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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in Cloud computing 1

A Hybrid Approach Using Cryptography and K-Least Significant...

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1st international conference on advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Goyal, Anshika Gupta, Bhumika VMSB Uttarakhand Technical University Dept. of Computer Science & Engineering Dehradun India G.B. Pant Institute of Engineering & Technology Dept. of Computer Science & Engineering Pauri India

ISBN: (纸本)9798331518578

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in Cloud computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT). © 2025 IEEE.

关键词： Cloud platforms

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Developing Scalable and Fault Tolerant distributed Architecture for Big Unstructured Data Systems 6

Developing Scalable and Fault Tolerant Distributed Architect...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Saranya, G. Kumaran, K. Pratheep, V. Dhanush, S.R. School of Computer Science and Engineering Vellore Institue of Technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

In the era of big data, efficiently processing and retrieving insights from unstructured data presents a critical challenge. This paper introduces a scalable leader-worker distributed data pipeline designed to handle large sets of unstructured data blobs. Leveraging data parallelism, the system distributes workload across worker nodes, ensuring high performance for embarrassingly parallel tasks with minimal inter-process communication. Fault tolerance is achieved through an optimized next-in-line leader re-election strategy, addressing cascading failures and mitigating the thundering herd effect. The use of Apache Zookeeper for service discovery ensures seamless coordination within the cluster while maintaining a loosely coupled architecture for enhanced scalability. Google Protocol Buffers are employed for secure data transmission, offering tamper resistance against eavesdropping and man-in-the-middle attacks. The system also integrates the TF-IDF algorithm for efficient information retrieval, demonstrating the ability to achieve data parallelism while preserving task uniformity across nodes. Performance evaluation highlights significant reductions in response times with increasing cluster size, validating the system’s scalability, efficiency, and robustness. The proposed architecture is well-suited for real-world applications requiring secure, fault-tolerant, and scalable solutions for unstructured data analysis. © 2025 IEEE.

关键词： Big data

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Order, Unite, and Conquer: A Group Formulation for Multi-Armed Bandits in Microservice Provisioning 33

Order, Unite, and Conquer: A Group Formulation for Multi-Arm...

引用

33rd Euromicro international conference on parallel, distributed, and Network-Based Processing, PDP 2025

作者： Labres, Joao Vitor Bruniera De Souza, Paulo Silas Severo Moreira, Deborah Victoria Lima Da Silva, Paulo Ricardo Branco De Paiva, Michel Bernardo Alves, Erika Costa Azevedo, Layane Menezes Campinas Brazil Alegrete Brazil

ISBN: (纸本)9798331524937

Bringing intelligent decision-making to industrial facilities involves coordinating a complex ecosystem of applications with different performance requirements that often depend on each other. A promising approach to managing this complexity is leveraging loosely coupled software architectures, such as microservices, with distributed infrastructures, such as the Edge-to-Cloud Continuum. The order in which microservices are provisioned in the Edge-to-Cloud Continuum can significantly impact outcomes when distributing tasks to agents. Poor provisioning orderings can lead to non-critical microservices overburdening servers with lower latency while leaving latencysensitive microservices with no option but to run on more distant servers. To address this challenge, we introduce a novel provisioning ordering mechanism within a Multi-Armed Bandit (MAB) algorithm, helping to conquer the problem. Simulations based on real-world specifications indicate a $\mathbf{3 8 \%}$ reduction in latency violations and up to 6% decrease in power consumption compared to an unordered approach. © 2025 IEEE.

关键词： Edge computing

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COSMICA: a novel parallel GPU code for Cosmic Rays propagation in heliosphere 33

COSMICA: a novel parallel GPU code for Cosmic Rays propagati...

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33rd Euromicro international conference on parallel, distributed, and Network-Based Processing, PDP 2025

作者： Cavallotto, Giovanni Torre, Stefano Della Vacca, Giuseppe La Gervasi, Massimo Division of Milano-Bicocca ICSC - Centro Nazionale di Ricerca in HPC Big Data and Quantum Computing Italy University of Milano-Bicocca Department of Physics 'Giuseppe Occhialini' Italy

ISBN: (纸本)9798331524937

The complex structure of interplanetary magnetic fields and their variability, due to solar activity, make it necessary to compute the Cosmic Ray (CR) modulation with numerical simulations. COde for a Speedy Monte Carlo (MC) Involving Cuda Architecture (COSMICA) is a MC code, solving backward-in-time the system of Stochastic Differential Equations (SDE) equivalent to the Parker Transport Equation (PTE). The Graphics Processing Unit (GPU) parallelization of COSMICA code is a game changer in this field because it reduces the computational time of a standard simulation from the order of hundred of minutes to few of them. Furthermore, the code is capable of distributing the computations on clusters of machines with multiple GPUs, opening the way for scaling. In COSMICA we implemented the synchronous broadcasting of memory access for evolving variable samples, the rounding of virtual particle set numbers, to fulfil the GPU blocks, and the exploitation of shared memory to free registers. Furthermore, we compactify the mathematical computations and pass to the lighter momentum formulation of SDE. The first porting of the code on GPU architecture brings it to a speed-up of 40 X. The successful optimizations bring 1.5X speed-up. © 2025 IEEE.

