SRA-E-ABCO:面向云端环境的终端任务卸载

Shun Jiao, Haiyan Wang, Jian Luo
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引用次数: 0

摘要

互联网技术的飞速发展和智能应用的不断涌现,对任务卸载提出了更高的要求。在云-边缘-端(CEE)环境中,将终端设备的计算任务卸载到边缘服务器和云服务器可以有效减少系统延迟,缓解网络拥塞。在 CEE 环境中设计可靠的任务卸载策略以满足用户需求是一个具有挑战性的问题。为了设计有效的卸载策略,本文提出了针对云-边缘-终端环境的服务可靠性分析和精英-人工蜂群卸载模型(SRA-E-ABCO)。具体而言,提出了一种服务可靠性分析(SRA)方法,以协助预测终端任务的卸载必要性,并分析终端设备和边缘节点的属性。此外,还提出了一种精英人工蜂群卸载(E-ABCO)方法,该方法通过将精英种群与改进的适合度公式、位置更新公式和种群初始化方法相结合来优化卸载策略。在真实数据集上的仿真结果验证了所提方案的高效性能,与基线方案相比,该方案不仅减少了任务卸载延迟,还优化了系统开销。
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SRA-E-ABCO: terminal task offloading for cloud-edge-end environments
The rapid development of the Internet technology along with the emergence of intelligent applications has put forward higher requirements for task offloading. In Cloud-Edge-End (CEE) environments, offloading computing tasks of terminal devices to edge and cloud servers can effectively reduce system delay and alleviate network congestion. Designing a reliable task offloading strategy in CEE environments to meet users’ requirements is a challenging issue. To design an effective offloading strategy, a Service Reliability Analysis and Elite-Artificial Bee Colony Offloading model (SRA-E-ABCO) is presented for cloud-edge-end environments. Specifically, a Service Reliability Analysis (SRA) method is proposed to assist in predicting the offloading necessity of terminal tasks and analyzing the attributes of terminal devices and edge nodes. An Elite Artificial Bee Colony Offloading (E-ABCO) method is also proposed, which optimizes the offloading strategy by combining elite populations with improved fitness formulas, position update formulas, and population initialization methods. Simulation results on real datasets validate the efficient performance of the proposed scheme that not only reduces task offloading delay but also optimize system overhead in comparison to baseline schemes.
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