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Multilevel Secure Container Deployment Framework in Edge computing 1

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8th international symposium on Security in computing and Communications, SSCC 2020

作者： Nambiar, Seema Nandakrishna Tubakad, Chirag Kiran, Adithya Kalambur, Subramaniam Computer Science and Engineering PES University Bengaluru Karnataka India

ISBN: (数字)9789811604225

ISBN: (纸本)9789811604218

Large scale distributed IOT applications like smart city, smart building etc. are becoming a reality. The microservice architectural pattern is now becoming common for its ease of development and is also used in edge systems. In order to secure containers, the gVisor container framework is emerging as an alternative to the standard Docker container, but has increased performance overheads in the network stack and file system processing. In this paper, we first characterize the performance of gVisor containers running real programs and demonstrate the loss in performance. Next, we propose a multi-level container deployment framework that chooses the right container framework trading off between performance and security based on the containers use in a microservice application. We demonstrate that using our framework, it is possible to ensure security with a relatively lower impact on performance. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Containers

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A High Precision Refresh Method to Improve The Performance of Flash Storage Devices

A High Precision Refresh Method to Improve The Performance o...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Peixuan Li Yaofang Zhang Deli Yin Ping Xie College of Computer of Qinghai Normal University Xining P. R. China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining P. R. China The Key Laboratory of Internet of Things of Qinghai Province Xining P. R. China Academy of Plateau Science and Sustainability Xining P. R. China Chuzhou Polytechnic Chuzhou P. R. China

ISBN: (纸本)9781665428903

With the development of flash memory, its storage density has gradually increased, but its reliability has been greatly reduced. Now, flash memory relies on LDPC to solve this problem. Although LDPC (Low Density Parity Check Code) can correct data with a high error rate, it will cause a lot of delays. The refresh operation can cause the LDPC decoding delay. In the work of this article, we propose a precise refresh strategy. On the basis of high latency pages, according to the characteristics of data access frequency, we take the read-hot data in the write-cold data as our refresh target. We test the performance of the precise refresh scheme through different workloads. Compared with ordinary refresh schemes, the average response time of flash memory storage devices is reduced by 16%-49% and the lifetime of flash memory is extended.

关键词： Performance evaluation Error analysis distributed databases Parity check codes Delays Decoding Reliability

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Machine Learning Techniques to Predict Academic Performance of Health sciences Students

Machine Learning Techniques to Predict Academic Performance ...

引用

international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Hana Alharthi College of Public Health Imam Abdulrahman Bin Faisal University (IAU) Dammam Saudi Arabia

ISBN: (纸本)9781665428903

Prediction of academic performance of health sciences students prior to being fully engaged in academic studies will identify those students who may need early intervention. Machine learning (ML), a branch of artificial intelligence, can be used to predict the academic performance of such students and the factors that continue to impact their academic performance. Objective: To use a best fit model in ML to predict the academic performance of health science students and rank the most important factors affecting their performance. Method: The academic records of 3468 students were extracted from the student information system (SIS), which included preparatory year great point average (GPA), high school GPA, Achievement Test (AT), General Aptitude Test (GAT), and cumulative GPA upon graduation. Multiple machine learning algorithms were used to develop the best fit model to predict students' performance GPA and identify factors that contributed to GP A. Results: The best performing classifier based on area under the curve (AUC) is random forest (.773) followed by naïve bayes (.758), Support Vector Machine (.686), k-nearest neighbors (.684) and decision tree (.658), the three scoring methods showed preparatory year GPA, gender, and high school GPA were the top variables predicating student cumulative GPAs. Conclusion: Random forest model can assist college administrators and faculty in health colleges to predict which students are more likely to underperform during their undergraduate studies.

关键词： Support vector machines Machine learning algorithms Predictive models Decision trees Data mining distributed computing Random forests

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Comparison of Meta-Heuristic Algorithms for Task Scheduling in distributed Stream Processing

Comparison of Meta-Heuristic Algorithms for Task Scheduling ...

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Pacific Rim international symposium on Dependable computing

作者： Dohan Kim Aming Wu Young-Woo Kwon School of Computer Science and Engineering Kyungpook National University Daegu South Korea

With the emergence of IoT and cloud computing, the demand for big data processing continues to rise. To expedite such big data processing, distributed stream processing systems (DSPS) are commonly used. However, because the rate of incoming messages to DSPS can vary depending on a stream application and execution environments such as like network conditions, it can be challenging to provide the necessary quality of services (QoS). Modern DSPS typically use heuristic or meta-heuristic algorithms to find near-optimal solutions to meet QoS requirements; however, it is still difficult to accomplish multiple QoS goals at once. In this paper, multiple meta-heuristic algorithms are evaluated to determine if they can simultaneously achieve multiple objectives, including response time and system failure. We implemented schedulers using various meta-heuristic algorithms operating within DSPS simulation environments. Then, we executed three stream applications utilizing various scheduling algorithms and demonstrated that meta-heuristic algorithms outperform a conventional algorithm.

