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arXiv 2025年

作者： Zarebski, Alexander E. Tellioglu, Nefel Stockdale, Jessica E. Spencer, Julie A. Khuda Bukhsh, Wasiur R. Miller, Joel C. Zachreson, Cameron School of Mathematics & Statistics University of Melbourne Australia Pandemic Sciences Institute University of Oxford United Kingdom MRC Biostatistics Unit University of Cambridge United Kingdom Department of Infectious Diseases University of Melbourne The Peter Doherty Institute for Infection and Immunity MelbourneVIC Australia Department of Mathematics Simon Fraser University Burnaby Canada Information Systems and Modeling Group Los Alamos National Laboratory Los AlamosNM United States School of Mathematical Sciences University of Nottingham United Kingdom Department of Mathematical and Physical Sciences La Trobe University Australia Australian Centre for AI in Medical Innovation Australia School of Computing and Information Systems University of Melbourne ParkvilleVIC Australia

Decisions on public health interventions to control infectious disease are often informed by computational models. Interpreting the predicted outcomes of a public health decision requires not only high-quality modelling, but also an ethical framework for assessing the benefits and harms associated with different options. The design and specification of ethical frameworks matured independently of computational modelling, so many values recognised as important for ethical decision-making are missing from computational models. We demonstrate a proof-of-concept approach to incorporate multiple public health values into the evaluation of a simple computational model for vaccination against a pathogen such as SARS-CoV-2. By examining a bounded space of alternative prioritisations of values (outcome equity and aggregate benefit) we identify value trade-offs, where the outcomes of optimal strategies differ depending on the ethical framework. This work demonstrates an approach to incorporating diverse values into decision criteria used to evaluate outcomes of models of infectious disease interventions. Copyright © 2025, The Authors. All rights reserved.

关键词： Computational cost

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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Federated Graph Learning via Constructing and Sharing Feature Spaces for Cross-Domain IoT

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IEEE Internet of Things Journal 2025年

作者： Chen, Jiale Zhuo, Shengda He, Jinchun Qiu, Wangjie Zhang, Qinnan Xiong, Zehui Zheng, Zhiming Tang, Yin Chen, Min Wang, Changdong Huang, Shuqiang Jinan University College of Cyber Security Guangzhou China Beihang University Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China Zhongguancun Laboratory Beijing China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore Jinan University School of Management Guangzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China Sun Yat-sen University School of Computer Guangzhou China Jinan University College of Cyber Security of Jinan University China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China

The Internet of Things (IoT) collects large volumes of diverse data, with graph data as a critical component, and extensively utilizes Federated Graph Learning (FGL) to process this data while preserving data security. However, the graph data collected by different IoT institutions are relatively independent due to various factors (e.g., data collection methods, geographical locations), and data access is restricted to local environments due to privacy constraints, IoT institutions typically possess heterogeneous feature spaces. Aggregating under these conditions could potentially contaminate local graph representations. Unfortunately, most existing FGL methods tend to overlook this issue. To address this challenge, we propose Federated Graph Learning via Constructing and Sharing Features (FedCSF), a novel FGL framework designed to build a globally consistent feature space and share it among IoT institutions. To construct and extract the feature space, we employ an uniform feature initialization across IoT institutions and design an encoder to extract both global and local feature relationships, thereby facilitating effective collaboration across data from different IoT institutions. Furthermore, we introduce an independent adaptive aggregation strategy to eliminate the integration of harmful knowledge, ensuring that the contributions from each IoT institution are effectively integrated into the global model. We theoretically analyze the convergence of FedCSF. To validate the effectiveness of FedCSF, we conducted extensive experiments under various settings (i.e., cross-datasets, and cross-domains), demonstrating its significant advantages of FedCSF in terms of performance, convergence speed, and practical adaptability © 2014 IEEE.

关键词： Federated learning

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High-Performance Mining of COVID-19 Open Research Datasets for Text Classification and Insights in cloud computing Environments

High-Performance Mining of COVID-19 Open Research Datasets f...

