计算机科学
云计算
工作流程
分布式计算
延迟(音频)
边缘计算
资源配置
服务质量
GSM演进的增强数据速率
可靠性(半导体)
能源消耗
粒子群优化
算法
计算机网络
数据库
人工智能
操作系统
电信
功率(物理)
生态学
物理
量子力学
生物
作者
J. Li,Zhiwei Qin,Liu Wei,Xiao Yu
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-1
被引量:1
标识
DOI:10.1109/jiot.2023.3315339
摘要
With the rapid development of intelligent Internet of Things (IoT) technology, many intensive computing workflow applications have been generated every day. Edge-cloud collaboration computing is a promising computational paradigm that combines the advantages of both edge and cloud to improve the application QoS, shorten the time latency, and reduce the energy of the terminals. However, joining the heterogeneous resources for edge-cloud workflows efficiently and safely is still challenging. In this article, we develop an Energy-aware and Trust-collaboration Cross-domain Resource Allocation algorithm (ETCRA) for edge-cloud workflows. The objective is to minimize the Comprehensive System Function (CSF) while guaranteeing the latency constraints of the workflows and trust constraints of the cross-domain edges. A dynamic algorithm is proposed to solve the formulated problem and to obtain the optimal task-resources mapping decision. It consists of two phases: 1) initial resource allocation decision-making based on Particle Swarm Optimization (PSO) statically; and 2) real-time updating decision-making based on the trust value assessment dynamically. Simulation results verify the effectiveness of ETCRA and prove that the proposed scheme significantly outperforms other baselines on four key measurements, including the CSF, total execution time, total energy consumption, and reliability performance.
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