关键词： Cosmic rays

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The Application of Digital Empowerment Combined with Intelligent Algorithms of Computer Technology in Decision Support Systems 3

The Application of Digital Empowerment Combined with Intelli...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Lu, Wan Chen, Li Gao, Fei Wang, Chang Wuhan Windoor Information & Technology Co. Ltd Hubei Wuhan China

ISBN: (纸本)9798331508456

Traditional decision support systems (DSS) show obvious limitations in dealing with increasingly complex and dynamic decision-making scenarios. By integrating graph neural networks (GNNs) and expert systems (ESs), this paper constructs a new IDSS framework aimed at improving decision-making efficiency and accuracy. In terms of research methods, large-scale distributed computing is performed in the cloud based on parallel computing technology;the logical reasoning of expert systems and the self-learning ability of graph neural networks are combined to achieve dynamic updates and efficient processing of data by unstructured knowledge bases. Compared with traditional DSS, the accuracy of IDSS based on the fusion of GNNs and ES in financial forecasting and risk assessment scenarios is improved to more than 90%, and resource utilization is significantly optimized. This study shows that the integrated application of intelligent algorithms has broad prospects in improving the dynamic adaptability of decision support systems and coping with complex decision-making environments. © 2025 IEEE.

关键词： distributed cloud

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CMS: A Computility Resource Status Management and Storage Framework 20th

CMS: A Computility Resource Status Management and Storage F...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Peng, Xiaohui Luo, Xudong Chang, Kuo Wang, Yifan Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Nanjing Institute of InforSuperBahn Nanjing China

ISBN: (纸本)9789819628292

With the increasing demand for computility, managing distributed computility resources is crucial for improving service quality and performance of the cross-region computility infrastructure. In order to ensure timely and accurate scheduling of computing tasks on such infrastructure, a distributed computility status storage and query system is needed to achieve millisecond-level performance in terms of multidimensional sort query and multidimensional range query for computility status. Thus, we propose CMS, a computility resource status management and storage framework, which leverages specialized multidimensional indexes to support millisecond-level resource status reporting and querying, as well as sort and range queries. In addition, we developed BCQL and a set of querying algorithms to enhance throughput and reduce query latency. Experimental results show that the average query latency of the multidimensional sorting queries and range queries of the CMS system is 2.499 ms and 2.014 ms, respectively. While, the latency of those queries in Redis-based framework reach 25.66 ms and 25.13 ms, which are 10× higher than the CMS. © IFIP international Federation for Information Processing 2025.

关键词： Structured Query Language

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Optimizing Fog computing Performance: A Machine Learning-Based Task Scheduling Approach Using Fuzzy Logic and Clustering 1

Optimizing Fog Computing Performance: A Machine Learning-Bas...

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1st international conference on advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Dbritto, Ceres Lopes, Joel Oli, Sanjay Joshi, Maulik S Singh, Aniket Rayal, Ashish St Louis United States Redmond United States Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India Gujarat Power Engineering and Research Institute GTU Department of Humanity and Science Gujarat India Veer Madho Singh Bhandari Uttarakhand Technical University Department of Computer Science and Engineering Pithoragarh Campus India School of Applied and Life Sciences Uttaranchal University Department of Mathematics Dehradun248007 India

ISBN: (纸本)9798331518578

The ability to deliver results promptly has become crucial for analytical frameworks due to the swift increase in data volumes. Complex algorithms are needed to schedule data processing activities on distributed compute clusters efficiently. Because it is impractical to create and fine-tune a scheduling policy for every task, current systems rely on basic, generic heuristics and neglect workload peculiarities. Effective task scheduling is essential for optimising the performance of fog computing systems. Fog nodes are dynamic, heterogeneous, and resource-constrained, and traditional task scheduling techniques, which were largely created for centralised cloud settings, frequently cannot accommodate these characteristics. We provide a cutting-edge machine learning (ML) based solution that adjusts job assignment to the evolving needs of the Fog environment in order to get beyond these restrictions. Fuzzy logic, a reliable unsupervised learning method, is used with the K-Means clustering algorithm in our method to effectively group. We have used Python to model and assess our suggested method. The simulation findings validate the effectiveness of our scheduling method, demonstrating notable improvements in response time, reduced network usage, and execution time reduction in comparison to other ML and non-ML based scheduling techniques available in the Python framework. © 2025 IEEE.

