Users of cloud computing are increasingly overwhelmed with the wide range of providers and services offered by each provider. As such, many users select cloud services based on description alone. An emerging alternative is to use a decision support system (DSS), which typically relies on gaining insights from observational data in order to assist a customer in making decisions regarding optimal deployment or redeployment of cloud applications. The primary activity of such systems is the generation of a prediction model (e.g. using machine learning), which requires a significantly large amount of training data. However, considering the varying architectures of applications, cloud providers, and cloud offerings, this activity is not sustainable as it incurs additional time and cost to collect training data and subsequently train the models. We overcome this through developing a Transfer Learning (TL) approach where the knowledge (in the form of the prediction model and associated data set) gained from running an application on a particular cloud infrastructure is transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures. In this paper, we present our approach and evaluate it through extensive experimentation involving three real world applications over two major public cloud providers, namely Amazon and Google. Our evaluation shows that our novel two-mode TL scheme increases overall efficiency with a factor of 60\% reduction in the time and cost of generating a new prediction model. We test this under a number of cross-application and cross-cloud scenarios.
Transferable Low-Rank Convolutional Bases for Onboarding Unseen Medical Imaging Modalities
Bangladesh University of Engineering and Technology (BUET)（孟加拉国工程技术大学）；Osaka Metropolitan University（大阪都市大学）；Chittagong University of Engineering and Technology (CUET)（吉大港工程技术大学）；Kochi University of Technology（高知工科大学）
Joint Continual Learning of Local Language Models and Cloud Offloading Decisions with Budget Constraints
Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN（电子与计算机工程学院，普渡大学）；Department of Computer Science, University of Exeter, UK（计算机科学系，埃克塞特大学）
A Cloud Infrastructure Service Recommendation System for Optimizing Real-time QoS Provisioning Constraints
云计算的普及已经彻底改变了基于互联网的应用服务的托管和交付。然而，随着几乎每个月都有大型云计算公司（例如，Amazon Web Service、Microsoft Azure）和小公司（例如Rackspace、Ninefold），决策者（例如应用程序开发人员、CIO）可能会被可用的选择所淹没。由于异构的服务配置和应用程序供应服务质量（QoS）的约束，决策问题进一步复杂化。为了应对这一严峻挑战，在我们以前的工作中，我们开发了一种半自动化的、可扩展的、基于本体的方法，用于仅基于设计时约束（例如，租赁成本、中心位置、服务特色等）。在本文中，我们扩展我们的方法，包括实时的【形】（运行时）的QoS（端到端的消息延迟，端到端的消息吞吐量）的决策过程。在线交互式游戏、大规模传感器分析和云服务上的实时的【形】移动应用领域中的下一代应用的托管需要优化这种实时的【形】QoS约束以满足服务水平协议（SLA）。为此，我们提出了一个实时的【形】的QoS感知的多标准决策技术，建立在众所周知的层次分析法（AHP）的方法。所提出的技术适用于选择基础设施即服务（IaaS）云产品，并且它允许用户定义多个设计时和实时的【形】QoS约束或要求。然后将这些需求与我们的知识库进行匹配，以计算IaaS层云服务的可能最佳组合。我们进行了大量的实验，以证明我们的方法的可行性。
Joint Offloading and Resource Allocation for Hybrid Cloud and Edge Computing in SAGINs: A Decision Assisted Hybrid Action Space Deep Reinforcement Learning Approach
