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A Deep Learning Approach for Energy Efficient computational offloading in Mobile Edge Computing

IEEE ACCESS 2019年 7卷 149623-149633页

作者： Ali, Zaiwar Jiao, Lei Baker, Thar Abbas, Ghulam Abbas, Ziaul Haq Khaf, Sadia GIK Inst Engn Sci & Technol Telecommun & Networking TeleCoN Res Lab Topi 23640 Pakistan Univ Agder UiA Dept Informat & Commun Technol N-4898 Grimstad Norway Liverpool John Moores Univ Dept Comp Sci Fac Engn & Technol Liverpool L3 3AF Merseyside England GIK Inst Engn Sci & Technol Fac Comp Sci & Engn Topi 23640 Pakistan GIK Inst Engn Sci & Technol Fac Elect Engn Topi 23640 Pakistan

Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by application components, network conditions, computational load, amount of data transfer, and delays in communication. We formulate the cost function involving all aforementioned factors, obtain the cost for all possible combinations of component offloading policies, select the optimal policies over an exhaustive dataset, and train a deep learning network as an alternative for the extensive computations involved. Simulation results show that our proposed model is promising in terms of accuracy and energy consumption of UEs.

关键词： Deep learning Energy consumption Servers Decision making Cost function Mathematical model Task analysis computational offloading deep learning energy efficient offloading mobile edge computing user equipment

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DRL-based Task and computational offloading for Internet of Vehicles in Decentralized Computing

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JOURNAL OF GRID COMPUTING 2024年第1期22卷 18-18页

作者： Zhang, Ziyang Gu, Keyu Xu, Zijie Brunel Univ London Dept Math London UB8 3PH England

This paper focuses on the problem of computation offloading in a high-mobility Internet of Vehicles (IoVs) environment. The goal is to address the challenges related to latency, energy consumption, and payment cost requirements. The approach considers both moving and parked vehicles as fog nodes, which can assist in offloading computational tasks. However, as the number of vehicles increases, the action space for each agent grows exponentially, posing a challenge for decentralised decision-making. The dynamic nature of vehicular mobility further complicates the network dynamics, requiring joint cooperative behaviour from the learning agents to achieve convergence. The traditional deep reinforcement learning (DRL) approach for offloading in IoVs treats each agent as an independent learner. It ignores the actions of other agents during the training process. This paper utilises a cooperative three-layer decentralised architecture called Vehicle-Assisted Multi-Access Edge Computing (VMEC) to overcome this limitation. The VMEC network consists of three layers: the fog, cloudlet, and cloud layers. In the fog layer, vehicles within associated Roadside Units (RSUs) and neighbouring RSUs participate as fog nodes. The middle layer comprises Mobile Edge Computing (MEC) servers, while the top layer represents the cloud infrastructure. To address the dynamic task offloading problem in VMEC, the paper proposes using a Decentralized Framework of Task and computational offloading (DFTCO), which utilises the strength of MADRL and NOMA techniques. This approach considers multiple agents making offloading decisions simultaneously and aims to find the optimal matching between tasks and available resources.

关键词： computational offloading Internet of vehicles Task management Deep reinforcement learning Decentralized Framework Mobile edge computing

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Opportunistic computational offloading System for Clusters of Drones 20

Opportunistic Computational Offloading System for Clusters o...

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20th International Conference on Advanced Communication Technology (ICACT)

作者： Valentino, Rico Jung, Woo-Sung Ko, Young-Bae Ajou Univ Dept Comp Engn Worldcup Ro 206 Suwon South Korea Elect & Telecommun Res Inst Daejeon South Korea

ISBN: (纸本)9791188428014

Providing many beneficial characteristics, Unmanned Aerial Vehicles (UAVs) have become extremely popular in terms of their utilization in everyday life and military system. As they have bigger and more advanced tasks as time goes by, clusters of small UAVs are preferred rather than one single large UAV since they offer bigger coverage area, higher flexibility, and even more reliability. However, the drone system has limited computing power and energy resource which makes it a challenge how to utilize this system to have a long operation time. In this paper, we propose an opportunistic computational offloading system which borrows computing power from other drones cluster to help finishing tasks on a specified cluster. When there is a drone cluster having a lot of computing tasks exists with another cluster in the communication range that has enough computing resources or in the idle state, our proposed scheme will make a cluster head to opportunistically decide to do offloading or not by considering various costs. The proposed scheme aims to increase the drone's lifetime and also to shorten the response time.