关键词： Fog computing

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distributed and parallel Low-Diameter Decompositions for Arbitrary and Restricted Graphs 16

Distributed and Parallel Low-Diameter Decompositions for Arb...

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16th Innovations in Theoretical Computer Science conference, ITCS 2025

作者： Dou, Jinfeng Götte, Thorsten Hillebrandt, Henning Scheideler, Christian Werthmann, Julian Paderborn University Germany University of Hamburg Germany

ISBN: (纸本)9783959773614

We consider the distributed and parallel construction of low-diameter decompositions with strong diameter. We present algorithms for arbitrary undirected, weighted graphs and also for undirected, weighted graphs that can be separated through k ∈ Õ(1) shortest paths. This class of graphs includes planar graphs, graphs of bounded treewidth, and graphs that exclude a fixed minor Kr. Our algorithms work in the PRAM, CONGEST, and the novel HYBRID communication model and are competitive in all relevant parameters. Given D > 0, our low-diameter decomposition algorithm divides the graph into connected clusters of strong diameter D. For an arbitrary graph, an edge e ∈ E of length e is cut between two clusters with probability O(e·log(n)D). If the graph can be separated by k ∈ Õ(1) paths, the probability improves to O(e·log log nD). In either case, the decompositions can be computed in Õ(1)1 depth and Õ(m) work in the PRAM and Õ(1) time in the HYBRID model. In CONGEST, the runtimes are Õ(HD + √n) and Õ(HD) respectively. All these results hold w.h.p.2 Broadly speaking, we present distributed and parallel implementations of sequential divide-and-conquer algorithms where we replace exact shortest paths with approximate shortest paths. In contrast to exact paths, these can be efficiently computed in the distributed and parallel setting [STOC’22]. Further, and perhaps more importantly, we show that instead of explicitly computing vertex-separators to enable efficient parallelization of these algorithms, it suffices to sample a few random paths of bounded length and the nodes close to them. Thereby, we do not require complex embeddings whose implementation is unknown in the distributed and parallel setting. © Jinfeng Dou, Thorsten Götte, Henning Hillebrandt, Christian Scheideler, and Julian Werthmann.

关键词： Undirected graphs

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A Survey of Federated Learning Orchestration Using Kubeflow: Challenges, advances, and Future Directions 1

A Survey of Federated Learning Orchestration Using Kubeflow:...

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1st international conference on advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Josyula, Prashanth Ulaganathan, Sethuraman Arava, Sai Kumar Salesforce San Francisco United States Adobe San Francisco United States San Francisco United States

ISBN: (纸本)9798331518578

In distributed machine learning, Federated Learning (FL) has become a game-changing paradigm that allows for cooperative model training while protecting data privacy and taking regulatory compliance into account and with a focus on edge computing integration and privacy protection techniques. This paper offers a systematic assessment of federated learning orchestration using Kubeflow, looking at architectural approaches and implementation issues in Kubernetes-native contexts and includes thorough technical evaluation and code-driven solutions, identifies a number of crucial implementation challenges for Federated Learning processes, such as edge gateway service design, dynamic device management, and adaptive privacy methods. We explore exciting research avenues like context-aware federation protocols, adaptive privacy mechanisms, and dynamic privacy preservation. We also point out important research gaps in existing implementations, particularly with regard to standardized performance metrics, sophisticated fault tolerance mechanisms, and machine learning-optimized orchestration strategies. For researchers and practitioners working on Kubeflow-based federated learning deployments, this paper offers conceptual examples for managing heterogeneous device capabilities, secure model exchange, and adaptive training configurations in Kubeflow-based FL systems highlighting important areas that need further research for enterprise-grade implementations. © 2025 IEEE.

关键词： Federated learning

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computing Performance Analysis of IEC 61499 distributed Automation Systems 26

Computing Performance Analysis of IEC 61499 Distributed Auto...

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26th international conference on Industrial Technology, ICIT 2025

作者： Lyu, Tuojian Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798331521950

This work analyzes the computing performance of distributed control systems in IEC 61499 on two hardware devices, Dell XPS workstation and Raspi 4B. Based on the test IEC 61499 application, three different system configurations are tested to simulate the distribution options of singleprocess and single-thread, multi-threading, and multi-process on both hardware platforms. Experimental results show that multiprocess configurations yield more consistent execution time due to dedicated resource allocation, while multi-threading, though resource-efficient, can lead to latency variability. These results aim at help in choosing optimal configurations and selecting hardware for performance in distributed automation systems. © 2025 IEEE.

关键词： parallel processing systems

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