检索结果-内蒙古大学图书馆

Research on computing offloading strategy based on Genetic Ant Colony fusion algorithm

SIMULATION MODELLING PRACTICE AND THEORY 2022年 118卷

作者： Xu, Fei Qin, Zengshi Ning, Linpeng Zhang, Zhuoya Xian Technol Univ Sch Comp Sci & Engn Xian Peoples R China TheStateand Prov Joint Engn Lab Adv Network Monit Engn Lab Xian Peoples R China

As the key technology of edge computing, computing offloading has attracted the attention of many scholars in recent years. Many people use heuristic algorithm as the basic algorithm to study the algorithm of computing offloading, but a single heuristic algorithm has some defects, such as some will fall into local optimal, and some will converge prematurely. In order to make up for the defects of single heuristic algorithm applied to the calculation offloading and improve the efficiency of the algorithm, this paper combines genetic algorithm with ant colony algorithm, and designs the calculation offloading strategy of gene-ant colony fusion algorithm. Firstly, a group of solutions are obtained through the selection, crossover, mutation and other operations of genetic algorithm, and the solution is improved as the initial solution of ant colony algorithm. The fusion algorithm makes full use of the feedback value of genetic algorithm and the high efficiency of ant colony algorithm to overcome the shortcomings of the two algorithms. The feasibility of the algorithm is verified by several groups of experiments. The simulation results show that compared with GA, ACA and PSO, the number of iterations is reduced by 17.96%, 24.43% and 36.25% respectively. When the base station remains unchanged, the G-ACA has the lowest objective function value. Compared with GA, ACA and PSO algorithm, the objective function value is reduced by 36.68%, 16.15% and 11.35% respectively. That is, the fused algorithm is better than the non fused GA,ACA and PSO in time delay, energy consumption and objective function value.

关键词： Mobile edge computing computing offloading Genetic algorithm Ant colony algorithm

来源：评论

学校读者我要写书评

暂无评论

JORA: Blockchain-based efficient joint computing offloading and resource allocation for edge video streaming systems

引用

JOURNAL OF SYSTEMS ARCHITECTURE 2022年 133卷

作者： Yuan, Shijing Li, Jie Wu, Chentao Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

Edge computing has been widely applied in video streaming systems, aiming to provide low-latency video streaming services. However, limited resources and a lack of incentivization affect the performance of edge video streaming systems. In this paper, we propose an efficient Joint computing offloading and Resource Allocation (JORA) method for blockchain-supported edge video streaming systems (BEVS). Specifically, we develop a novel incentive mechanism based on the blockchain smart contract to enable automatic incentivization. Meanwhile, we customize a system prototype of BEVS on Amazon EC2 using Apache Storm. Preliminary experiments with the customized prototype reveal the coupling impacts of the offloading mode, the allocation scheme, and video redundancy on system performance and resource demand. Motivated by the results, we formulate joint computing offloading, resource allocation, and video adaptive compression as an optimization problem. We transform the original problem into a convex problem and a mixed-integer programming problem (MIP). We then develop an algorithm based on the generalized Benders decomposition (GBD) to solve the original problem in an iterative way. Extensive experiments demonstrate that the proposed scheme achieves full resource utilization and an effective trade-off between energy consumption and accuracy.

关键词： Video streaming Resource allocation computing offloading Blockchain Incentive mechanism

来源：评论

学校读者我要写书评

暂无评论

A High Reliable computing offloading Strategy Using Deep Reinforcement Learning for IoVs in Edge computing

引用

JOURNAL OF GRID computing 2021年第2期19卷 15-15页

作者： Wang, Kun Wang, Xiaofeng Liu, Xuan Shanxi Univ Coll Phys & Elect Engn Taiyuan 030006 Shanxi Peoples R China State Grid Shanxi Elect Power Co Maintenance Branch Taiyuan 030000 Shanxi Peoples R China

