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Cost-Optimized Task Offloading for Dependent Applications in Collaborative Edge and Cloud Computing

IEEE Internet of Things Journal 2025年第9期12卷 12975-12988页

作者： Yuan, Haitao Hu, Qinglong Wang, Shen Bi, Jing Buyya, Rajkumar Lu, Jinhu Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Laboratory School of Computing and Information Systems MelbourneVIC3010 Australia CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Lyle School of Engineering Department of Computer Science DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm called Genetic Simulated-annealing-based Particle swarm optimization with Auto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs. © 2014 IEEE.

关键词： Mobile edge computing

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A novel workload forecasting model for cloud computing using ALAA-DBN algorithm

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Multimedia Tools and Applications 2025年第13期84卷 11383-11407页

作者： Karimunnisa, Syed Gopu, Arunkumar Rao, T. Prabhakara Ayyadurai, M. Kumar, ESaravana Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP VaddeswaramGuntur-522501 India School of Computer Science and Engineering VIT - AP University Amaravati522237 India Department of CSE Godavari Institute Of Engineering And Technology AP Rajahmundry533296 India Department of ECE SRM Institute of Science and Technology Ramapuram Campus Chennai India Department of CSE The Oxford College of Engineering Bengaluru India

Cloud Computing (CC) generally exhibits varying workload patterns. This autoscaling feature of CC has been extensively managed through predictive cloud resource management approaches. For this reason, a solitary forecasting model is not sufficient to forecast various workload patterns of CC web applications. With the intention of developing a proficient CC model, this paper implements a novel cloud workload forecasting model with the efficiency of DL algorithms like Deep Belief Network (DBN) and Lion Algorithm (LA). Here, the previous workload models are assessed and trained via DBN, and the prediction efficacy is enhanced by optimizing the DBN's hidden neurons through LA. The proposed workload forecast model for the cloud is accomplished by tuning the significant parameters via the Adaptive Lion Algorithm Assisted-DBN (ALAA-DBN)model. Besides, a well-organized resource provisioning pattern is followed to manage the under/over provisioning issues. The proposed CC model also considers providing better Quality of Service (QoS) to the users without violating the Service Level Agreement (SLA). Eventually, the significance of the proposed ALAA-DBN is verified through a comparative analysis with state-of-the-art models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Quality of service

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Behavioral insights into compression: a study of Move-to-Front-or-Middle deterministic online algorithm through sequence classification and characterization

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International Journal of Information Technology (Singapore) 2025年第1期17卷 189-203页

作者： Baisakh Mohapatra, Hitesh Guru, Abhishek Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Sambalpur India Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to Be University Bhubanseswar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Since the list update problem was applied to data compression as an effective encoding technique, numerous deterministic algorithms have been studied and analyzed. A powerful strategy, Move-to-Front (MTF), involves moving an accessed item to the front of the list immediately. Theoretical analysis has demonstrated MTF’s potential, showing it to be 2-competitive through competitive analysis. This success has inspired researchers to develop various intriguing MTF variants and assess their performance both theoretically and experimentally. One such variant, Move-to-Front-or-Middle (MFM), has been experimentally shown that MFM performed better than the classic MTF on Calgary and Cantebury Datasets. However, the experimental performance alone does not provide sufficient insight into the internal behavior of the algorithm or explain why its performance surpasses MTF on certain datasets. Therefore, in this paper, we conduct a detailed behavioral study of MFM through sequence classification and characterization to gain deeper understanding of the algorithm and aims to reconcile the disparity between theoretical and experimental outcomes. This paper also by classifying and characterizing MFM’s performance across various request sequences, considering both the presence and absence of locality of reference. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Competitive analysis Deterministic list update algorithm List update problem Sequence classification

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Method of Automated Dataset Collection for Microwave Filters Synthesis

Method of Automated Dataset Collection for Microwave Filters...

