Matrix Factorization Techniques for Recommender Systems

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A Break in the Clouds: Towards a Cloud Definition

Industry 4.0: A Survey on Technologies, Applications and Open Research Issues

The Grid 2: Blueprint for a New Computing Infrastructure

Cloud Computing is a flexible, cost-effective, and proven delivery platform for providing business or consumer IT services over the Internet. However, cloud Computing presents an added level of risk because essential services are often outsourced to a third party, which makes it harder to maintain data security and privacy, support data and service availability, and demonstrate compliance. Cloud Computing leverages many technologies (SOA, virtualization, Web 2.0); it also inherits their security issues, which we discuss here, identifying the main vulnerabilities in this kind of systems and the most important threats found in the literature related to Cloud Computing and its environment as well as to identify and relate vulnerabilities and threats with possible solutions.

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An analysis of security issues for cloud computing

As an increasing number of traditional applications migrated to the cloud, achieving resource management and performance optimization in such a dynamic and uncertain environment becomes a big challenge for cloud-based application providers. In particular, job scheduling is a non-trivial task, which is responsible for allocating massive job requests submitted by users to the most suitable resources and satisfying user QoS requirements as much as possible. Inspired by recent success of using deep reinforcement learning techniques to solve AI control problems, in this paper, we propose an intelligent QoS-aware job scheduling framework for application providers. A deep reinforcement learning-based job scheduler is the key component of the framework. It is able to learn to make appropriate online job-to-VM decisions for continuous job requests directly from its experiences without any prior knowledge. Experimental results using synthetic workloads and real-world NASA workload traces show that compared with other baseline solutions, our proposed job scheduling approach can efficiently reduce average job response time (e.g., reduced by 40.4% compared with the best baseline for NASA traces), guarantee the QoS at a high level (e.g., job success rate is higher than 93% for all simulated changing workload scenarios), and adapt to different workload conditions.

DRL-Scheduling: An Intelligent QoS-Aware Job Scheduling Framework for Applications in Clouds

A fast shapelet selection algorithm for time series classification

Cloud Storage is an important part of Cloud Computing, and it provides a way to achieve large scale storage architecture. And virtual storage is a strategy for Cloud Storage. A load balancing virtual storage strategy (LBVS) is proposed in this paper. The contribution of this strategy is that, it provides a large scale net data storage model and Storage as a Service model based on Cloud Storage. Three layers architecture is used to achieve storage virtualization, and two load balancing modules are used to balance systems load. This paper describes details of LBVS strategy, the model of virtual storage as well as how to implement the LBVS with iRODS and two load balancing algorithms.

LBVS: A Load Balancing Strategy for Virtual Storage.

Networks are general tools for modeling numerous information with features and complex relations. Network Embedding aims to learn low-dimension representations for vertexes in the network with rich information including content information and structural information. In recent years, many models based on neural network have been proposed to map the network representations into embedding space whose dimension is much lower than that in original space. However, most of existing methods have the following limitations: 1) they are based on content of nodes in network, failing to measure the structure similarity of nodes; 2) they cannot do well in protecting the privacy of users including the original content information and the structural information. In this paper, we propose a similarity-based graph neural network model, SGNN, which captures the structure information of nodes precisely in node classification tasks. It also takes advantage of the thought of federated learning to hide the original information from different data sources to protect users’ privacy. We use deep graph neural network with convolutional layers and dense layers to classify the nodes based on their structures and features. The node classification experiment results on public data sets including Aminer coauthor network, Brazil and Europe flight networks indicate that our proposed model outperforms state-of-the-art models with a higher accuracy.

SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure

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Shijun Liu

Papers

DRL-Scheduling: An Intelligent QoS-Aware Job Scheduling Framework for Applications in Clouds

A fast shapelet selection algorithm for time series classification

LBVS: A Load Balancing Strategy for Virtual Storage.

SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding Structure

LBVS: A Load Balancing Strategy for Virtual Storage