Dynamic Caching Dependency-Aware Task Offloading in Mobile Edge Computing

计算机科学 移动边缘计算 任务(项目管理) 计算卸载 依赖关系(UML) 边缘计算 移动计算 GSM演进的增强数据速率 分布式计算 并行计算 计算机网络 计算机体系结构 嵌入式系统 服务器 人工智能 管理 经济
作者
Liang Zhao,Zijia Zhao,Ammar Hawbani,Zhi Liu,Zhiyuan Tan,Keping Yu
出处
期刊:IEEE Transactions on Computers [Institute of Electrical and Electronics Engineers]
卷期号:74 (5): 1510-1523 被引量:16
标识
DOI:10.1109/tc.2025.3533091
摘要

Mobile Edge Computing (MEC) is a distributed computing paradigm that provides computing capabilities at the periphery of mobile cellular networks. This architecture empowers Mobile Users (MUs) to offload computation-intensive applications to large-scale computing nodes near the edge side, reducing application latency for MUs. The resource allocation and task offloading in MEC has been widely studied. However, the burgeoning complexity inherent to modern applications, often represented as Directed Acyclic Graphs (DAGs) comprising a multitude of subtasks with interdependencies, poses huge challenges for application offloading and resource allocation. Meanwhile, previous work has neglected the impact of edge caching on the offloading execution of dependent tasks. Therefore, this paper introduces a novel dynamic caching dependency-aware task offloading (CachOf) scheme. First, to effectively enhance the rationality of cache and computing resource allocation, we develop a subtask priority computation scheme based on DAG dependencies. This scheme includes the execution sequence priority of subtasks on a single MU and the offloading sequence priority of subtasks from multiple MUs. Second, a dynamic caching scheme, designed to cater to dependent tasks, is proposed. This caching approach can not only assist offloading decisions, but also contribute to load balancing by harmonizing caching resources among edge servers. Finally, based on the task prioritization results and caching results, this paper presents a Deep Reinforcement Learning (DRL)-based offloading scheme to judiciously allocate resources and improve the execution efficiency of applications. Extensive simulation experiments demonstrate that CachOf outperforms other baseline schemes, achieving improved execution efficiency for applications.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刻苦的丹妗完成签到,获得积分10
1秒前
NexusExplorer应助zhutier采纳,获得10
1秒前
1秒前
健壮鸡翅完成签到 ,获得积分10
1秒前
lpp完成签到 ,获得积分10
1秒前
ajing完成签到,获得积分10
2秒前
hh完成签到 ,获得积分10
2秒前
雅樱完成签到,获得积分10
3秒前
西瓜西瓜完成签到,获得积分10
4秒前
年华完成签到,获得积分10
4秒前
霍弃疾完成签到,获得积分10
6秒前
6秒前
KOBE94FU完成签到,获得积分10
7秒前
齐朕完成签到,获得积分10
7秒前
梁正凤完成签到,获得积分10
8秒前
王山完成签到,获得积分10
8秒前
大福蛙发布了新的文献求助20
9秒前
左传琦完成签到,获得积分10
10秒前
村上春树的摩的完成签到 ,获得积分10
11秒前
不想看文献完成签到,获得积分10
11秒前
岁月如歌完成签到 ,获得积分0
11秒前
愉快又莲完成签到,获得积分10
12秒前
12秒前
健康的鸽子完成签到,获得积分10
12秒前
14秒前
及时雨完成签到 ,获得积分10
15秒前
认真的可冥完成签到,获得积分10
15秒前
15秒前
Owen应助科研通管家采纳,获得10
16秒前
16秒前
充电宝应助科研通管家采纳,获得10
16秒前
16秒前
小蘑菇应助科研通管家采纳,获得10
16秒前
16秒前
NexusExplorer应助科研通管家采纳,获得10
16秒前
Jasper应助科研通管家采纳,获得10
16秒前
华仔应助科研通管家采纳,获得10
16秒前
bkagyin应助科研通管家采纳,获得10
16秒前
张之琪关注了科研通微信公众号
17秒前
D1完成签到 ,获得积分10
18秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Development Across Adulthood 800
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
天津市智库成果选编 600
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6444873
求助须知:如何正确求助?哪些是违规求助? 8258696
关于积分的说明 17592214
捐赠科研通 5504599
什么是DOI,文献DOI怎么找? 2901598
邀请新用户注册赠送积分活动 1878587
关于科研通互助平台的介绍 1718214