• Title/Summary/Keyword: Resource Placement

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A Study on the Integrated System of the Hotel Employment Management and the Determinants of the Employment (호텔종사원의 통합적 채용관리 시스템과 채용결정요인에 대한 연구 - 채용 전문가와 지원자 간의 차이분석 -)

  • Kim, U-Jin
    • Journal of Applied Tourism Food and Beverage Management and Research
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    • v.17 no.2
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    • pp.61-94
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    • 2006
  • This study analyzes the integrated system of the hotel employment management and the differences between the persons of employment concerning and the applicants about the importance of the determinants of the employment around the five-star hotels in Seoul. Firstly, the integrated strategies are presented by 3 stages such as recruiting, selection and placement. And then the 15 determinants of the employment are derived to analyze the perceptional differences between the persons of employment concerning and the applicants about the important determinants and to present the reasons and strategical implications. The results of this study indicates that the strategy to make him/her aware of sufficient information about the hotel and job through the proper balancing demand and supply and the industry-university cooperation program order to maintain and utilize the human resource to be aligned with the business performance of the hotel.

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Modified Deep Reinforcement Learning Agent for Dynamic Resource Placement in IoT Network Slicing

  • Ros, Seyha;Tam, Prohim;Kim, Seokhoon
    • Journal of Internet Computing and Services
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    • v.23 no.5
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    • pp.17-23
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    • 2022
  • Network slicing is a promising paradigm and significant evolution for adjusting the heterogeneous services based on different requirements by placing dynamic virtual network functions (VNF) forwarding graph (VNFFG) and orchestrating service function chaining (SFC) based on criticalities of Quality of Service (QoS) classes. In system architecture, software-defined networks (SDN), network functions virtualization (NFV), and edge computing are used to provide resourceful data view, configurable virtual resources, and control interfaces for developing the modified deep reinforcement learning agent (MDRL-A). In this paper, task requests, tolerable delays, and required resources are differentiated for input state observations to identify the non-critical/critical classes, since each user equipment can execute different QoS application services. We design intelligent slicing for handing the cross-domain resource with MDRL-A in solving network problems and eliminating resource usage. The agent interacts with controllers and orchestrators to manage the flow rule installation and physical resource allocation in NFV infrastructure (NFVI) with the proposed formulation of completion time and criticality criteria. Simulation is conducted in SDN/NFV environment and capturing the QoS performances between conventional and MDRL-A approaches.

A Study on Production Well Placement for a Gas Field using Artificial Neural Network (인공신경망 시뮬레이터를 이용한 가스전 생산정 위치선정 연구)

  • Han, Dong-Kwon;Kang, Il-Oh;Kwon, Sun-Il
    • Journal of the Korean Institute of Gas
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    • v.17 no.2
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    • pp.59-69
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    • 2013
  • This study presents development of the ANN simulator for well placement of infill drilling in gas fields. The input data of the ANN simulator includes the production time, well location, all inter well distances, boundary inter well distance, infill well position, productivity potential, functional links, reservoir pressure. The output data includes the bottomhole pressure in addition to the production rate. Thus, it is possible to calculate the productivity and bottomhole pressure during production period simultaneously, and it is expected that this model could replace conventional simulators. Training for the 20 well placement scenarios was conducted. As a result, it was found that accuracy of ANN simulator was high as the coefficient of correlation for production rate was 0.99 and the bottomhole pressure 0.98 respectively. From the resultes, the validity of the ANN simulator has been verified. The term, which could produce Maximum Daily Quantity (MDQ) at the gas field and the productivity according to the well location was analyzed. As a result, the MDQ could be maintained for a short time in scenario C-1, which has the three infill wells nearby aquifer boundary, and a long time in scenario A-1. In conclusion, it was found that scenario A maintained the MDQ up to 21% more than those of scenarios B and C which include parameters that might affect the productivity. Thus, the production rate can be maximized by selecting the location of production wells in comprehensive consideration of parameters that may affect the productivity. Also, because the developed ANN simulator could calculate both production rate and bottomhole pressure, respectively, it could be used as the forward simulator in a various inverse model.

Prioeitization of domain dependent KR techniques using the combined AHP

  • Byun, Daeho;Jung, Kiho
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 1996.04a
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    • pp.421-424
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    • 1996
  • To provide an appropriate knowledge representation technique dependent on a particular domain, we consider the combine analytic hierachy process(CAHP). This is an extended method of the conventional AHP which is useful when two different expert groups are involved. Our problem domain is confined to human resource management including such major activities as planning, selection, placement, compensations, performance evaluation, training, and labor-management relations. We prioritize rules, frames, semantic nets, and predicate logic representation techniques best suited to each and all domains through an exploratory study.

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Efficient Virtual Machine Placement Considering System Load (시스템 부하를 고려한 효율적인 가상 머신 배치)

  • Jung, Sungmin
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.16 no.2
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    • pp.35-43
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    • 2020
  • Cloud computing integrates computing resources such as servers, storage, and networks with virtualization technology to provide suitable services according to user needs. Due to the structural characteristics of sharing physical resources based on virtualization technology, threats to availability can occur, so it is essential to respond to availability threats in cloud computing. Existing over-provisioning method is not suitable because it can generate idle resources and cause under-provisioning to degrade or disconnect service. System resources must be allocated in real-time according to the system load to guarantee the cloud system's availability. Through appropriate management measures, it is necessary to reduce the system load and increase the performance of the system. This paper analyzes the work response time according to the allocation or migration of virtual machines and discusses an efficient resource management method considering the system load.

