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Performance Evaluation of Partial Offloading under Various Scenarios in Mobile Edge computing 24

Performance Evaluation of Partial Offloading under Various S...

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24th international conference on Advanced Communication technology (ICACT) - Artificial Intelligence Technologies toward Cybersecurity

作者： Oh, SeokBeom Lee, SooJeong Hong, Yong-Geun Kong, Gyuyeol Kahng, Hyun-Kook Korea Univ Program Converging Technol Syst & Standardizat Seoul South Korea Daejeon Univ Dept Artificial Intelligence & Convergence Daejeon South Korea Chongshin Univ Inst Hokmah Gen Educ Seoul South Korea Korea Univ Dept Elect & Informat Engn Seoul South Korea

ISBN: (纸本)9791188428090

The problem of offloading policy is addressed for mobile edge computing (MEC) in this paper. We proposed a deep learning-based partial offloading method to reduce user equipment's energy consumption and service delay. The proposed method consists of two deep neural networks (DNNs) to find the best partitioning of a single task and their offloading policy, respectively. Multiclass classification is used for the selection of partitioning and offloading policies. For partitioning selection, the DNN was learned through the ratio of task size instead of the actual task size to improve the classification accuracy. The performance of the proposed method was evaluated in three scenarios which are delay-critical model (DCM), energy-critical model (ECM), and delay and energy-critical model (DECM). The simulation results show that ECM has the worse classification performance for partitioning selection than DCM and DECM, while three scenarios have similar classification performance for offloading selection. Additionally, the proposed method has more than 77% and 89% classification performances for partitioning and offloading in various scenarios, respectively.

关键词： Mobile edge computing computation offloading strategy task partitioning deep learning

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement Learning 3

Multi-Agent Systems for Autonomous IoT Network Management Us...

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3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Neelamegam, G. Venkatesan, Rajaram Ramya, S.R. Ramya, R.S. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak VSB College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Builders Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tirupur India Dr NGP Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems. © 2025 IEEE.

关键词： Resource allocation

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On the convergence of Fictitious Play: A Decomposition Approach 31

On the Convergence of Fictitious Play: A Decomposition Appro...

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31st international Joint conference on Artificial Intelligence (IJCAI)

作者： Chen, Yurong Deng, Xiaotie Li, Chenchen Mguni, David Wang, Jun Yan, Xiang Yang, Yaodong Peking Univ Sch Comp Sci Ctr Frontiers Comp Studies Beijing Peoples R China JD com Business Growth BU Beijing Peoples R China Huawei R&D Cambridge England UCL London England Huawei TCS Lab Shenzhen Peoples R China Peking Univ Inst AI Beijing Peoples R China

ISBN: (纸本)9781956792003

Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in n-player games, which builds the foundation for modern multi-agent learning algorithms. Although FP has provable convergence guarantees on zero-sum games and potential games, many real-world problems are often a mixture of both and the convergence property of FP has not been fully studied yet. In this paper, we extend the convergence results of FP to the combinations of such games and beyond. Specifically, we derive new conditions for FP to converge by leveraging game decomposition techniques. We further develop a linear relationship unifying cooperation and competition in the sense that these two classes of games are mutually transferable. Finally, we analyze a non-convergent example of FP, the Shapley game, and develop sufficient conditions for FP to converge.

关键词： Learning algorithms

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A Privacy Preserving Federated Learning Aggregation Algorithm for Noise Label 9

A Privacy Preserving Federated Learning Aggregation Algorith...

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9th international conference on Computer and Communications, ICCC 2023

作者： Han, Weiguang Yan, Xishan Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China College of Computer and Cyber Secutiry Fujian Normal University Fuzhou China University of Chinese Academy of Sciences Beijing China Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fuzhou China

ISBN: (纸本)9798350317251

To solve the problem that a large number of noise labels lead to the reduction of the efficiency of existing aggregation Algorithmic efficiency and the difficulty of extending existing methods to the privacy protection framework, a noise label oriented privacy protection Federated learning aggregation algorithm PP-ASNL is proposed. This algorithm is mainly divided into two parts: a client-side noise label sample detection model and a server-side model rating aggregation mechanism. Simulation results show that the proposed algorithm has higher model accuracy and Rate of convergence than the existing methods. © 2023 IEEE.

