Computational Grids, emerging as an infrastructure for next generation computing, enable the sharing, selection, and aggregation of geographically distributed resources for solving large-scale problems in science, engineering, and commerce. As the resources in the Grid are heterogeneous and geographically distributed with varying availability and a variety of usage and cost policies for diverse users at different times and, priorities as well as goals that vary with time. The management of resources and application scheduling in such a large and distributed environment is a complex task. This thesis proposes a distributed computational economy as an effective metaphor for the management of resources and application scheduling. It proposes an architectural framework that supports resource trading and quality of services based scheduling. It enables the regulation of supply and demand for resources and provides an incentive for resource owners for participating in the Grid and motives the users to trade-off between the deadline, budget, and the required level of quality of service. The thesis demonstrates the capability of economic-based systems for peer-to-peer distributed computing by developing users' quality-of-service requirements driven scheduling strategies and algorithms. It demonstrates their effectiveness by performing scheduling experiments on the World-Wide Grid for solving parameter sweep applications.
SPARS: A Reinforcement Learning-Enabled Simulator for Power Management in HPC Job Scheduling
Universitas Gadjah Mada（加查马达大学）；Universitas Ahmad Dahlan（艾哈迈德·达兰大学）；Institut Bisnis dan Teknologi Indonesia（印度尼西亚商业技术学院）；National Taiwan University of Science and Technology（台湾科技大学）；Tohoku University（东北大学）
Dynamic Service Scheduling and Resource Management in Energy-Harvesting Multi-access Edge Computing
University of Leeds（利兹大学）；Southern University of Science and Technology (SUSTech)（南方科技大学）；University of Thessaly（色萨利大学）；Research Institute of Trustworthy Autonomous Systems (RITAS), SUSTech（南方科技大学可信自主系统研究院）
Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review
School of Control and Computer Engineering, North China Electric Power University（华北电力大学控制与计算机工程学院）；School of Computer Science and Technology, Shandong University of Technology（山东理工大学计算机科学与技术学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；School of Computer Science, University College Dublin（都柏林大学学院计算机科学学院）；Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）
Autonomous Resource Management in Microservice Systems via Reinforcement Learning
University of California, Berkeley（加州大学伯克利分校）；Georgia Institute of Technology（佐治亚理工学院）；Rose-Hulman Institute of Technology（罗斯-霍曼理工学院）；University of Pennsylvania（宾夕法尼亚大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management
The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Zhejiang University（浙江大学）；Northern Arizona University（北亚利桑那大学）；Trine University（特莱恩大学）；University of Electronic Science and Technology of China（电子科技大学）
HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing
Imperial College London（帝国理工学院）；Queen Mary University of London（伦敦玛丽女王大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Lancaster University（兰卡斯特大学）；University of Birmingham（伯明翰大学）；Vienna University of Technology（维也纳工业大学）；University of Manchester（曼彻斯特大学）；Cardiff University（卡迪夫大学）；The University of Melbourne（墨尔本大学）；Loughborough University（拉夫堡大学）