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IEEE International Conference on Utility and cloud computing (UCC)

作者： Jie Zhao Maria A. Rodriguez Rajkumar Buyya Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems The University of Melbourne Australia

ISBN: (数字)9780738123943

ISBN: (纸本)9781665415637

The COVID-19 global pandemic is an unprecedented health crisis. Many researchers around the world have produced an extensive collection of literature since the outbreak. Analysing this information to extract knowledge and provide meaningful insights in a timely manner requires a considerable amount of computational power. cloud platforms are designed to provide this computational power in an on-demand and elastic manner. Specifically, hybrid clouds, composed of private and public data centers, are particularly well suited to deploy computationally intensive workloads in a cost-efficient, yet scalable manner. In this paper, we developed a system utilising the Aneka Platform as a Service middleware with parallel processing and multi-cloud capability to accelerate the data process pipeline and article categorising process using machine learning on a hybrid cloud. The results are then persisted for further referencing, searching and visualising. The performance evaluation shows that the system can help with reducing processing time and achieving linear scalability. Beyond COVID-19, the application might be used directly in broader scholarly article indexing and analysing.

关键词： cloud computing Unified modeling language Task analysis COVID-19 Data visualization Computer architecture Scalability

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cloud computing market segmentation 13

Cloud computing market segmentation

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13th International Conference on Software Technologies, ICSOFT 2018

作者： Wu, Caesar Buyya, Rajkumar Ramamohanarao, Kotagiri Laboratory School of Computing and Information Systems University of Melbourne Australia

ISBN: (纸本)9789897583209

The topics of cloud pricing models and resources management have been receiving enormous attention recently. However, very few studies have considered the importance of cloud market segmentation. Moreover, there is no a better, practical and quantifiable solution for a cloud service providers (CSP) to segment cloud market. We propose a novel solution that combines both hierarchical clustering and time series forecasting on the basis of the classical theory of market segmentation. In comparison with some traditional approaches, such as nested, analytic, Delphi, and strategy-based approaches, our method is much more effective, flexible, measurable and practical for CSPs to implement their cloud market strategies by rolling out different pricing models. Our tested results and empirical analysis show that our solution can efficiently segment cloud markets and also predict the market demands. Our primary goal is to offer a new solution so that CSPs can tail its limited cloud resources for its targeted market or cloud customers. Copyright © 2018 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

关键词： Forecasting

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Microservices-based IoT application placement within heterogeneous and resource constrained fog computing environments 19

Microservices-based IoT application placement within heterog...

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12th IEEE/ACM International Conference on Utility and cloud computing, UCC 2019

作者： Pallewatta, Samodha Kostakos, Vassilis Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Australia

ISBN: (纸本)9781450368940

Fog computing paradigm has created innovation opportunities within Internet of Things (IoT) domain by extending cloud services to the edge of the network. Due to the distributed, heterogeneous and resource constrained nature of the Fog computing nodes, Fog applications need to be developed as a collection of interdependent, lightweight modules. Since this concept aligns with the goals of microservices architecture, efficient placement of microservices-based IoT applications within Fog environments has the potential to fully leverage capabilities of Fog devices. In this paper, we propose a decentralized microservices-based IoT application placement policy for heterogeneous and resource constrained Fog environments. The proposed policy utilizes the independently deployable and scalable nature of microservices to place them as close as possible to the data source to minimize latency and network usage. Moreover, it aims to handle service discovery and load balancing related challenges of the microservices architecture. We implement and evaluate our policy using iFogSim simulated Fog environment. Results of the simulations show around 85% improvement in latency and network usage for the proposed microservice placement policy when compared with cloud-only placement approach and around 40% improvement over an alternative Fog application placement method known as Edge-ward placement policy. Moreover, the decentralized placement approach proposed in this paper demonstrates significant reduction in microservice placement delay over centralized placement. © 2019 Association for computing Machinery.

关键词： Internet of things

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High-performance mining of COVID-19 open research datasets for text classification and insights in cloud computing environments

arXiv

引用

arXiv 2020年

作者： Zhao, Jie Rodriguez, Maria A. Buyya, Rajkumar Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Australia

COVID-19 global pandemic is an unprecedented health crisis. Since the outbreak, many researchers around the world have produced an extensive collection of literatures. For the research community and the general public to digest, it is crucial to analyse the text and provide insights in a timely manner, which requires a considerable amount of computational power. clouding computing has been widely adopted in academia and industry in recent years. In particular, hybrid cloud is gaining popularity since its two-fold benefits: utilising existing resource to save cost and using additional cloud service providers to gain assess to extra computing resources on demand. In this paper, we developed a system utilising the Aneka PaaS middleware with parallel processing and multi-cloud capability to accelerate the ETL and article categorising process using machine learning technology on a hybrid cloud. The result is then persisted for further referencing, searching and visualising. Our performance evaluation shows that the system can help with reducing processing time and achieving linear scalability. Beyond COVID-19, the application might be used directly in broader scholarly article indexing and analysing. Copyright © 2020, The Authors. All rights reserved.