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2025 Systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Arinin, O.V. Bakhmach, D.M. Katsnelson, A.I. Kurbeteva, A.S. Yelizarov, A.A. Moscow Technical University of Communications and Informatics Moscow Russia Hse University Electronic Engineering Department Moscow Russia Hse University Computer Engineering Department Moscow Russia

ISBN: (纸本)9798331532635

This research discusses the method of dataset collection automatization for microwave filter synthesis by integrating machine learning techniques, thus reducing development time. Utilizing the 3D electromagnetic analysis software package, the study involves simulation and collecting geometric parameters and amplitude-frequency characteristics from three variants of passband highly selective microstrip tworesonator combined filters with stepped impedance resonators. In this paper, specific items and restrictions on filter topology creation in the simulation program are discussed, as well the review of the script used for dataset collection and its requirements is given. In addition, a dataset structure and format are described. © 2025 IEEE.

关键词： Software packages

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A Two-Pronged Approach to Security of Discrete-Event Systems

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Journal of Systems Science & Complexity 2025年第1期38卷 178-209页

作者： MATSUI Shoma RUDIE Karen CAI Kai Department of Electrical and Computer Engineering&the Ingenuity Labs Research Institute Queen's UniversityKingston K7L 3N6Canada Department of Core Informatics Osaka Metropolitan UniversityOsaka 545-0051Japan

This paper investigates a security problem of simultaneously addressing two types of attacks:Eavesdropping and *** authors model the target system as a discrete-event system(DES)with subsets of concealable events and protectable events,in order to make the proposed methodology applicable to various practical systems and employ two existing works of DES security:Degree of opacity and state ***,the authors consider that all protectable events are observable,and some observable events are *** addition,protectable events cannot be protected once they are *** such a constraint,the goal is to figure out which events to conceal and which transitions to protect so that the prescribed requirements of degree of opacity and state protection are *** this work the authors decide which events to conceal as all transitions of a given event label are concealed or not *** proposed problem formulation also requires a solution to only involve absolutely necessary protectable events in order for the system to avoid superfluous protection *** authors first examine a general version of our security problem with an intuitive algorithm to compute acceptable solutions,and then present a special version which results in a reduced computation time compared to the general version.

关键词： Degree of opacity discrete-event systems security state protection

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Design and In-training Optimization of Binary Search ADC for Flexible Classifiers 25

Design and In-training Optimization of Binary Search ADC for...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Lozano Duarte, Paula Carolina Afentaki, Florentia Zervakis, Georgios Tahoori, Mehdi Department of Computer Science Karlsruhe Inst. of Tech. Karlsruhe Germany Computer Engineering and Informatics Department Univ. of Patras Patras Greece

ISBN: (纸本)9798400706356

Flexible Electronics (FE) offer distinct advantages, including mechanical flexibility and low process temperatures, enabling extremely low-cost production. To address the demands of applications such as smart sensors and wearables, flexible devices must be small and operate at low supply voltages. Additionally, target applications often require classifiers to operate directly on analog sensory input, necessitating the use of Analog to Digital Converters (ADCs) to process the sensory data. However, ADCs present serious challenges, particularly in terms of high area and power consumption, especially when considering stringent area and energy budget. In this work, we target common classifiers in this domain such as MLPs and SVMs and present a holistic approach to mitigate the elevated overhead of analog to digital interfacing in FE. First, we propose a novel design for Binary Search ADC that reduces area overhead 2× compared with the state-of-the-art Binary design and up to 5.4× compared with Flash ADC. Next, we present an in-training ADC optimization in which we keep the bare-minimum representations required and simplifying ADCs by removing unnecessary components. Our in-training optimization further reduces on average the area in terms of transistor count of the required ADCs by 5× for less than 1% accuracy loss. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Budget control

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Optimized Temporary Frequency Support for Wind Power Plants Considering Expanded Operational Region of Wind Turbines

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CSEE Journal of Power and Energy Systems 2025年第1期11卷 51-64页

作者： Zhengyang Hu Bingtuan Gao Zhao Xu Sufan Jiang School of Electrical Engineering Southeast UniversityNanjing210096China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong Kong SARChina Department of Electrical Engineering and Computer Science The University of TennesseeKnoxvilleTN 37996 USA

