hanchuan Xu, and Dianhui Chu. "Allocating MapReduce workflows with deadlines to heterogeneous servers in a cloud data center." Service Oriented Computing and Applications 14, no. 2 (2020): 101–18. http://dx.doi.org/10.1007/s11761-020-00290-1.

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AL-Azawee, Sanarya Jamal, and Nadia Adnan Shiltagh Al-Jamali. "Handling Heterogeneous Traffic for Software Defined Data-Center Network Using Spike Neural Network." Journal of Engineering 31, no. 5 (2025): 21–34. https://doi.org/10.31026/j.eng.2025.05.02.

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Software Defined Networking (SDN) allows for more flexible network administration than traditional architectures. Software-defined networks (SDNs) efficiently manage data flows and optimize network resources. However, heterogeneity influences the quality of the services. (QoS) needs and network resource demands. They behave differently when traveling to their last point. Currently, numerous data center networks (DCNs) struggle with the unfair use of several network resources by big packets (Elephant flowing) arriving during any instant affecting specific flows (mice flow). Elephant Flows (EF)

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Huang, Haiyang, and Zhanlei Shang. "Fast mining method of network heterogeneous fault tolerant data based on K-means clustering." Web Intelligence 19, no. 1-2 (2021): 115–24. http://dx.doi.org/10.3233/web-210460.

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In the traditional network heterogeneous fault-tolerant data mining process, there are some problems such as low accuracy and slow speed. This paper proposes a fast mining method based on K-means clustering for network heterogeneous fault-tolerant data. The confidence space of heterogeneous fault-tolerant data is determined, and the range of motion of fault-tolerant data is obtained; Singular value decomposition (SVD) method is used to construct the classified data model to obtain the characteristics of heterogeneous fault-tolerant data; The redundant data in fault-tolerant data is deleted by

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Ronchieri, Elisabetta, Luca Giommi, Luigi Benedettto Scarponi, et al. "Anomaly Detection in Data Center IT & Physical Infrastructure." EPJ Web of Conferences 295 (2024): 07004. http://dx.doi.org/10.1051/epjconf/202429507004.

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Anomaly detection in data center IT and physical infrastructure is challenging due to the amount of heterogeneous data to be analyzed. Defining a solution that early identifies unexpected anomalies is particularly important to prevent data losses, breakdown of the system, and any other event considered to be critical for the activity of the data center. In the context of the INFN CNAF data center, one of the WLCG Tier-1s, we have performed a study based on monitored cooling, electrical, and IT hardware and software metrics to identify anomalies. In the present work, we aim to explore statistic

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Nurhendratno, Slamet Sudaryanto, and Sudaryanto Sudaryanto. "DATA INTEGRATION MODEL DESIGN FOR SUPPORTING DATA CENTER PATIENT SERVICES DISTRIBUTED INSURANCE PURCHASE WITH VIEW BASED DATA INTEGRATION." Computer Engineering, Science and System Journal 3, no. 2 (2018): 162. http://dx.doi.org/10.24114/cess.v3i2.8895.

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Data integration is an important step in integrating information from multiple sources. The problem is how to find and combine data from scattered data sources that are heterogeneous and have semantically informant interconnections optimally. The heterogeneity of data sources is the result of a number of factors, including storing databases in different formats, using different software and hardware for database storage systems, designing in different data semantic models (Katsis & Papakonstantiou, 2009, Ziegler & Dittrich , 2004). Nowadays there are two approaches in doing data integr

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Gou, Xiaopeng, Qi Ge, Quan Guo, et al. "Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage." Applied Sciences 15, no. 9 (2025): 5050. https://doi.org/10.3390/app15095050.

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DNA data storage has emerged as a promising alternative to traditional storage media due to its high density and durability. However, large-scale DNA storage systems generate massive sequencing reads, posing substantial computational complexity and latency challenges for data readout. Here, we propose a novel heterogeneous computing architecture based on a field-programmable gate array (FPGA) to accelerate DNA data readout. The software component, running on a general computing platform, manages data distribution and schedules acceleration kernels. Meanwhile, the hardware acceleration kernel i

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Osama, Mayada, Salwa El Ramly, and Bassant Abdelhamid. "Interference Mitigation and Power Minimization in 5G Heterogeneous Networks." Electronics 10, no. 14 (2021): 1723. http://dx.doi.org/10.3390/electronics10141723.