关键词： Efficiency

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A Prediction based Autoscaling in Serverless computing 13

A Prediction based Autoscaling in Serverless Computing

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13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Phung, Ha-Duong Kim, Younghan School of Electronic Engineering Soongsil University Seoul Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Nowadays, serverless computing has risen rapidly as an emerging cloud computing paradigm in recent years. Auto-scaling is a crucial enabler for adapting to workload changes by scaling instances corresponding to the number of incoming requests. Recently, a popular Kubernetes-based platform for managing serverless workloads known as Knative has been proposed. Its scaling algorithm increases and decreases resources based on configured parameter values such as a maximum number of requests that can be processed in parallel per pod. However, users have no idea about the appropriate parameter values, while those predefined values can strongly impact the performance. The negative influence can cause response time increase and throughput reduction if those are overprovisioning or under-provisioning. Besides, Knative uses a moving average method to calculate the number of pods based on past data but cannot reflect the future workload trend, leading to the delay effect. In this paper, we focus on maximizing performance with the lowest resource utilization and optimizing response time to satisfy the quality of service (QoS) requirements so that it is content with users' experience while users only must pay the least cost. To do that, our proposed approach first finds effective scaling policies per pod to maximize the performance with the acceptable latency. Then we apply the forecasting model Bi-LSTM to optimize the number of pods calculation and make Knative more adaptive to the workload changes to reduce the delay time. Our preliminary experiments show that our proposed model can significantly improve performance with an effective scaling policy and an adaptive pod calculation method compared to the default Knative auto-scaling scheme. © 2022 IEEE.

关键词： Quality of service

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A Hybrid Modified ABC-PSO Algorithm for Optimal Robotic Path Planner 15

A Hybrid Modified ABC-PSO Algorithm for Optimal Robotic Path...

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15th international conference on Developments in eSystems Engineering, DeSE 2023

作者： Khalil, Nadia I. Abdullah, Hadeel N. Hassnawi, Layth A. University of Technology-Iraq Communication Engineering Department Baghdad Iraq University of Technology-Iraq Electrical Engineering Department Baghdad Iraq Ministry of Science and Technology Research and Development Department Baghdad Iraq

ISBN: (纸本)9798350335149

Path planning is one of the most fundamental problems that must be dealt with before the mobile robot can navigate and explore autonomously in any environment. A good path-planning algorithm can save time and reduce mobile robot wear and capital investment. Path computing time and average path length are important factors over cost functions that reflect the algorithm's effectiveness, such as power consumption or average trip time. The Artificial Bee Colony (ABC) represents one of the most important global search algorithms. The main problem with ABC is that it suffers from a slow convergence rate due to lousy exploitation and tends to get trapped in the local minima. This paper proposes and evaluates a new robot path-planning algorithm named Modified Artificial Bee Colony (MABC). MABC algorithm design is based on modifying the ABC algorithm by cross-layer design between ABC and Particle Swarm Optimization (PSO) algorithms. The MABC is different from the original ABC algorithm in that it modifies the original one to use PSO's exploitation rather than its exploitation. On the other hand, the PSO algorithm has better exploitation but poor exploration characteristics. The evaluation and analysis were performed for several performance metrics and under different evaluation scenarios. It has been observed from the results that the MABC-PSO outperforms the original ABC with respect to average path length and convergence time which leads to improving the planning of the path. © 2023 IEEE.

关键词： Motion planning

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Predicting Carboxymethyl Cellulase assay (CMCase) production using Artificial Neural Network and explicit feature selection approach 8

Predicting Carboxymethyl Cellulase assay (CMCase) production...

引用

8th IEEE international conference for convergence in technology, I2CT 2023

作者： Ullah Miah, M. Saef Sulaiman, Junaida Zamli, Kamal Z. Samiur Rashid, Shah Khan Chowdhury, Ahmed Jalal Universiti Malaysia Pahang Faculty of Computing Pekan Malaysia Department of Biochemistry and Biotechnology Chittagong Bangladesh International Islamic University Malaysia Kulliyyah of Science Kuantan Malaysia

ISBN: (纸本)9798350334012

This paper presents a method for predicting carboxymethyl cellulase (CMCase) production using artificial neural networks (ANNs) and an explicit feature selection approach. A dataset of CMCase production experiments was collected, and an explicit feature selection approach was applied to select the most relevant features for CMCase production prediction. The ANN model was trained using both the selected features and all available features of the CMCase production data. The results showed that the explicit feature selection approach improved the performance of the ANN model in terms of prediction accuracy compared to using all the features available in the dataset. The main effect analysis (MEA) was found to be the best method for selecting the explicit features for predicting CMCase production. The ANN model trained using the MEA identified features, achieved 96.3% R2 score and a MAE of 0.057 and a MSE of 0.035. The proposed method is an effective approach for predicting CMCase production and can be used to optimize CMCase production and reduce costs in various industries. © 2023 IEEE.