In view of the serious problems of increasing delay energy consumption and decreasing service quality caused by complex network state and massive computing data in Internet of vehicles (IOT), a high reliable computing offloading strategy based on edge computing is proposed in this paper. Firstly, the architecture of the Internet of vehicles is designed. The real-time business of terminal vehicles is directly unloaded to the mobile edge computing (MEC) equipment for processing, which reduces the high transmission delay of data on the core network. The combination of software defined network (SDN) and MEC is used to provide flexible network control and centralized resource management for the Internet of vehicles. Then, according to the computing model, communication model and privacy protection model, a joint computing offload and resource allocation strategy is proposed. Finally, taking the shortest time delay and minimum computing cost as the optimization objectives, Q-learning is used to solve the problem to achieve the optimization of unloading strategy, that is, the optimal allocation of communication and computing resources, and the system security is the best. Based on the Matlab simulation platform, the system model is built to carry out the experimental test. The results show that compared with other strategies, the unloading strategy can achieve fast convergence and reduce the system overhead effectively, and the computation complexity, data size and the number of computation nodes have the least impact on the delay.

关键词： Internet of vehicles Edge computing computing offloading Software defined network Privacy protection Deep reinforcement learning algorithm

来源：评论

学校读者我要写书评

暂无评论

A computing offloading Resource Allocation Scheme Using Deep Reinforcement Learning in Mobile Edge computing Systems

引用

JOURNAL OF GRID computing 2021年第3期19卷 35-35页

作者： Li, Xuezhu Suzhou Univ Dept Informat Engn Suzhou 234000 Anhui Peoples R China

Aiming at the problems of increased latency and energy consumption and decreased service quality caused by current vehicle networks, this paper proposes a computing offloading resource allocation strategy based on deep reinforcement learning in Internet of Vehicles. Firstly, the system architecture for Internet of Vehicles is designed, calculation model and communication model of computing offloading strategy are constructed. Then, the resource allocation problem in offloading process is studied for real-time energy-aware offloading scheme in mobile edge computing. Besides, considering the battery capacity of vehicle users, the remaining energy rate is utilized to redefine weighting factors to sense energy consumption in real time. Finally, with the shortest delay and smallest computational cost as optimization goals, Q-learning is used to achieve the optimization of offloading strategy, that is, the optimal allocation of communication and computing resources, and the best system security. The simulation results show that the delay of the proposed algorithm is 0.442 s when the computational complexity is 9000 cycles/byte, and the performance of the delay is improved compared with the other three algorithms.

关键词： Deep reinforcement learning computing offloading Mobile edge computing computing resources Task allocation offloading decision

来源：评论

学校读者我要写书评

暂无评论

Deep reinforcement learning-based edge computing offloading algorithm for software-defined IoT

引用

COMPUTER NETWORKS 2023年 235卷

作者： Zhu, Xiaojuan Zhang, Tianhao Zhang, Jinwei Zhao, Bao Zhang, Shunxiang Wu, Cai Anhui Univ Sci & Technol Huainan Huainan 232001 Peoples R China

Edge computing offloading can effectively solve the problem of insufficient computing resources for terminal devices and improve the performance and efficiency of the system. When network states and tasks change rapidly, data-driven intelligent algorithms have difficulty obtaining comprehensive statistics for accurate pre-diction, resulting in degraded performance of computational offloading and difficulty in adaptive adjustment. It is a current challenge to improve the environment-aware, intelligent optimization so that the computational offloading algorithm can adapt to the dynamic changes in network state and task demands, thus achieving global multi-objective optimization. This paper presents optimized edge computing offloading algorithm for software-defined IoT. First, to provide global state for making decisions, a software defined edge computing (SDEC) architecture is proposed. The edge layer is integrated into the control layer of software-defined IoT, and multiple controllers share the global network state information via east-west message exchange. Moreover, an edge computing offloading algorithm in software-defined IoT (ECO-SDIoT) based on deep reinforcement learning is proposed. It enables the controllers to offload the computing task to the most appropriate edge server according to the global states, task requirements, and reward. Finally, the performance metrics for edge computing offloading were evaluated in terms of unit task processing latency, load balancing of edge servers, task processing energy consumption, and task completion rate, respectively. Simulation results show that ECO-SDIoT can effectively reduce task completion time and energy consumption compared with other strategies.