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Macro cells’ (MCs) densification with small cells (SCs) is one of the promising solutions to cope with the increasing demand for higher data rates in 5G heterogeneous networks (HetNets). Unfortunately, the interference that arises between these densely deployed SCs and their elevated power consumption have caused huge problems facing the 5G HetNets. In this paper, a new soft frequency reuse (SFR) scheme is proposed to minimize the interference and elevate the network throughput. The proposed scheme is based on on/off switching the SCs according to their interference contribution rate (ICR) val

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Jamal, M. Hasan, M. Tayyab Chaudhry, Usama Tahir, Furqan Rustam, Soojung Hur, and Imran Ashraf. "Hotspot-Aware Workload Scheduling and Server Placement for Heterogeneous Cloud Data Centers." Energies 15, no. 7 (2022): 2541. http://dx.doi.org/10.3390/en15072541.

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Data center servers located in thermal hotspot regions receive inlet air at a higher than the set temperature and thus generate comparatively high outlet temperature. Consequently, there is a rise in energy that is consumed to cool down the servers that otherwise would undergo reliability hazards. The workload deployment across the servers should be resilient to thermal hotspots to ensure smooth performance. In a heterogeneous data center environment, an equally important fact is the placement of the servers in a thermal hotspot-aware manner to lower the peak outlet temperatures. These approac

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Bayati, Marziyeh. "Power Management Policy for Heterogeneous Data Center Based on Histogram and Discrete-Time MDP." Electronic Notes in Theoretical Computer Science 337 (May 2018): 5–22. http://dx.doi.org/10.1016/j.entcs.2018.03.031.

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Maytham, N. Meqdad, Hasan Hussein Abdullah, O. Husain Saif, and Mohammed Jawad Alyaa. "Classification of electrocardiogram signals based on federated learning and a gaussian multivariate aggregation module." Classification of electrocardiogram signals based on federated learning and a gaussian multivariate aggregation module 30, no. 2 (2023): 936–43. https://doi.org/10.11591/ijeecs.v30.i2.pp936-943.

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Categorization of cardiac abnormalities received from several centers is not possible within the quickest time because of privacy and security restrictions. Today, individuals’ security problem is considered as one of the most important research fields in most research sciences. This study provides a novel approach for detection of cardiac abnormalities based on federated learning (FL). This approach addresses the challenge of accessing data from remote centers and presents the possibility of learning without the need for transferring data from the main center. We present a novel aggrega

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Ilchev, Svetozar, Rumen Andreev, and Zlatoliliya Ilcheva. "Heterogeneous IoT Platform for Device Management and Environmental Sensor Data Gathering." Serdica Journal of Computing 12, no. 1-2 (2018): 23–46. http://dx.doi.org/10.55630/sjc.2018.12.23-46.

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In this paper, we propose a new IoT platform which supports the use of different wired and radio connections to manage devices and gather environmental sensor data. We discuss the pilot implementation of our new platform which deals with the smart control of air-conditioning devices in shopping malls. The building blocks of the platform - two types of electronic controllers of our own design - are described in detail. The Internet connectivity of the platform enables the use of an Internet-based control center for storing historical data, performing statistical analyses, making automated corre

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Asperti, Andrea, Gabriele Raciti, Elisabetta Ronchieri, and Daniele Cesini. "Machine Learning-Based Anomaly Prediction for Proactive Monitoring in Data Centers: A Case Study on INFN-CNAF." Applied Sciences 15, no. 2 (2025): 655. https://doi.org/10.3390/app15020655.

