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Resource allocation and task scheduling for computing power aware networks

Resource allocation and task scheduling for computing power ...

引用

2024 international conference on Physics, Photonics, and Optical engineering, ICPPOE 2024

作者： Peng, Guozheng Zhao, Chuanqi Chen, Lingying Wang, Lanruo Beijing100089 China State Grid Shandong Electric Power Research Institute Ji’nan250003 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

As an important cornerstone of future information and communication technology, the computing power network cleverly integrates cloud resources, third-party computing devices, and various heterogeneous computing resources scattered at the edge of the network through seamless fusion of widely covered network connections. This innovative architecture aims to break the boundaries of traditional computing models and achieve precise scheduling and global optimization of computing resources by building a highly collaborative computing network resource management system. This article proposes a resource allocation and task scheduling algorithm based on deep reinforcement learning (DRL), which simulates the human decision-making process and enables the system to autonomously learn and optimize strategies in a constantly changing computing network environment. This algorithm can not only effectively cope with large-scale and high dynamic computing power demands, but also accurately predict future resource trends, pre allocate resources in advance, and ensure smooth and efficient task execution. The experimental results show that the algorithm performs well in improving the utilization of computing power resources, reducing task execution latency, enhancing system stability and flexibility, and laying a solid foundation for the further development and application promotion of computing power networks. © 2025 SPIE.

关键词： computing power

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Idempotency in Service Mesh: For Resiliency of Fog-Native Applications in Multi-Domain Edge-to-cloud Ecosystems 15

Idempotency in Service Mesh: For Resiliency of Fog-Native Ap...

引用

15th international conference on cloud computing and Services Science, CLOSER 2025

作者： Whitaker, Matthew Volckaert, Bruno Al-Naday, Mays School of Computer Science and Electrical Engineering University of Essex United Kingdom Ghent University - imec IDLab Department of Information Technology Gent Belgium

ISBN: (纸本)9789897587474

Resilient operation of cloud-native applications is a critical requirement to service continuity, and to fostering trust in the cloud paradigm. So far, service meshes have been offering resiliency to a subset of failures. But, they fall short in achieving idempotency for HTTP POST requests. In fact, their current resiliency measures may escalate the impact of a POST request failure. Besides, the current tight control over failures - within central clouds - is being threatened by the growing distribution of applications across heterogeneous clouds. Namely, in moving towards a fog-native paradigm of applications. This renders achieving both idempotency and request satisfaction for POST microservices a non-trivial challenge. To address this challenge, we propose a novel, two-pattern, resiliency solution: Idempotency and Completer. The first is an idempotency management system that enables safe retries, following transient network/infrastructure failure. While the second is a FaaS-based completer system that enables automated resolution of microservice functional failures. This is realised through systematic integration and application of developer-defined error solvers. The proposed solution has been implemented as a fog-native service, and integrated over example service mesh Consul. The solution is evaluated experimentally, and results show considerable improvement in user satisfaction, including 100% request completion rate. The results further illustrate the scalability of the solution and benefit in closing the current gap in service mesh systems. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Hypertext systems

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Efficient cloud-assisted revocable identity-based broadcast encryption in IoT 4

Efficient cloud-assisted revocable identity-based broadcast ...

引用

4th international conference on Network communication and Information Security, ICNCIS 2024

作者： Chen, Zhou Han, Lidong School of Information Science and Technology Hangzhou Normal University Hangzhou China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

While the Internet of Things (IoT) has greatly facilitated our daily lives, this trend has encountered numerous security challenges in data storing and sharing especially for sensitive personal data. Identity-Based Broadcast Encryption (IBBE) is an efficient cryptographic technique to securely share messages among multiple users. Nevertheless, to prevent access by users who were previously authorized but now lack permission, user revocation should be integrated into IBBE. Despite several previous Revocable Identity-Based Broadcast Encryption (RIBBE) schemes were proposed, they are not practical for resource-constrained IoT devices due to their high computational cost during encryption and decryption, as well as the significant storage needs for the revocation list. To tackle these issues, we propose an outsourced RIBBE scheme that is appropriate for resource-constrained IoT devices. It not only effectively implements user revocation but also leverages a cloud server to handle complex calculations. Additionally, our scheme is designed to resist collusion attacks, enhancing overall security. Performance evaluation describes the great practicality and efficiency of our scheme. © 2025 SPIE.