关键词： Edge computing computing offloading Software defined network Internet of things Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Two-stage computing offloading algorithm in cloud-edge collaborative scenarios based on game theory

引用

COMPUTERS & ELECTRICAL ENGINEERING 2022年 97卷 107624-107624页

作者： Xu, Fei Xie, Yue Sun, Yongyong Qin, Zengshi Li, Gaojie Zhang, Zhuoya Xian Technol Univ Xian Peoples R China State & Prov Joint Engn Lab Adv Network Monitorin Xian Peoples R China

Mobile Edge computing (MEC) enables smart mobile devices to handle computationally intensive tasks with limited resources. By offloading tasks to the edge of the network closer to mobile devices, mobile devices can provide users with intelligent services with shorter latency and lower energy consumption than cloud computing. while mobile edge computing is superior in terms of reliability and low latency, it is still not a complete replacement for cloud computing. In order to make full use of the large amount of network resources of cloud computing as well as to take advantage of parallel computing, this paper investigates the partial offloading of multiple mobile devices in the cloud-side collaboration scenario. Through game theory, we first obtain the edgeto-terminal optimal offloading decision, then find the cloud-edge optimal offloading decisions for all terminal devices that choose to offload to the edge, and finally reach the goal of optimal offloading on the three sides of the cloud-edge-terminal. Based on this idea, we propose a twostage computing offloading algorithm (TSGTA). The whole algorithm is divided into two stages, and both stages are designed to offload algorithms based on game theory. The simulation results show that under the time-delay constraint, this method has an average improvement of 32% compared with the traditional method.

关键词： Cloud-edge collaboration computing offloading DAG task model Energy consumption optimization Game theory

来源：评论

学校读者我要写书评

暂无评论

An Adaptive computing offloading and Resource Allocation Strategy for Internet of Vehicles Based on Cloud-Edge Collaboration

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年

作者： Shu, Wanneng Yu, Haoxin Zhai, Cao Feng, Xuanxuan South Cent Minzu Univ Coll Comp Sci Wuhan 430074 Peoples R China

With the development of the Internet of Vehicles (IoV) industry, the introduction of cloud-edge collaboration has greatly enhanced the computing capabilities of vehicle networks. However, optimizing computing offloading and resource allocation strategies in IoV to reduce latency and energy consumption at the vehicle terminals remains a challenge. This paper proposes an Adaptive computing offloading and Resource Allocation Strategy (ACORAS) for IoV based on cloud-edge collaboration. Firstly, a Vehicles-Collaborative Road Side Units-Cloud (VCRSUC) system architecture is constructed by considering the use of idle resources on edge servers at remote Road Side Unit (RSU) to reduce the total cost at the vehicle terminals. Secondly, the discrete particle swarm optimization algorithm is combined with chaotic mapping and Cauchy mutation, and dynamically adjusts weights and learning factors based on variable updates. Finally, our proposed ACORAS gradually approaches the optimization of the computing offloading decisions and resource allocation decisions through iterative calculations. Simulation results show that our proposed ACORAS can effectively reduce the total cost while considering latency and energy consumption, demonstrating superior performance compared to traditional algorithms.

关键词： Servers Resource management Cloud computing Collaboration Energy consumption Delays Computational modeling Internet of Vehicles cloud-edge collaboration computing offloading resource allocation cauchy mutation

来源：评论

学校读者我要写书评

暂无评论

Fog-Based computing and Storage offloading for Data Synchronization in IoT

引用

IEEE INTERNET OF THINGS JOURNAL 2019年第3期6卷 4272-4282页

作者： Wang, Tian Zhou, Jiyuan Liu, Anfeng Bhuiyan, Md Zakirul Alam Wang, Guojun Jia, Weijia Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Fujian Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Fordham Univ Dept Comp & Informat Sci New York NY 10458 USA Guangzhou Univ Sch Comp Sci & Educ Software Guangzhou 510006 Guangdong Peoples R China Univ Macau Data Sci Ctr Macau Peoples R China