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Anomaly prediction in time series is crucial for ensuring the stability and security of data centers, especially in scientific contexts such as INFN-CNAF, the National Center for Research and Development in Information and Communication Technology of the National Institute for Nuclear Physics. At INFN-CNAF, large volumes of heterogeneous data critical to international experiments are managed using dedicated monitoring systems. To ensure continuous availability, artificial intelligence solutions are being explored to detect anomalies and predict potential failures proactively. This work present

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Jäkel, René, Eric Peukert, Wolfgang E. Nagel, and Erhard Rahm. "ScaDS Dresden/Leipzig – A competence center for collaborative big data research." it - Information Technology 60, no. 5-6 (2018): 327–33. http://dx.doi.org/10.1515/itit-2018-0026.

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Abstract The efficient and intelligent handling of large, often distributed and heterogeneous data sets increasingly determines the scientific and economic competitiveness in most application areas. Mobile applications, social networks, multimedia collections, sensor networks, data intense scientific experiments, and complex simulations nowadays generate a huge data deluge. Nonetheless, processing and analyzing these data sets with innovative methods open up new opportunities for its exploitation and new insights. Nevertheless, the resulting resource requirements exceed usually the possibiliti

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Dal Pra, Stefano, Daniele Spiga, Tommaso Boccali, et al. "Enabling INFN–T1 to support heterogeneous computing architectures." EPJ Web of Conferences 295 (2024): 11006. http://dx.doi.org/10.1051/epjconf/202429511006.

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The INFN–CNAF Tier-1 located in Bologna (Italy) is a center of the WLCG e-Infrastructure providing computing power to the four major LHC collaborations and also supports the computing needs of about fifty more groups - also from non HEP research domains. The CNAF Tier1 center has been historically very active putting effort in the integration of computing resources, proposing and prototyping solutions both for extension through Cloud resources, public and private, and with remotely owned sites, as well as developing an integrated HTC+HPC system with the PRACE CINECA supercomputer center locate

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Zhu, Yongjie, and Shenzhan Feng. "A Matching Method of Heterogeneous Database based on SOM and BP Neural Network." International Journal of Circuits, Systems and Signal Processing 15 (April 22, 2021): 383–92. http://dx.doi.org/10.46300/9106.2021.15.42.

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In the process of data integration among heterogeneous databases, it is significantly important to analyze the identical attributes and characteristics of the databases. However, the existing main data attribute matching model has the defects of oversize matching space and low matching precision. Therefore, this paper puts forward a heterogeneous data attribute matching model on the basis of fusion of SOM and BP network through analyzing the attribute matching process of heterogeneous databases. This model firstly matches the heterogeneous data attributes in advance by SOM network to determine

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Yu, Liang, Tao Jiang, Yang Cao, and Qi Qi. "Joint Workload and Battery Scheduling with Heterogeneous Service Delay Guaranteesfor Data Center Energy Cost Minimization." IEEE Transactions on Parallel and Distributed Systems 26, no. 7 (2015): 1937–47. http://dx.doi.org/10.1109/tpds.2014.2329491.

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Ahn, Sang Un, and Jin Kim. "A Conceptual Design of Job Pre-processing Flow for Heterogeneous Batch Systems in Data Center." Wireless Personal Communications 89, no. 3 (2016): 847–61. http://dx.doi.org/10.1007/s11277-016-3224-x.

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Jin, Gang, Peng Zhang, Lei Liu, and Xiang Yu Meng. "A Virtual Machine Scheduling Strategy Based on Grouping Genetic Algorithm in Cloud Environment." Applied Mechanics and Materials 411-414 (September 2013): 203–6. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.203.

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With the emergence of cloud computing, data center is becoming resource pooling and the applications are becoming multi-tenant and heterogeneous which make the server resource load balancing problem become more and more important. In order to resolve contradiction between the heterogeneous applications and sharing resource pool, we think this problem as a multi-dimensional variable vector packing model. For the NP-hard feature, we design a virtual machine scheduling strategy and it resolve the data center resource balancing problem. The experiment indicated that the algorithm can reduce the nu

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Ji, Xin, Da Hua Zhang, and De Yue Men. "Research on Heterogeneous Cloud Service Model for Smart Grid Business Application." Advanced Materials Research 860-863 (December 2013): 2427–33. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2427.

