Cloud computing - CLOUD 2022 : 15th international conference, held as part of the Services Conference Federation, SCF 2022, Honolulu, Hi, USA, December 10-14 2022, proceedings / / Kejiang Ye and Liang-Jie Zhang

• Record Nr.: UNISA996503469503316
• Autore: Ye Kejiang
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2022]; ©2022
• ISBN: 3-031-23498-7
• Descrizione fisica: 1 online resource (131 pages)
• Collana: Lecture Notes in Computer Science
• Disciplina: 004.6782
• Soggetti: Cloud computing
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Preface -- Organization -- Services Society -- Services Conference Federation (SCF) -- SCF 2023 Events -- Contents -- Performance Evaluation of Modified Best First Decreasing Algorithms for Dynamic Virtual Machine Placement in Cloud Computing -- 1 Introduction -- 2 Related Work -- 3 System Model -- 4 Performance Evaluation -- 4.1 Implementation Tools -- 4.2 Experimental Setup on CloudSim -- 4.3 Results from Experimentation with Modified Best First Decreasing Algorithms -- 4.4 Discussions -- 5 Conclusion and Recommendation -- References -- Towards an Efficient Client Selection System for Federated Learning -- 1 Introduction -- 2 Design -- 2.1 Overview -- 2.2 Architecture of Resource Management Trees -- 2.3 Selection of Available Clients -- 2.4 Real-Time Resource Management -- 3 Evaluations -- 4 Related Work -- 5 Conclusion -- References -- Hestia: A Cost-Effective Multi-dimensional Resource Utilization for Microservices Execution in the Cloud -- 1 Introduction -- 2 Related Work -- 3 Problems and Challenges -- 4 Framework -- 5 Hestia Algorithm -- 5.1 Algorithm Details -- 6 Implementation -- 6.1 Preparation -- 6.2 Detail -- 7 Evaluation -- 8 Conclusion -- References -- Optimizing Cache Accesses with Tensor Memory Format Search for Transformers in TVM -- 1 Introduction -- 2 Related Works -- 2.1 Image Transformers -- 2.2 GPU Architecture -- 2.3 Compiler ; Optimizations -- 3 Method -- 3.1 Preliminaries and Problem Formulation -- 3.2 Search Algorithm -- 4 Results -- 4.1 Inference Performance -- 4.2 Analyses -- 5 Conclusion -- References -- Improving Few-Shot Image Classification with Self-supervised Learning -- 1 Introduction -- 2 Background and Related Work -- 2.1 Few-Shot Image Classification (FSIC) -- 2.2 Self-Supervised Learning (SSL) -- 2.3 Few-Shot Image Classification with Self-Supervised Learning -- 3 Methodology -- 3.1 Pre-training.; 3.2 Meta-training -- 4 Experimental Results -- 4.1 Datasets and Baseline -- 4.2 Implementation Details -- 4.3 Results -- 4.4 Analysis -- 5 Conclusion and Future Work -- References -- New Commonsense Views Inspired by Infants and Its Implications for Artificial Intelligence -- 1 Introduction -- 2 Literature Review -- 2.1 Disciplinary Differences in Commonsense -- 2.2 Subject Consensus of Commonsense -- 3 A New View of Commonsense Inspired by Infant Learning -- 3.1 Commonsense Learning Characteristics of Infants -- 3.2 A New View of Commonsense -- 4 The Inspiration of the New View of Commonsense to AI -- 4.1 Commonsense Acquisition and Representation -- 4.2 Commonsense Organization and Reasoning -- References -- Analysis of Data Micro-governance in Full Life Cycle Management of the Leased Assets -- 1 Introduction -- 1.1 Background -- 1.2 Pain Points -- 1.3 Purpose and Meaning -- 1.4 Achievements -- 2 Management Requirements Analysis and Platform Solutions -- 2.1 Management Needs of Financial Leased Assets -- 2.2 Related Technologies -- 2.3 Construction Objectives of the Management Platform -- 2.4 Brief Description of the Management Platform Architecture Design -- 3 Micro-governance in Platform Construction -- 3.1 The Concept and Significance of Micro-governance -- 3.2 Analysis of Data Characteristics and Management Difficulties of Leased Assets -- 3.3 Requirement Analysis of the Leased Assets Data Service -- 3.4 Practice of Micro-governance in the Online Management Platform of Leased Assets -- 3.5 Achievement and Value Realization of Micro-governance -- 3.6 Summary -- 4 Outlook and Thinking -- 4.1 Remote Intelligent Due Diligence -- 4.2 ESG Rating Study -- 4.3 Digital Scene Mining -- References -- How to Build an Efficient Data Team -- 1 Introduction -- 2 CDO and Organizational Capability -- 2.1 Team Capability -- 2.2 Team Thinking.; 2.3 Team Governance -- 3 How to Implement Organizational Capability -- 3.1 Establishment of a Chief Data Officer (CDO) System -- 3.2 Synergy of Four Key Groups -- 4 CDO Roadmap -- 4.1 IsCDO Roadmap Theory -- 4.2 Typical CDO Roadmap -- 5 Conclusion -- References -- A Novel Unsupervised Anomaly Detection Approach Using Neural Transformation in Cloud Environment -- 1 Introduction -- 2 Related Work -- 3 OUR METHOD: NT-E-AR -- 3.1 NT-E-AR Architecture -- 3.2 Neural Transformation(NT) -- 3.3 Convolutional Long-Short Term Memory Network (ConvLSTM) -- 3.4 Autoregressive Long-Short Term Memory Network (LSTM) -- 3.5 Loss Function -- 4 Evaluation -- 4.1 Datasets -- 4.2 Baseline -- 4.3 Implementation Details -- 4.4 Comparison Results -- 5 Conclusion -- References -- Author Index.