关键词： Cluster computational offloading Drone Multiple UAVs Wireless Communication

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A Soft Body Physics Simulator with computational offloading to the Cloud

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INFORMATION 2018年第12期9卷 318页

作者： Danevicius, Edvinas Maskeliunas, Rytis Damasevicius, Robertas Polap, Dawid Wozniak, Marcin Kaunas Univ Technol Dept Multimedia Engn LT-51368 Kaunas Lithuania Kaunas Univ Technol Dept Software Engn LT-51368 Kaunas Lithuania Silesian Tech Univ Inst Math PL-44100 Gliwice Poland

We describe the gamification of a soft physics simulator. We developed a game, called Jelly Dude, that allows the player to change and modify the game engine by tinkering with various physics parameters, creating custom game levels and installing scripts. The game engine is capable of simulating soft-body physics and can display the simulation results visually in real-time. In order to ensure high quality graphics in real time, we have implemented intelligent computational offloading to the cloud using Jordan Neural Network (JNN) with a fuzzy logic scheme for short time prediction of network traffic between a client and a cloud server. The experimental results show that computation offloading allowed us to increase the speed of graphics rendering in terms of frames per second, and to improve the precision of soft body modeling in terms of the number of particles used to represent a soft body.

关键词： soft physics body simulation gamification cloud computing computational offloading

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Edge Intelligence: A computational Task offloading Scheme for Dependent IoT Application

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第9期21卷 7222-7237页

作者： Xiao, Han Xu, Changqiao Ma, Yunxiao Yang, Shujie Zhong, Lujie Muntean, Gabriel-Miro Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Capital Normal Univ Informat Engn Coll Beijing 100048 Peoples R China Dublin City Univ Sch Elect Engn Performance Engn Lab Dublin 9 Ireland

computational offloading, as an effective way to extend the capability of resource-limited edge devices in Internet of Things (IoT), is considered as a promising emerging paradigm for coping with delay-sensitive services. However, on one hand, applications commonly include several subtasks with dependent relations and on the other hand, the dynamic changes in network environments make offloading decision-making become a coupling and complex NP-hard problem, difficult to address. This paper proposes an intelligent computational offloading scheme for Dependent IoT Application (CODIA), which decouples the performance enhancement problem into two processes: scheduling and offloading. First, a prioritized scheduling strategy is designed and its complexity is analyzed. Then, an offloading algorithm with offline training and online deployment is introduced. Due to the temporal continuity between subtasks, the dependency relation is transformed into a transition of device state, and the overhead for the whole application is considered to be the long-term benefit. CODIA leverages an Actor-Critic-based solution, where the IoT devices are able to deploy intelligent models and dynamically adjust the offloading strategy to achieve low latency, while controlling energy consumption. Finally, a series of experiments are conducted to verify the robustness and efficiency of the proposed solution in terms of convergence, latency, and energy consumption.