关键词： Internet of things clouds Computer security cloud computing Cryptography Network security Systems modeling

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Optimizing Resource Costs With Machine Learning Techniques 2

Optimizing Resource Costs With Machine Learning Techniques

引用

2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Balamanigandan, R. Mahaveerakannan, R. Saraswathi, S. Jenifer, A. Mary Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331505745

The number of SMEs opting for cloud computing rises, efficient utilisation of the cloud has become a crucial issue because of the rental nature of the resources. Current approaches tend to be more traditional, targeting only individual components such as the amount of computing capacity, which may not adequately capture or mitigate the factors that define most actual cloud workloads and their unpredictable behaviour. In light of this, this article presents a new strategy that applies anomaly detection learning algorithms and particle swarm optimization to achieve the right cloud resource configuration at the least cost possible. The suggested idea works independent of a specific context in a loop fashion and distinguishes exceptional cases through an anomaly detection strategy in the loop process and at the same time learns the usage patterns. More importantly, it adapts to changes in system utilisation, as well as the pricing models offered by various cloud providers. Realistic costs as well as system data using the Microsoft Azure proved significant with highlighting 85% costs shaving within 10 months. This research supports the effectiveness of our integrated solution for solving multifaceted issues related to resource management in cloud environments for SMEs. © 2025 IEEE.

关键词： Resource allocation

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Edge Intelligence Hybrid Framework for Real-Time IoT Applications using Multi-Stage Adaptive Processing Methods 6

Edge Intelligence Hybrid Framework for Real-Time IoT Applica...

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

作者： Suneel, Sajja Yadav, Rakesh Kumar Nirmala Devi, K. Teja sree, B. Aarthi, R. Upadhyay, Shrikant Institute of Aeronautical Engineering Dundigal Telangana Hyderabad500043 India Amity School of Engineering and Technology Amity University Bihar Patna India Department of Computer Applications Madanapalle Institute of Technology & Science India Department of IT S.R.K.R Engineering College Bhimavaram India Department of ECE Rajalakshmi Institute of Technology Kuthambakkam Post Tamil Nadu Chembarambakkam600124 India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

The rapid proliferation of IoT devices has created an unprecedented need for real-time data processing and decision-making. This paper proposes an Edge Intelligence Hybrid Framework designed for real-time IoT applications using multi-stage adaptive processing methods. The framework structured into three layers: edge-level processing, fog-level aggregation, and cloud-level computation, enabling efficient distribution of tasks across the hierarchy. At the edge, lightweight models and preprocessing techniques, such as Kalman Filtering and Huffman Coding, ensure low-latency and energy-efficient operation. Intermediate computations and model synchronization occur at the fog level, while high-complexity tasks like deep learning model training performed at the cloud level. Results demonstrate that edge-level processing achieves an accuracy of 85.4%, while fog and cloud levels improve accuracy to 89.4% and 94.9%, respectively. Latency at the edge is as low as 9–13ms, compared to fog (23–29ms) and cloud (117–125ms), ensuring suitability for time-critical IoT tasks. The proposed framework effectively balances latency, accuracy, and resource optimization, making it ideal for applications such as predictive maintenance, healthcare, and smart surveillance. © 2025 IEEE.

关键词： Kalman filters

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Design of a mobile fire alarm system capable of real-time alarm 3

Design of a mobile fire alarm system capable of real-time al...

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3rd international conference on Electrical, Electronics, and Information engineering, EEIE 2024

作者： Feng, Wenhui He, Qing Wang, Tingli Wang, Shixin School of Instrument Science and Opto-Electronics Engineering Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9781510688179

As the public pays more and more attention to fire, the importance of fire prevention is becoming increasingly prominent. This paper focuses on the realization of fire alarm software that can alarm in real time. Through in-depth research on sensor technology, cloud computing technology, mobile application development framework and real-time data processing algorithm, the accurate monitoring of smoke concentration and timely alarm function are realized. Through cloud server communication technology, the software can be stably connected with the supporting hardware equipment to complete the alarm notification in real time. At the same time, by continuously querying the server data at a frequency of once per second, it ensures that the alarm notification is issued to the user as soon as the server alarm data is updated. In addition, the software has a simple user interface, which is convenient for users to set up and view historical data, and fully considers the convenience of use. After rigorous testing, this software has excellent performance in accuracy, real-time and stability, providing a convenient and efficient solution for improving fire prevention capabilities, and has broad market prospects for its application in fire protection. © The Authors.

关键词： Fire alarm systems

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Hybrid Machine Learning Approach for Enhanced Vulnerability Detection in cloud Environments Using NIST and MITRE Frameworks 5

Hybrid Machine Learning Approach for Enhanced Vulnerability ...

引用

5th international conference on Advanced Research in computing, ICARC 2025

作者： Lasantha, N W Chanaka Maduranga, M.W.P. Abeysekara, Ruvan Tilwari, Valmik Chakraborty, Nilotpal Sharma, Debashree Iic University of Technology Faculty of Graduate Studies Phnom Penh Cambodia University of Sri Jayewardenepura Department of Electrical and Electronic Engineering Nugegoda Sri Lanka Bcas Campus Faculty of Computing Colombo Sri Lanka Indian Institute of Information Technology Department of Computer Science Engineering Guwahati India Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India

ISBN: (纸本)9798331530983

cloud security is challenged by constant adaptive cyber threats and traditional detection methods lack real time adaptability. In this paper, we propose a new hybrid ML approach stitching data from National Institute of Standards and Technology (NIST) and MITRE Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) databases together. In contrast to existing methods, this framework combines, in unique and unprecedented fashion, ensemble ML models and Realtime data processing for increased detection accuracy and reduced false positives. This approach addresses specific challenges in the cloud environments and by leveraging standardised frameworks, it offers actionable audiences for efficient and effective vulnerability management process. The framework is validated by the experimental results which prove its robustness to evolution of the cybersecurity demands. © 2025 IEEE.