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With the construction of smart grid and centralized data center, traditional information technology management mode already cannot meet the requirements on the timeliness and flexibility. Cloud computing technology can solve the complicated calculation, mass data processing, and network dynamic migration issues for electric power business, but when in the face of different business applications demand, due to the heterogeneous resources variety of applications bottleneck will be produced, difficult to achieve real "smart". In view of the smart grid application scenarios, heterogeneous resource

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Zhao, Jia Kui, Lian Shun Mu, Hong Ouyang, et al. "In-Network Time-Series Data Compression for Electric Internet of Things." Applied Mechanics and Materials 241-244 (December 2012): 3213–23. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.3213.

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The IoT is considered as one of the most important supporting technologies of the smart grid and the smart grid is considered as one of the most important application areas of the IoT. However, the electric IoT has vast amount and many kinds of sensors, very high data acquisition frequency, and a highly heterogeneous network, which lead to the challenge that if the raw time-series data gathered by sensors is all transmitted to the sensing data center via network and then compressed and reserved, the bandwidth and the computing resource requirements of the network and the sensing data center, r

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Choudhury, Rebecca, Ronald Beaulieu, Thomas Talbot, and George Nelson. "Clinical Team Distribution and Antibiotic Use Patterns at a Tertiary-Care Academic Medical Center." Infection Control & Hospital Epidemiology 41, S1 (2020): s168—s169. http://dx.doi.org/10.1017/ice.2020.695.

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Background: As more US hospitals report antibiotic utilization to the CDC, standardized antimicrobial administration ratios (SAARs) derived from patient care unit-based antibiotic utilization data will increasingly be used to guide local antibiotic stewardship interventions. Location-based antibiotic utilization surveillance data are often utilized given the relative ease of ascertainment. However, aggregating antibiotic use data on a unit basis may have variable effects depending on the number of clinical teams providing care. In this study, we examined antibiotic utilization from units at a

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Ren, Jie, and Lijuan Liu. "A Study on Information Classification and Storage in Cloud Computing Data Centers Based on Group Collaborative Intelligent Clustering." Journal of Electrical and Computer Engineering 2022 (March 25, 2022): 1–11. http://dx.doi.org/10.1155/2022/1476661.

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Internet of things (IoT) and cloud computing are combined to form a cloud computing data center, and cloud computing provides virtualization, storage, computing, and other support services for IoT applications. Data is the foundation and core of cloud IoT platform applications, and massive multisource heterogeneous IoT data aggregation and storage have basic requirements such as real-time, security, and scalability. This paper focuses on the aggregation and storage methods of massive heterogeneous cloud IoT data, solving the multisource data aggregation problem caused by inconsistent protocols

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Han, Yue, Xiaolei Deng, Junjian Zheng, Xiaoliang Lin, Xuanyi Wang, and Yong Chen. "Thermal Error Prediction for Vertical Machining Centers Using Decision-Level Fusion of Multi-Source Heterogeneous Information." Machines 12, no. 8 (2024): 509. http://dx.doi.org/10.3390/machines12080509.

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To address the limitations in predictive capabilities of thermal error models built from single-source, single-structure data, this paper proposes a thermal error prediction model based on decision-level fusion of multi-source heterogeneous information to enhance prediction accuracy. First, an experimental platform for multi-source heterogeneous information acquisition was constructed to collect thermal error data from different signal sources (multi-source) and different structures (heterogeneous). Next, based on the characteristics of the multi-source and heterogeneous data, relevant feature

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Chen, Yanping, Mingdao Zhao, Hong Xia, Xiaodong Jin, Zhongmin Wang, and Zhong Yu. "A Method for Extracting High-Quality Core Data from Edge Computing Nodes." Mathematical Problems in Engineering 2019 (June 12, 2019): 1–10. http://dx.doi.org/10.1155/2019/3834846.

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Intelligent factory has the characteristics of wide data sources, high data dimensions, and strong data relevance. Intelligent factories need to make different decisions for different needs, so they need to efficiently analyze these data and explore the inherent laws contained in them. At the same time, the increasing amount of data brings various burdens to the network infrastructure between users and smart devices. For the above needs, this paper proposes a tension-based heterogeneous data fusion model in the edge computing layer, which represents the multisource heterogeneous data in the in

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