关键词： Task analysis Internet of Things Wireless communication computational modeling Quality of experience Vehicle dynamics Reinforcement learning Edge intelligence computational offloading dependent application deep reinforcement learning

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Semi-Online Algorithms for computational Task offloading with Communication Delay

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2017年第4期28卷 1189-1201页

作者： Champati, Jaya Prakash Liang, Ben Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

We study the scheduling of computational tasks on one local processor and one remote processor with communication delay. This problem has important application in cloud computing. Although the communication time to transmit a task can be inferred from the known data size of the task and the transmission bandwidth, the processing time of the task is generally unknown until it is processed to completion. Given a set of independent tasks with unknown processing times, our objective is to minimize makespan. We study the problem under two scenarios: 1) the communication times of the tasks to the remote processor are smaller than their corresponding processing times on the remote processor, and 2) the communication times of the tasks to the remote processor are larger than their corresponding processing times on the remote processor. For the first scenario we propose the Semi-online Partitioning and Communication (SPaC) algorithm, and for the second scenario we propose the SPaC-Restart (SPaC-R) algorithm. Even though the offline version of this problem, with a priori known processing times, is NP-hard, we show that the proposed semi-online algorithms achieve O(1) competitive ratios for their intended scenarios. We also provide competitive ratios for both algorithms for more general communication times. We use simulation to demonstrate that SPaC and SPaC-R outperform online list scheduling and performs comparably well with the best known offline heuristics.

关键词： computational offloading mobile cloud computing computation with communication semi-online algorithms

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Resource-Aware Decentralized Adaptive computational offloading & Task-Caching for Multi-Access Edge Computing 19

Resource-Aware Decentralized Adaptive Computational Offloadi...

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Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference

作者： Getenet Tefera Kun She Fareh Deeba Awais Ahmed School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9781450371858

Smart technologies or IoT devices have been designed to execute intensive applications that request more computational and other computer system resources. However, those devices have a resource constraint. To address the challenge, we adopt Multi-Access Edge Computing which is a new paradigm that transforms and localize Cloud services and capabilities at the Edge of Radio-Access Network based on proximity for mobile subscribers. In this paper, we proposed a Resource-Aware Decentralized Computing and Caching framework for Multi-Access Edge Computing. So, smart end-user devices work collaboratively and independently with resourceful edge devices or peer devices in close proximity during the unreliable network. Moreover, those devices can offload intensive application or access completed cached tasks to provide efficient resource utilization & Quality of User Experience. The drawback is expressed based on Non-Cooperative Game Theory which is NP-hard to solve and we show that the game concedes a Nash Equilibrium. Our Scheme optimizes computational and storage resources efficiently. We have done exhaustive observation the outcome shows that our scheme provides better performance than the conventional scheme in terms of enhanced storage capability, high Quality of User Experience, and low energy consumption.

关键词： Cloud Computing Distributed Computing computational offloading Game Theory Multi-Access Edge Computing Dew Computing

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Dynamic Service Placement in Edge Computing: A Comparative Evaluation of Nature-Inspired Algorithms

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IEEE ACCESS 2025年 13卷 2653-2670页

作者： Kazmi, Aqeel H. Staffolani, Alessandro Zhang, Tianhao Cabrera, Christian Clarke, Siobhan Trinity Coll Dublin Sch Comp Sci & Stat Dublin 2 Ireland Univ Bologna Dept Comp Sci & Engn I-40126 Bologna Italy Univ Cambridge Dept Comp Sci & Technol Cambridge CB2 1TN England

Edge computing has emerged as a promising solution for delivering services that demand low latency, high bandwidth, and stringent privacy requirements in numerous data- and compute-intensive applications, such as those in Smart Cities. Heterogeneity in edge computing resources and diverse application requirements demand adaptive optimization techniques, such as service placement, to conform to changing conditions. A service placement model must optimize the selection of edge nodes for deploying and executing services, thereby improving application QoS and maximizing resource utilization. Numerous optimization techniques for adaptive service placement problem have been proposed in the recent past. However, in most cases, the results have been evaluated in limited scenarios. This paper presents a comprehensive comparative study evaluating representative optimization algorithms applied to the problem of dynamic service placement across various application scenarios. The study covers nature-inspired approaches, including both meta-heuristics and reinforcement learning. Our experimental findings offer valuable insights into the strengths and weaknesses of the selected nature-inspired algorithms for service placement optimization, evaluated for applications with different QoS requirements. In our analysis, the Genetic Algorithm shows superior performance in achieving lower average distance and the average number of servers selected. Particle Swarm Optimization excels in minimizing average waiting time and placement decision time. The Artificial Bee Colony maintains low average latency, whereas the RL Proximal Policy Optimization demonstrates superior performance in terms of balancing the utilization of network resources.