关键词： Feature Selection

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A Resource Provisioning Framework Overview for a Joint Optical/Wireless Transport Network (JOWTN)

A Resource Provisioning Framework Overview for a Joint Optic...

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2023 IEEE international conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Shezi, Nokwanda Meschack, Molefe Bakhe, Nleya Durban University of Technology Department of Electronic and Computer Engineering Musgrave South Africa

ISBN: (纸本)9798350327816

As data traffic levels surge globally, research is intensifying toward designing high-capacity Data Transport networks that will maximally utilize photonics-electronics convergence. The focus is mainly on defining appropriate architectures and network services to accommodate the diverse traffic types with consistent performance objectives such as provisioning ultra-high capacity, high connectivity, and extremely low latencies for critical mission delay-sensitive services. Thus, we define an appropriate modern JOWTN and propose a joint resource allocation scheme considering 5G/7G scenarios. A model for dimensioning the available resources in the wireless and optical domain sections is also overviewed. Finally, the proposed scheme's performance is validated by way of simulation. © 2023 IEEE.

关键词： mobile edge computing domain optical domain resource allocation wireless access domain

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MMES: Improved Mayfly Algorithm Based on Electrostatic Optimization Algorithm 8

MMES: Improved Mayfly Algorithm Based on Electrostatic Optim...

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8th IEEE international conference on Computer and Communications, ICCC 2022

作者： He, Shaojie Yu, Bihui Wei, Jingxuan Bu, Liping Shenyang Institute of Computing Technology Chinese Academy of Science University of Chinese Academy of Science Shengyang China Shenyang Institute of Computing Technology Chinese Academy of Science Shengyang China

ISBN: (纸本)9781665450515

Cloud computing divides a huge program into countless subtasks through the network, which are calculated and analyzed by multiple servers, and then the results are returned to users. Therefore, the strategy of task scheduling is very important for computing performance. Aiming at the essence of cloud computing task scheduling and the optimization problem of seeking solutions, this paper proposes a hybrid algorithm called MMES algorithm (MA-MIX-ESDA). This algorithm not only guarantees the search space of electrostatic discharge algorithm (ESDA), but also accelerates its convergence speed, and solves the problem that mayfly algorithm (MA) is easy to fall into local optimization. Latin hypercube sampling is used for population initialization, exploration and development are balanced by the direction of the balance vector, and the step size control factor is added to jump out of local optimization. In order to evaluate the performance of the algorithm, 23 groups of test functions commonly used by CEC and 30 benchmark functions of CEC2014 are used to test the global search and local development functions of the algorithm, and the results are compared with the improved algorithm and classical algorithm. Experimental results show that the proposed MMES algorithm is more superior in search space and convergence speed. © 2022 IEEE.

关键词： Cloud computing

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High Altitude Platforms-assisted Hierarchical computing Offloading in Marine-IoT Networks: A Delay Minimization Approach

High Altitude Platforms-assisted Hierarchical Computing Offl...

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IEEE conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Li, Mingqing Qian, Li Ping Wang, Qianru Wu, Yuan Lin, Bin Yang, Xiaoniu Zhejiang Univ Technol Coll Informat Engn Hangzhou Zhejiang Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Zhuhai UM Sci & Technol Res Inst Zhuhai 519031 Peoples R China Dalian Maritime Univ Dept Commun Engn Dalian Peoples R China Sci & Technol Commun Informat Secur Control Lab Jiaxing Peoples R China Zhejiang Univ Technol Inst Cyberspace Secur Hangzhou 310023 Peoples R China

ISBN: (纸本)9798350310900

Mobile edge computing has been a promising technology that enables diverse applications of computation-intensive yet latency-sensitive in marine Internet of Things networks. In this paper, we propose a framework of hierarchical computing offloading with the assistance of high altitude platforms (HAPs), and a hybrid transmission scheme of non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA) is designed for achieving efficient computation offloading. Specifically, the offshore sensing devices (SDs) initially perform computation offloading to the HAPs by forming NOMA groups, and the HAPs further offload partial workload to the onshore base station (BS) via FDMA. For efficient calculation, we aim to minimize the overall delay in completing all the workload processing of these SDs by jointly optimizing the durations of NOMA and FDMA transmission as well as the hierarchical computation offloading workload. Though the problem is in the form of non-convexity, we design an efficient SCA-based algorithm to tackle it. Finally, numerical results demonstrate the optimality and convergence of the proposed algorithm, as well as the performance gains of the proposed scheme.

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