A Policy-based Resource Management Framework for QoS Improvement of Mobile Terminals in heterogeneous Network

In this introduction to the following series of papers on Bayesian belief networks (BBNs) we briefly summarize BBNs, review their application in ecology and natural resource management, and provide an overview of the papers in this section. We suggest that BBNs are useful tools for representing expert knowledge of an ecosystem, evaluating potential effects of alternative management decisions, and communicating with nonexperts about making natural resource management decisions. BBNs can be used effectively to represent uncertainty in understanding and variability in ecosystem response, and the influence of uncertainty and variability on costs and benefits assigned to model outcomes or decisions associated with natural resource management. BBN tools also lend themselves well to an adaptive-management framework by posing testable management hypotheses and incorporating new knowledge to evaluate existing management guidelines.

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UDARMF: An Underwater Distributed and Adaptive Resource Management Framework

IEEE Internet of Things Journal ◽

10.1109/jiot.2021.3097493 ◽

2021 ◽

pp. 1-1

Author(s):

Tong Zhang ◽

Yu Gou ◽

Jun Liu ◽

Tingting Yang ◽

Jun-Hong Cui

Keyword(s):

Resource Management ◽

Adaptive Resource Management ◽

Management Framework ◽

Adaptive Resource

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A Novel Resource Management Framework for Fog Computing by Using Machine Learning Algorithm

Advances in Computer and Electrical Engineering - Architecture and Security Issues in Fog Computing Applications ◽

10.4018/978-1-7998-0194-8.ch002 ◽

2020 ◽

pp. 42-52

Author(s):

Shanthi Thangam Manukumar ◽

Vijayalakshmi Muthuswamy

Keyword(s):

Machine Learning ◽

Resource Management ◽

Mobile Devices ◽

Service Providers ◽

Learning Algorithm ◽

Fog Computing ◽

Mobile Network ◽

Resource Provisioning ◽

Management Framework ◽

Iot Devices

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.

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Management of Large Balanced Scorecard Implementations

Information Technology Evaluation Methods and Management ◽

10.4018/978-1-878289-90-2.ch015 ◽

2001 ◽

pp. 231-239

Author(s):

Peter Verleun ◽

Egon Berghout ◽

Maarten Looijen ◽

Roel van Rijnback

Keyword(s):

Resource Management ◽

The Netherlands ◽

Measurement System ◽

Balanced Scorecard ◽

Business Performance ◽

Information Resource ◽

Massive Data ◽

Insurance Company ◽

Management Framework ◽

The Balanced Scorecard

In this chapter, established information resource management theory is applied to improve the development and maintenance of large balanced scorecard implementations. The balanced scorecard has proved to be an effective tool for measuring business performance. Maintaining a business-wide balanced scorecard measurement system over a longer period implies, however, many risks. An example of such a risk is the excessive growth of scorecards as well as scorecard metrics, resulting in massive data warehouses and difficulties with the interpretation of data. This is particularly the case in large organisations. This chapter proposes balanced scorecard management framework that is illustrated with the experience gathered from the company-wide balanced scorecard implementation in the insurance company Nationale-Nederlanden in the Netherlands.

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