Wind power plants(WPPs)are increasingly mandated to provide temporary frequency support to power systems during contingencies involving significant power ***,the frequency support capabilities of WPPs under derated operations remain insufficiently investigated,highlighting the potential for further improvement of the frequency *** paper proposes a bi-level optimized temporary frequency support(OTFS)strategy for a *** implementation of the OTFS strategy is collaboratively accomplished by individual wind turbine(WT)controllers and the central WPP ***,to exploit the frequency support capability of WTs,the stable operational region of WTs is expanded by developing a novel dynamic power control approach in WT *** approach synergizes the WTs'temporary frequency support with the secondary frequency control of synchronous generators,enabling WTs to release more kinetic energy without causing a secondary frequency ***,a model predictive control strategy is developed for the WPP *** strategy ensures that multiple WTs operating within the expanded stable region are coordinated to minimize the magnitude of the frequency drop through efficient kinetic energy ***,comprehensive case studies are conducted on a real-time simulation platform to validate the effectiveness of the proposed strategy.

关键词： Active power control model predictive control optimized temporary frequency support wind power plant wind turbine

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Review on Predicting the Landslide Susceptibility using Machine Learning Techniques 2

Review on Predicting the Landslide Susceptibility using Mach...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sinha, Abhishek Giri, Shivam Kumar Yadav, Himanshu Prasad, Sanjeev Kumar Mishra, Priya School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Application ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

This Landslide susceptibility prediction is a critical task for Alleviating the risks associated with landslide hazards, which have significant socio-economic and environmental impacts. Existing techniques face challenges related to data quality, model coherence, and generalizability across diverse geographical and climatic regions. Additionally, while advanced machine learning (ML) and deep learning (DL) models offer high accuracy, they often require significant computational resources, which limits their practical application in real-time scenarios. In this review, we aim to highlight the strengths, limitations, and potential improvements of machine learning techniques in predicting landslide susceptibility, providing a comprehensive reference for researchers and practitioners in this domain. Furthermore, the role of Explainable AI is examined to address the critical need for interpretable and transparent models in real-world applications. Hybrid ML-DL models outperform traditional methods in handling complex spatial-temporal data, with dynamic factors improving accuracy and reliability. Explainable AI enhances interpretability, while advancements and collaborations address data and generaliz ability challenges. © 2025 IEEE.

关键词： Adversarial machine learning

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Optimized Resource Allocation in Vehicle Edge Computing through Platoon Collaboration

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IEEE Internet of Things Journal 2025年第11期12卷 16129-16141页

作者： Zhao, Liang Feng, Yuhang Hawbani, Ammar Xu, Lexi Liu, Zhi Bi, Yuanguo Shenyang Aerospace University School of Computer Science Shenyang110136 China China Unicom China United Network Communications Corporation Research Institute Beijing100048 China University of Electro-Communications Department of Computer and Network Engineering Japan Northeastern University School of Computer Science and Engineering Shenyang110819 China

In modern vehicular networks, the absence of infrastructure support such as Roadside Units (RSUs) presents significant challenges for efficient task offloading and allocation. Limited computational capabilities of individual vehicles, combined with task allocation imbalances caused by varying task complexity and vehicle capacities, further complicate the process. Additionally, the formation of vehicular platoons requires accurate future route and destination information to ensure stable collaboration and effective coordination. However, such information can be challenging to obtain due to dynamic and unpredictable road environments, hindering the reliability of platoon formation. To address these challenges, we propose a platoon-based offloading strategy that integrates Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) networks to enhance task allocation efficiency. This approach also leverages the Convoy Formation Algorithm considering Future Positions (CFA-FP) to manage platoon constraints effectively. Experimental results demonstrate that our method significantly improves key performance metrics, including total computation cost, latency, and offloading success rate, compared to other task offloading strategies. © 2014 IEEE.

关键词： Deep reinforcement learning

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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