scholarly journals Energy Proportionality in Heterogeneous Data Center Supporting Applications with Variable Load

2016 ◽  
Author(s):  
Violaine Villebonnet ◽  
Georges Da Costa ◽  
Laurent Lefevre ◽  
Jean-Marc Pierson ◽  
Patricia Stolf
Keyword(s):  
Data Center ◽  
Heterogeneous Data ◽  
Variable Load ◽  
Energy Proportionality

scholarly journals A Method for Extracting High-Quality Core Data from Edge Computing Nodes

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Yanping Chen ◽  
Mingdao Zhao ◽  
Hong Xia ◽  
Xiaodong Jin ◽  
Zhongmin Wang ◽  
...  
Keyword(s):  
Data Center ◽  
Heterogeneous Data ◽  
Edge Computing ◽  
Smart Devices ◽  
Tensor Model ◽  
Fusion Model ◽  
High Quality ◽  
Data Set ◽  
Core Data ◽  
High Data

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 industrial scene as a tensor model, and uses the incremental decomposition algorithm to extract high-quality core data. The model reduces the data flow between the data center and the central cloud while retaining the core data set. Experiments show that the approximate tensor reconstructed from the tensor with 15% core data can guarantee 90% accuracy.

DATA INTEGRATION MODEL DESIGN FOR SUPPORTING DATA CENTER PATIENT SERVICES DISTRIBUTED INSURANCE PURCHASE WITH VIEW BASED DATA INTEGRATION

2018 ◽  
Vol 3 (2) ◽  
pp. 162
Author(s):  
Slamet Sudaryanto Nurhendratno ◽  
Sudaryanto Sudaryanto
Keyword(s):  
Data Integration ◽  
Data Center ◽  
Data Exchange ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Query Rewriting ◽  
Schema Mapping ◽  
Data Interoperability ◽  
Integration Model ◽  
Data Source

 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 integration that is Global as View (GAV) and Local as View (LAV), but both have different advantages and limitations so that proper analysis is needed in its application. Some of the major factors to be considered in making efficient and effective data integration of heterogeneous data sources are the understanding of the type and structure of the source data (source schema). Another factor to consider is also the view type of integration result (target schema). The results of the integration can be displayed into one type of global view or a variety of other views. So in integrating data whose source is structured the approach will be different from the integration of the data if the data source is not structured or semi-structured. Scheme mapping is a specific declaration that describes the relationship between the source scheme and the target scheme. In the scheme mapping is expressed in in some logical formulas that can help applications in data interoperability, data exchange and data integration. In this paper, in the case of establishing a patient referral center data center, it requires integration of data whose source is derived from a number of different health facilities, it is necessary to design a schema mapping system (to support optimization). Data Center as the target orientation schema (target schema) from various reference service units as a source schema (source schema) has the characterization and nature of data that is structured and independence. So that the source of data can be integrated tersetruktur of the data source into an integrated view (as a data center) with an equivalent query rewriting (equivalent). The data center as a global schema serves as a schema target requires a "mediator" that serves "guides" to maintain global schemes and map (mapping) between global and local schemes. Data center as from Global As View (GAV) here tends to be single and unified view so to be effective in its integration process with various sources of schema which is needed integration facilities "integration". The "Pemadu" facility is a declarative mapping language that allows to specifically link each of the various schema sources to the data center. So that type of query rewriting equivalent is suitable to be applied in the context of query optimization and maintenance of physical data independence.Keywords: Global as View (GAV), Local as View (LAV), source schema ,mapping schema

Balancing Global and Local Fairness Allocation Model in Heterogeneous Data Center

2017 ◽  
pp. 136-139
Author(s):  
Bingyu Zhou ◽  
Guangjun Qin ◽  
Limin Xiao ◽  
Zhisheng Huo ◽  
Bing Wei ◽  
...  
Keyword(s):  
Data Center ◽  
Heterogeneous Data ◽  
Allocation Model ◽  
Global And Local

scholarly journals Thermal-Aware Virtual Machine Allocation for Heterogeneous Cloud Data Centers

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2880
Author(s):  
Abbas Akbari ◽  
Ahmad Khonsari ◽  
Seyed Mohammad Ghoreyshi
Keyword(s):  
Energy Consumption ◽  
Resource Management ◽  
Virtual Machine ◽  
Data Center ◽  
Data Centers ◽  
Greedy Algorithms ◽  
Heterogeneous Data ◽  
Management Problem ◽  
Vm Allocation ◽  
Machine Allocation

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 Machine (VM) allocation heuristic based on the genetic algorithm. Experimental results show that, using the proposed formulation, up to 30 % energy saving is achieved compared to thermal-aware greedy algorithms and power-aware VM allocation heuristics.

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