关键词： Scheduling algorithms Metaheuristics Clustering algorithms Optical wavelength conversion Quality of service Big Data Scheduling

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Cloud-Edge Microservice Architecture for DNN-based distributed Multimedia Event Processing 8th

Cloud-Edge Microservice Architecture for DNN-based Distribut...

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1st international Workshop on Edge Adoption and Migration, EdgeWays 2020, 16th international Workshop on engineering Service-Oriented applications and Cloud Services, WESOACS 2020, ESOCC 2020 PhD symposium, ESOCC 2020 EU Projects Track which took place at 8th European Conference on Service-Oriented and Cloud computing, ESOCC 2020

作者： Pontes, Felipe Arruda Curry, Edward Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Ireland

ISBN: (纸本)9783030719050

The rise of Big Data, Internet of Multimedia Things (IoMT), and Deep Neural Network (DNN) enabled the growth of DNN-based Computer Vision solutions to Multimedia Event Processing (MEP) applications. When these are applied to a real-world scenario we notice the importance of having a system with a satisfactory speed that can fit in the limited resources of most IoMT devices. However, most solutions for distributed MEP are dependent on a Cloud architecture, which makes these applications migration to the Edge more challenging. As a response to this, we present a microservice architecture for DNN-based distributed MEP over heterogeneous Cloud-Edge environments. We describe our solution that allows for an easier deployment both on the Edge and on the Cloud. We show that choosing the proper tools for an Edge-Friendly solution can lead to 100 times less resource utilisation. Our preliminary investigation shows promising results, with a reduction in energy consumption by 8% with a minor drawback of 15% in throughput in the Edge and a negligible increase in energy consumption on the Cloud. © 2021, Springer Nature Switzerland AG.

关键词： Deep neural networks

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Federated Learning Solution Blueprints for Use Cases Surveyed in Austrian Industries

Federated Learning Solution Blueprints for Use Cases Surveye...

引用

IEEE Conference on Commerce and Enterprise computing, CEC

作者： Thomas Blumauer-Hiessl Angela Fessl Gert Breitfuss Daniel Schall Stefan Schulte TU Wien and Siemens Technology Vienna Austria Know-Center GmbH Graz University of Technology Institute of Interactive Systems and Data Science Graz Austria Know-Center GmbH Graz Austria Siemens Technology Vienna Austria Christian Doppler Laboratory for Blockchain Technologies for the Internet of Things Institute for Data Engineering TU Hamburg Hamburg Germany

ISBN: (数字)9798331529093

ISBN: (纸本)9798331529109

Federated Learning (FL) holds immense potential for transforming the industrial landscape by leveraging distributed data to solve Artificial Intelligence (AI) use cases with collectively trained models in a privacy-preserving way. In this regard, Industrial FL (IFL) arose as a collaborative approach for training AI models between multiple industry partners and devices without the need to share the actual training data. However, despite its promising prospects, the transition and successful implementation of FL in practice is currently lagging behind, posing challenges for industrial companies. To address this, it is of crucial relevance to analyze different business types and involved stakeholders to be able to design FL-based solutions tailored to the industries needs. This paper presents the results of 13 semi-structured interviews conducted in Austrian industries, involving 11 companies from different domains. We identify AI applications, pain points, and attitudes towards AI and FL. Based on the interviews, three industry personas are derived, namely, service business, production optimization, and complex product and project business. To address the needs of these personas, three collaborative FL solution blueprints are proposed. The blueprints include system architectures, implementation steps, and collaboration modes for the involved parties. The blueprints are discussed based on dimensions such as FL paradigm, collaboration mode, key benefits, main addressed needs, and challenges.

关键词： Industries Pain Collaboration Systems architecture Production Companies Artificial intelligence Interviews Optimization business

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Predicting Reliability of Web Services Using Hidden Markov Model 8th

Predicting Reliability of Web Services Using Hidden Markov M...