With the development of Internet of Things (IoT) technologies, increasingly many devices are connected, and large amounts of data are produced. By offloading the computing-intensive tasks to the edge devices, cloud-based storage technology has become the mainstream. However, if the end IoT devices send all of their data to the cloud, then data privacy becomes a great issue. In this paper, we propose a new architecture for data synchronization based on fog computing. By offloading part of computing and storage work to the fog servers, the data privacy can be guaranteed. Moreover, to decrease the communication cost and reduce the latency, we design a differential synchronization algorithm. Furthermore, we extend the method by introducing Reed-Solomon code for security consideration. We prove that our architecture and algorithm really have better performance than traditional cloud-based solutions in terms of both efficiency and security through a series of experiments.

关键词： Cloud computing cloud storage computing offloading differential synchronization fog computing

来源：评论

学校读者我要写书评

暂无评论

Energy efficient computing offloading mechanism based on FPGA cluster for edge cloud 20

Energy efficient computing offloading mechanism based on FPG...

引用

Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

作者： Yanwei Wang Hongwei Kan Dongdong Su Yanmei Shen Wei Liu Mingyang Ou Architecture Research Department Inspur Electronic Information Industry Co. Ltd Beijing China Inspur Electronic Information Industry Co. Ltd and AI Institute China University of Mining and Technology Beijing China Architecture Research Department Inspur Electronic Information Industry Co. Ltd Jinan China

ISBN: (纸本)9781450387811

Towards the computing overload problem in cloud data center, we propose a computing offloading mechanism based on FPGA clusters for edge cloud. Firstly, the FPGA cluster we proposed is decoupled from traditional server, and FPGA BOX is used to power batch FPGA accelerators. The network-based FPGA cluster is deployed at the edge to undertake the computing tasks of the data center. On this basis, we propose an edge cloud network model based on FPGA clusters, and study the energy consumption minimization problem. Secondly, considering the diversity of computing tasks, the MapReduce algorithm based on the numbering mechanism proposed in this paper realizes the classification of computing tasks, and converts the energy consumption minimization problem into the energy consumption minimization problem when the user's computing tasks are determined. Based on the classification results, our improved Hungary algorithm can obtain the minimum energy consumption. Meantime, the computing task results can feedback to corresponding user according to the user information in classification results. Finally, numerical calculation results show that the computing offloading mechanism proposed in this paper shows good performance in terms of energy consumption.

关键词： numbering mechanism FPGA computing offloading edge cloud Hungary algorithm energy consumption

来源：评论

学校读者我要写书评

暂无评论

Improved NSGA-II algorithm-based task offloading decision in the internet of vehicles edge computing scenario

引用

MULTIMEDIA SYSTEMS 2025年第1期31卷 1-14页

作者： Zhu, Sifeng Duan, Haowei Yao, Yaxing Chen, Hao Zhu, Hai Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin 300384 Peoples R China Zhoukou Normal Univ Sch Network Engn Zhoukou 466001 Peoples R China

In the context of Mobile Edge computing (MEC) for the Internet of Vehicles (IoV), vehicles can establish communication connections with other entities to access relevant IoV services. However, current research on offloading schemes rarely considers the utilization of idle computing resources available on the road. To address this issue and provide a more efficient offloading scheme that makes more efficient use of computing resources, we propose a vehicle-edge-cloud collaborative offloading scheme incorporating Vehicle-to-Vehicle (V2V) communication. Our scheme effectively utilizes the computing resources of Task-Requesting Vehicles (TRVs), Idle computing Resource Vehicles (ICRVs), edge devices, and the cloud. First, a vehicle position determination mechanism is designed to ensure the stability of the task offloading process. Second, latency models, energy consumption models, task completion quality models, and multi-objective optimization models are constructed. In addition, an improved NSGA-II algorithm is proposed for task offloading decisions. Finally, the feasibility and stability of the proposed scheme are validated through simulation experiments. The results show that, compared with other schemes, the proposed scheme significantly improves system latency, energy consumption, and service quality.

关键词： Internet of vehicles computing offloading Cloud-edge collaboration Multi-objective optimization algorithm NSGA-II

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：