关键词： Text processing

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A perturbation method to the tent map based on Lyapunov exponent and its application

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Chinese Physics B 2015年第10期24卷 78-85页

作者：曹绿晨罗玉玲丘森辉刘俊秀 Guangxi Key Laboratory of Multi-source Information Mining & Security Faculty of Electronic EngineeringGuangxi Normal University School of Computing and Intelligent Systems University of Ulster

Perturbation imposed on a chaos system is an effective way to maintain its chaotic features. A novel parameter perturbation method for the tent map based on the Lyapunov exponent is proposed in this paper. The pseudo-random sequence generated by the tent map is sent to another chaos function - the Chebyshev map for the post processing. If the output value of the Chebyshev map falls into a certain range, it will be sent back to replace the parameter of the tent map. As a result, the parameter of the tent map keeps changing dynamically. The statistical analysis and experimental results prove that the disturbed tent map has a highly random distribution and achieves good cryptographic properties of a pseudo-random sequence. As a result, it weakens the phenomenon of strong correlation caused by the finite precision and effectively compensates for the digital chaos system dynamics degradation.

关键词： perturbation tent map Lyapunov exponent finite precision

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Edge affinity-based management of applications in fog computing environments 19

Edge affinity-based management of applications in fog comput...

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12th IEEE/ACM International Conference on Utility and cloud computing, UCC 2019

作者： Mahmud, Redowan Ramamohanarao, Kotagiri Buyya, Rajkumar Laboratory School of Computing and Information Systems University of Melbourne Australia

ISBN: (纸本)9781450368940

Fog computing overcomes the limitations of executing Internet of Things (IoT) applications in remote cloud datacentres by extending the computation facilities closer to data sources. Since most of the Fog nodes are resource constrained, accommodation of every IoT application within Fog environments is very challenging. Hence, we need to efficiently identify which set of applications should be deployed in Fog. It becomes even more complicated when the application characteristics in terms of urgency, size and flow of inputs are considered simultaneously. The necessity of time-optimized execution further intensifies the application management problem. In this work, we propose a policy for Fog environments that distributes application management tasks across the gateway and the infrastructure level. It classifies and places applications according to their Edge affinity. Edge affinity of an application denotes the relative intensity of different attributes coherent with its characteristics such as user-defined deadline, amount of data per input and sensing frequency of IoT devices, which are required to be addressed within Fog environments to meet its Quality of Service (QoS). The proposed policy also minimizes the service delivery time of applications in Fog infrastructure. Its performance is compared with existing application management policies in both iFogSimsimulated and FogBus-based real environments. The experiment results show that our policy outperforms others in combined QoS enhancement, network relaxation and resource utilization. © 2019 Copyright held by the owner/author(s).

关键词： Internet of things

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On-demand Cold Start Frequency Reduction with Off-Policy Reinforcement Learning in Serverless computing

arXiv

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arXiv 2023年

作者： Agarwal, Siddharth Rodriguez, Maria A. Buyya, Rajkumar Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems The University of Melbourne Australia

Function-as-a-Service (FaaS) is a cloud computing paradigm offering an event-driven execution model to applications. It features 'serverless' attributes by eliminating resource management responsibilities from developers, and offers transparent and on-demand scalability of applications. To provide seamless on-demand scalability, new function instances are prepared to serve the incoming workload in the absence or unavailability of function instances. However, FaaS platforms are known to suffer from cold starts, where this function provisioning process introduces a non-negligible delay in function response and reduces the enduser experience. Therefore, the presented work focuses on reducing the frequent, on-demand cold starts on the platform by using Reinforcement Learning(RL). The proposed approach uses model-free Q-learning that consider function metrics such as CPU utilization, existing function instances, and response failure rate, to proactively initialize functions, in advance, based on the expected demand. The proposed solution is implemented on Kubeless and evaluated using an open-source function invocation trace applied to a matrix multiplication function. The evaluation results demonstrate a favourable performance of the RL-based agent when compared to Kubeless' default policy and a function keep-alive policy by improving throughput by up to 8.81% and reducing computation load and resource wastage by up to 55% and 37%, respectively, that is a direct outcome of reduced cold starts. © 2023, CC BY.

关键词： Reinforcement learning

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