引用

8th international Conference on Frontiers of Intelligent computing: Theory and applications, FICTA 2020

作者： Allagi, Shridhar Surasura, Pradeep Department of Computer Science and Engineering K L. E. Institute of Technology HubballiKarnataka580027 India

ISBN: (纸本)9789811557873

The dynamic environment applications are the approaches for the construction of distributed business framework. The quality of service (QoS) banks of these systems of these systems depends on the Web services (WS) and Internet associations. Outlining productive and viable reliability prediction of WS have turned into an imperative issue. This paper focuses reliability of systems by using hidden Markov model (HMM) for the modeling of failure and prediction of Web service reliability. The forward--backward estimation-maximization is used to estimate the modeling parameters of HMM and by using Bayesian Information Criterion (BIC), model selection is done. The favorable circumstances and disadvantages of this approach concerning regular modeling are examined. Examination of these models is done on real Web service data. Regarding reliability prediction, the hidden Markov model performs better with respect to other regular models. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reliability

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Blockchain-based Disaster Recovery Data Storage and Security Auditing Solution in Multi-cloud Environment

Blockchain-based Disaster Recovery Data Storage and Security...

引用

international Applied Computational Electromagnetics Society symposium (ACES)

作者： Bin Liu Yang Xin Suhuan Dai College of Information and Network Engineering Anhui Science and Technology University Bengbu China National Engineering Research Center for Disaster Backup and Recovery Beijing University of Posts and Telecommunications Beijing China School of Foreign Languages Anhui Science and Technology University Bengbu China

The utilization of cloud disaster recovery services has emerged as the current trend in data disaster recovery research and application due to the quick growth of virtualization technology and cloud applications. Single-cloud disaster recovery services fall short of users’ demands for data security backup due to technical flaws, managerial weaknesses, network attacks, natural calamities, etc. To implement cross-cloud disaster recovery and multi-cloud data storage, disaster recovery systems must be deployed in multi-cloud and heterogeneous cloud settings. To do this, problems with distributed cross-cloud storage’s data batch audit and problem resolution must also be resolved. In this paper, we propose a blockchain-based distributed storage and security audit solution for disaster recovery data in a multi-cloud environment, which addresses the issues of multi-copy storage management and security audit of multi-cloud data.

关键词： Cloud computing Data security Storage management distributed databases Memory Market research Computational electromagnetics

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Bi-objective optimization of availability and cost for cloud services

Bi-objective optimization of availability and cost for cloud...

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IEEE international symposium on Network computing and applications

作者： Andre Bento João Soares António Ferreira Joao Duraes José Ferreira Rita Carreira Filipe Araujo Raul Barbosa CISUC DEI University of Coimbra Fiercely Coimbra Portugal Coimbra Engineering Academy Polytechnic of Coimbra Coimbra Portugal Cleanwatts Digital Coimbra Portugal

ISBN: (纸本)9798350397314

Cloud-based services are a current approach for developing large-scale applications with advantages such as flexibility, access to on-demand resources, and business agility. The overall application functionality results from complex interactions of many decoupled services, each having its operational specificity. Due to this complexity, the manual configuration of these systems is very arduous, error-prone and likely to impair the quality of service, leading to malfunctioning services, lowering availability and accruing costs. Identifying the optimal solution to simultaneously optimize availability and costs, whilst meeting service level objectives remains a challenge for professionals developing solutions using cloud services. This paper proposes a mathematical formulation of a bi-objective problem to identify the optimal set of solutions for the system configuration. Empirical evaluation of the proposed approach in a case study of a real industrial scenario results in an R-Squared of 0.85, an MSE of 0.021 and an optimization accuracy of 0.928. These methods can help practitioners to keep services at an optimum configuration enabling autonomic service operation, whilst improving availability and cost.

关键词： Costs Computational modeling Quality of service Manuals Predictive models Resource management Object recognition

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A Trace-Log-Clusterings-Based Fault Localization Approach to Microservice Systems

A Trace-Log-Clusterings-Based Fault Localization Approach to...

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IEEE international Conference on Web Services (ICWS)

作者： Chang-Ai Sun Tao Zeng Wanqing Zuo Huai Liu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Computing Technologies Swinburne University of Technology Melbourne Australia

Microservice architecture has been widely used for the development of large-scale distributed applications. Microservice systems normally have high complexity and loose coupling nature, which make it challenging to localize faults in them. Automated fault localization is particularly difficult for microservice systems, due to their unique features, such as frequent updates, complex dependencies, and multiple microservice instances. In this paper, we propose a fault localization approach for microservice systems based on trace log clusterings, called TLCluster. TLCluster first derives trace logs by collecting and combining communication messages and logs of microservice systems, then clusters trace logs for different business process categories, calculates similarities between normal and abnormal trace logs, and finally evaluates and ranks the suspiciousness scores of microservice instances. We conducted a series of experiments to evaluate the effectiveness of TLCluster using a large-scale microservice system. Experimental results show that our approach is able to effectively localize faults of microservice systems and demonstrates a better fault localization accuracy and precision compared with state-of-the-art baseline techniques.

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