关键词： Servers Cloud computing Edge computing Privacy Quality of service Microservice architectures Heuristic algorithms Metaheuristics Low latency communication computational modeling dynamic service placement multi-objective optimization meta-heuristics nature-inspired algorithms service offloading computational offloading service scheduling

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An Adaptable Pricing-Based Resource Allocation Scheme Considering User offloading Needs in Edge Computing

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IEEE INTERNET OF THINGS JOURNAL 2025年第1期12卷 582-594页

作者： Liao, Zhuofan Han, Xiyu Tang, Xiaoyong Feng, Chaochao Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Peoples R China Natl Univ Def Technol Coll Comp Sci Changsha 410073 Peoples R China

Multiaccess edge computing (MEC) is extensively utilized within the Internet of Things (IoT), wherein end-users pay services to meet the latency demands of their respective tasks. The pricing is impacted not solely by the quantity of data offloaded by the user but also associated with the leased computing and communication resources. Nevertheless, prevailing pricing strategies seldom account for the personalized resource requisites during user offloading. In this article, we present an adaptive pricing-oriented approach for concomitant task offloading and resource allocation, considering hybrid resources, comprising two key components. First, we propose a differential pricing framework for communication and computation resources, where the unit price will be influenced by the proportion of resources rented by users. Subsequently, we design a two-stage Stackelberg game model: 1) employing convex optimization theory to mitigate problem intricacies and 2) employing gradient descent to ascertain the potentially optimal price, thus achieving a balance between minimizing user expenses and maximizing server profitability. Simulation outcomes demonstrate that our approach slashes user costs by 23.3% and enhances average server revenue by 65.6% compared to a flat pricing model with a high-user request rate (five user-initiated requests per 100 ms). This maintains server occupancy within 60% to 80%, thereby alleviating user queuing and refining user Quality of Experience (QoE).

关键词： Pricing Resource management Servers Costs Internet of Things Optimization Games computational offloading multiple-access edge computing (MEC) offloading decision pricing resources allocation Stackelberg game

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Parking Vehicle-Assisted Task offloading in Edge Computing: A dynamic multi-objective evolutionary algorithm with multi-strategy fusion response☆

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Zhou, Yingbo Chai, Zheng-Yi Li, Ya-Lun Li, Jun-Jie Tiangong Univ Sch Comp Sci & Technol Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Quanzhou Vocat & Tech Univ Quanzhou 362268 Fujian Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

Vehicle-edge computing, as a promising paradigm, is employed to support applications that require low latency and high computational capability. In this study, we consider the idle resources of the surrounding parked vehicles (PVs) and roadside units (RSUs) as service providers to enhance the performance of User Equipment (UE). We propose a joint offloading architecture that uses parked vehicles. Additionally, owing to the dynamic and uncertain nature of the environment, we model computation offloading as a dynamic multi-objective optimization problem to simultaneously optimize the latency and energy consumption of UE applications. In this study, we propose a dynamic multi-objective evolutionary algorithm with a multi-strategy fusion response (DMOEA/D-MSFR). Specifically, we introduce a population center positioning strategy and a learnable prediction mechanism using Long Short-Term Memory (LSTM) in DMOEA-MSFR, which divides the prediction optimization process into two stages and exhibits a rapid response to environmental changes. In the static optimization phase, an adaptive weight vector adjustment strategy is employed, which significantly aids in the distribution and diversity of the solutions. Comprehensive experiments demonstrate that our proposed framework balances the trade-off between latency and energy consumption, and the convergence, feasibility, and diversity of the non-dominated solutions obtained.

关键词： Vehicle edge computing Dynamic multi-objective optimization Evolutionary algorithms computational offloading Vehicle collaboration

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