关键词： Cyber attacks

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A Critical Study on Hybrid computing and Its Current Influences and Future Challenges 3

A Critical Study on Hybrid Computing and Its Current Influen...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Praveen, K.V. Joe Prathap, P.M. Beevi, L. Sherin Dani, W. Vinil St. Peter's College of Engg. and Tech. Department of Information Technology Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India St. Peter's College of Engg. and Tech. Department of Eee Chennai India

ISBN: (纸本)9798331515911

This research focuses on the evolving field of hybrid computing, which combines traditional computing architectures with advanced computational paradigms like quantum computing, neuromorphic computing, or cloud-edge computing. Hybrid computing involves integrating multiple computing models or systems to leverage their complementary strengths. For example, combining classical computing with quantum or neuromorphic approaches to solve complex problems more efficiently. The goal is to enhance computational power, increase efficiency, and tackle problems that are difficult for traditional computing models alone. Emerging technologies, including quantum computing, machine learning, and AI, are driving the need for hybrid systems. These technologies enable the handling of more complex computations that classical systems struggle with, such as optimization, simulation, and cryptography. The integration of cloud and edge computing is also central to hybrid computing, enabling decentralized computation and processing closer to data sources. Combining different computing paradigms requires seamless communication and integration between systems, which is a significant technical challenge. Main challenging areas are scalability, software and hardware compatibility, energy efficiency and security. There is a need for standard protocols and frameworks to enable smoother integration of various computing systems. New algorithms that can take advantage of hybrid architectures need to be developed. Depend on applications significant advancements in hardware, including quantum processors, neuromorphic chips, and integrated hybrid processors required. The rise of hybrid computing will also bring ethical concerns, such as the impact on jobs, privacy, and inequality in access to advanced technologies. The study concludes by emphasizing the transformative potential of hybrid computing while acknowledging the technical, economic, and societal hurdles that need to be overcome. It highlights

关键词： Quantum computers

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RL with Balanced Reward and Masking Mechanism for Multi-NUMA Virtual Machine Scheduling 19

RL with Balanced Reward and Masking Mechanism for Multi-NUMA...

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19th international conference on Ubiquitous Information Management and communication, IMCOM 2025

作者： Gao, Yucen Cheng, Yunlong Ping, Chan Tin Gao, Xiaofeng Chen, Guihai MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331507817

With the rapid development of data center and cloud computing, the importance of resource management is increasing in recent years. In this paper, we focus on the virtual machine scheduling problem in resource management, that is, how to allocate the limited virtual machine resources when a request arrives. Existing works usually model the virtual machine scheduling problem as a bin-packing problem, but they do not consider the multi-NUMA architecture servers that have been widely used in recent years. Compared with the traditional symmetric multiprocessing architecture, the multi-NUMA architecture has the advantages of faster access time and higher energy efficiency, which is more suitable for dealing with today’s scenarios with a large number of computing requirements. In this paper, we first formalize the multi-NUMA virtual machine scheduling problem as a combinatorial optimization problem and explain why it is suitable to be solved by reinforcement learning. A reinforcement learning model called BM-DQN is then proposed, which designs a balanced reward function suitable for the multi-NUMA architecture and adopts a masking mechanism to screen out the optional servers in each step of reinforcement learning, so as to improve the solution performance. Experimental results on the open-source real-world virtual machine scheduling dataset of Huawei cloud show that BM-DQN is superior to the existing methods in terms of the number of completed requests. © 2025 IEEE.

关键词： Resource allocation

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Dynamic Spectrum Allocation with Service Priority in C+L-Band Elastic Optical Networks 4

Dynamic Spectrum Allocation with Service Priority in C+L-Ban...

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4th international Computational Imaging conference, CITA 2024

作者： Yang, Huijie Zheng, Nianying Dang, Jian Wang, Xiaoling Chen, Hong Chen, Bowen School of Electronic and Information Engineering Soochow University Suzhou China School of Information and Communication Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510688834

With the rapid development of cloud computing, video, smart home, and Internet of things (IoT), the bandwidth requirements of communication networks are also greatly increasing. This paper investigates dynamic spectrum allocation for both immediate and advance reservations in C+L-band elastic optical networks. We propose a dynamic spectrum allocation approach with service priority (DSA-SP) by considering the tolerable delay time. We consider the service importance when allocating the spectrum resources in C+L-band elastic optical networks. We discuss two types of the connection requests that include both immediate reservation and advance reservation based on the maximum tolerable delay time. The simulation results demonstrate that the proposed dynamic spectrum allocation approach achieves the low blocking probability, the high spectrum efficiency, and high average optical signal-to-noise ratio (SNR). © 2025 SPIE.

关键词： Signal to noise ratio

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