Extracting optimal moving patterns of edge devices for efficient resource placement in an FEC environment (FEC 환경에서 효율적 자원 배치를 위한 엣지 디바이스의 최적 이동패턴 추출)

  • Lee, YonSik;Nam, KwangWoo;Jang, MinSeok
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.1
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    • pp.162-169
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    • 2022
  • In a dynamically changing time-varying network environment, the optimal moving pattern of edge devices can be applied to distributing computing resources to edge cloud servers or deploying new edge servers in the FEC(Fog/Edge Computing) environment. In addition, this can be used to build an environment capable of efficient computation offloading to alleviate latency problems, which are disadvantages of cloud computing. This paper proposes an algorithm to extract the optimal moving pattern by analyzing the moving path of multiple edge devices requiring application services in an arbitrary spatio-temporal environment based on frequency. A comparative experiment with A* and Dijkstra algorithms shows that the proposed algorithm uses a relatively fast execution time and less memory, and extracts a more accurate optimal path. Furthermore, it was deduced from the comparison result with the A* algorithm that applying weights (preference, congestion, etc.) simultaneously with frequency can increase path extraction accuracy.

Resource and Network Routing Optimization in Smart Nodes Environment (스마트 네트워크 환경에서의 자원 및 경로 최적화 연구)

  • Seo, Dong-Won;Yoon, Seung Hyun;Chang, Byeong-Yoon
    • Journal of the Korea Society for Simulation
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    • v.22 no.4
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    • pp.149-156
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    • 2013
  • In this research, we dealt with an optimization problem which aims to minimize total cost of resources usage and network routing in a smart node environment. Toward this, we briefly introduced technology trends in smart nodes, methods in resource optimization fields, economic effects of smart network and content delivery (or distribution) network (CDN). Moreover, based on CDN we proposed and implemented a mathematical (mixed integer) programming model to combine replica placement and requests distribution problem (RPRDP) and routing problem. Computational result of an example on RPRDP+Routing problem is also provided.

A Study on Knowledge Representation Schemes for Use in Human Resource Management Problem Domains (인적자원관리 분야의 지식표현체계에 관한 연구)

  • Byeon, Dae-Ho
    • Asia pacific journal of information systems
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    • v.7 no.1
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    • pp.85-97
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    • 1997
  • This paper is concerned with knowledge representation schemes best suited for human resource management (HRM) problem domains including human resource planing, selection, placement, compensations, performance evaluation, training and labor-management relations. In order to suggest the scheme we consider two research gods. First, we evaluate and prioritize. The knowledge representation techniques of frames rules, semantic nets and predicate logic that hove been recommended to managerial domains. The combined Analytic Hierarchy Process technique is employed to combine individual judgments effectively between two different expert groups. As a result if we are to select a single knowledge representation technique, a frame representation is best for most HRM domains and to combine frames with others is another choice. Second as a strategy for knowledge representation schemes we show some examples for each damn in terms of labeled semantic nets and two types of rules derived from the semantic nets. We propose nine knowledge components as ontologies. The labeled semantic nets con provide some benefits compared with conventional one. More clearly definea node rode information maces it easy to find the ac information. In the rule sets, the variables are the node of the semantic nets. The consistency of rules is validated by the relationship of the knowledge components.

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Automatic Layout Design of CMOL FPGA (CMOL FPGA 자동 레이아웃 설계)

  • Kim, Kyo-Sun
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.44 no.11
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    • pp.56-64
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    • 2007
  • We developed the first automatic design system targeting a promising hybrid CMOS-Nanoelectronics Architecture called CMOL. The CMOL architecture uses NOR gates to implement combinational logic. In this hybrid CMOS-nanoelectronics architecture, logical functions and the interconnections share the nanoelectronics hardware resource. Towards automating the CMOL physical design process, we developed a model for the CMOL architecture, formulated the placement and routing problems for the CMOL architecture subject to the unique CMOL specific constraints, and solved it by combining a placement algorithm with a gate assignment algorithm in a loop. We validated the proposed approach by implementing several industrial strength designs.

High-revenue Online Provisioning for Virtual Clusters in Multi-tenant Cloud Data Center Network

  • Lu, Shuaibing;Fang, Zhiyi;Wu, Jie
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.3
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    • pp.1164-1183
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    • 2019
  • The rapid development of cloud computing and high requirements of operators requires strong support from the underlying Data Center Networks. Therefore, the effectiveness of using resources in the data center networks becomes a point of concern for operators and material for research. In this paper, we discuss the online virtual-cluster provision problem for multiple tenants with an aim to decide when and where the virtual cluster should be placed in a data center network. Our objective is maximizing the total revenue for the data center networks under the constraints. In order to solve this problem, this paper divides it into two parts: online multi-tenancy scheduling and virtual cluster placement. The first part aims to determine the scheduling orders for the multiple tenants, and the second part aims to determine the locations of virtual machines. We first approach the problem by using the variational inequality model and discuss the existence of the optimal solution. After that, we prove that provisioning virtual clusters for a multi-tenant data center network that maximizes revenue is NP-hard. Due to the complexity of this problem, an efficient heuristic algorithm OMS (Online Multi-tenancy Scheduling) is proposed to solve the online multi-tenancy scheduling problem. We further explore the virtual cluster placement problem based on the OMS and propose a novel algorithm during the virtual machine placement. We evaluate our algorithms through a series of simulations, and the simulations results demonstrate that OMS can significantly increase the efficiency and total revenue for the data centers.