Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
3,702
result(s) for
"Communication in management Computer network resources."
Sort by:
The social media management handbook
How do organizations manage social media effectively? Every organization wants to implement social media, but it is difficult to create processes and mange employees to make this happen. Most social media books focus on strategies for communicating with customers, but they fail to address the internal process that takes place within a business before those strategies can be implemented. This book is geared toward helping you manage every step of the process required to use social media for business. The Social Media Management Handbook provides a complete toolbox for defining and practicing a coherent social media strategy. It is a comprehensive resource for bringing together such disparate areas as IT, customer service, sales, communications, and more to meet social media goals. Wollan and Smith and their Accenture team explain policies, procedures, roles and responsibilities, metrics, strategies, incentives, and legal issues that may arise. You will learn how to: Empower employees and teams to utilize social media effectively throughout the organization Measure the ROI of social media investments and ensure appropriate business value is achieved over time Make smarter decisions, make them more quickly, and make them stick Get the most out of your social media investment and fully leverage its benefits at your company with The Social Media Management Handbook.
eBook
The value base of social work and social care
Explores the concepts and themes that help us understand the value base in social work. This book examines the tensions between values such as justice, anti-discrimination, compassion, and empathy, and the need for professionalism, accountability, cost codes, and performance measurement
Publication
Big Data, Little Data, No Data
\"Big Data\" is on the covers ofScience, Nature, theEconomist, andWiredmagazines, on the front pages of theWall Street Journaland theNew York Times.But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data -- because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines.Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure -- an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation -- six \"provocations\" meant to inspire discussion about the uses of data in scholarship -- Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.
eBook
Resource Management in Clouds: Survey and Research Challenges
Resource management in a cloud environment is a hard problem, due to: the scale of modern data centers; the heterogeneity of resource types and their interdependencies; the variability and unpredictability of the load; as well as the range of objectives of the different actors in a cloud ecosystem. Consequently, both academia and industry began significant research efforts in this area. In this paper, we survey the recent literature, covering 250+ publications, and highlighting key results. We outline a conceptual framework for cloud resource management and use it to structure the state-of-the-art review. Based on our analysis, we identify five challenges for future investigation. These relate to: providing predictable performance for cloud-hosted applications; achieving global manageability for cloud systems; engineering scalable resource management systems; understanding economic behavior and cloud pricing; and developing solutions for the mobile cloud paradigm .
Journal Article
Software Defined 5G and 6G Networks: a Survey
The current mobile communications could not satisfy the explosive data requirement of users. This paper reviews the frontier technology of software definition networks (SDN) of 5G and 6G, including system architecture, resource management, mobility management, interference management, challenges, and open issues. First of all, the system architectures of 5G and 6G mobile networks are introduced based on SDN technologies. Then typical SDN-5G/6G application scenarios and key issues are discussed. We also focus on mobility management approaches in mobile networks. Besides, three types of mobility management mechanism in software defined 5G/6G are described and compared. We then summarize the current interference management techniques in wireless cellular networks. Next, we provide a brief survey of interference management method in SDN-5G/6G. Additionally, considering the challenges, we discuss mm-Wave spectrum, un-availability of popular channel model, massive MIMO, low latency and QoE, energy efficiency, scalability, mobility and routing, inter operability, standardization and security for software defined 5G/6G networks.
Journal Article
ADAPTIVE6G: Adaptive Resource Management for Network Slicing Architectures in Current 5G and Future 6G Systems
Future intelligent wireless networks demand an adaptive learning approach towards a shared learning model to allow collaboration between data generated by network elements and virtualized functions. Current wireless network learning approaches have focused on traditional machine learning (ML) algorithms, which centralize the training data and perform sequential model learning over a large data set. However, performing training on a large dataset is inefficient; it is time-consuming and not energy and resource-efficient. Transfer Learning (TL) effectively addresses some challenges by training based on a small data set using pre-trained models for similar problems without impacting neural network model performance. TL is a technique that applies the knowledge (features, weights) gained from a previously trained ML model to another but related problem. This work proposes an Adaptive Learning framework ‘ADAPTIVE6G’, a novel approach for a network slicing architecture for resource management and load prediction in data-driven Beyond 5G (B5G), 6G Wireless systems influenced by the knowledge learning from TL techniques. We evaluated ADAPTIVE6G to solve complex network load estimation problems to promote a more fair and uniform distribution of network resources. We demonstrate that the ADAPTIVE6G model can reduce the Mean Squared Error (MSE) by more than 30% and improve the Correlation Coefficient ‘R’ by close to 6% while reducing under-provisioned resources.
Journal Article
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities
Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.
Journal Article
Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach
In current era, the next generation networks like 5th generation (5G) and 6th generation (6G) networks requires high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key element for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. An overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.
Journal Article
