We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn. After that, we discuss RL applications, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.

Deep reinforcement learning

A survey of fault tolerance in cloud computing

Nowadays, the use of digital content or digital media is increasing day by day. Therefore, there is a need to protect the digital document from both unauthorized users and authorized users. The digital document should be protected from authorized users who try to redistribute it illegally. Digital watermarking techniques along with cryptography are insufficient to ensure an adequate level of security of digital media. The security of the transferring digital data in the modern world is also a big challenge because there is a high risk of security breaches. In this article, a secure technique of image fusion using hybrid domains (spatial and frequency) for privacy preserving and copyright protection is proposed. The proposed method provides a secure technique for the digital content in cloud environment. Two cloud services are used to develop this work, which eliminates the role of a trusted third party (TTP). First is the design of an infrastructure as a service (IaaS) to store different images with encryption processes to speed up the image fusion process and save storage space. Second, a Platform as a Service (PaaS) is used to enable the digital content to improve computation power and to increase the bandwidth. The prime objective of the proposed scheme is to transfer the digital media between a service provider and customer in a secure way using a hybrid domain along with cloud storage. Imperceptibility and robustness measures are used to calculate the performance of the proposed approach.

Design of Secure Image Fusion Technique Using Cloud for Privacy-Preserving and Copyright Protection

Energy efficient fault tolerance techniques in green cloud computing: A systematic survey and taxonomy

The past few years have witnessed the emergence of a novel paradigm called cloud computing. CC aims to provide computation and resources over the internet via dynamic provisioning of services. There are several challenges and issues associated with implementation of CC. This research paper deliberates on one of CC main problems i.e. load balancing (LB). The goal of LB is equilibrating the computation on the cloud servers such that no host is under/ overloaded. Several LB algorithms have been implemented in literature to provide effective administration and satisfying customer requests for appropriate cloud nodes, to improve the overall efficiency of cloud services, and to provide the end user with more satisfaction. An efficient LB algorithm improves efficiency and asset's usage through effectively spreading the workload across the system's different nodes. This review research paper objective is to present critical study of existing techniques of LB, to discuss various LB parameters i.e. throughput, performance, migration time, response time, overhead, resource usage, scalability, fault tolerance, power savings, etc. The research paper also discusses the problems of LB in the CC environment and identifies the need for a novel LB algorithm that employs FT metrics. It has been found that traditional LB algorithms are not good enough and they do not consider FT efficiency metrics for their operation. Hence, the research paper identifies the need for FT efficiency metric in LB algorithms which is one of the main concerns in cloud environments. A novel algorithm that employs FT in LB is therefore proposed.

A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach

This paper presents a comprehensive survey of the state-of-the-art work on fault tolerance methods proposed for cloud computing. The survey classifies fault-tolerance methods into three categories: 1) ReActive Methods (RAMs); 2) PRoactive Methods (PRMs); and 3) ReSilient Methods (RSMs). RAMs allow the system to enter into a fault status and then try to recover the system. PRMs tend to prevent the system from entering a fault status by implementing mechanisms that enable them to avoid errors before they affect the system. On the other hand, recently emerging RSMs aim to minimize the amount of time it takes for a system to recover from a fault. Machine Learning and Artificial Intelligence have played an active role in RSM domain in such a way that the recovery time is mapped to a function to be optimized (i.e., by converging the recovery time to a fraction of milliseconds). As the system learns to deal with new faults, the recovery time will become shorter. In addition, current issues and challenges in cloud fault tolerance are also discussed to identify promising areas for future research.

Mukosi Abraham Mukwevho

Papers

Toward a Smart Cloud: A Review of Fault-Tolerance Methods in Cloud Systems