Incorporating Startup Delay into Collaborative Edge Computing for Superior Task Efficiency
Changfu Xu, Jianxiong Guo, Jiandian Zeng, Yupeng Li, Jiannong Cao, Tian Wang · 2024
Collaborative edge computing enables low service delay for many delay-sensitive Internet of Things applications through edge-edge and edge-cloud collaborations. Due to the limited edge resources and varying task demands, optimizing Joint Service Placement and Task Offloading (JSPTO) becomes crucial in minimizing overall processing delays. However, existing JSPTO methods overlook the impact of service startup delay, which may undermine total latency reduction, especially in scenarios with large startup delays. This paper introduces an online JSPTO method that integrates the consideration of service startup delay to enhance task offloading efficiency. However, a significant challenge is ensuring timely service response with large startup delays. We formulate this problem as an integer linear programming problem, aiming to minimize the total service startup and task processing delay. We propose a novel algorithm called SD-JSPTO, which performs online JSPTO in the presence of large startup delays. Theoretical performance analyses reveal that SD-JSPTO attains a near-optimal solution within polynomial time, demonstrating a competitive ratio of $1 + \frac{{{A_2}}}{{V{T^{{\text{opt}}}}}}$. Experimental evaluations demonstrate that our method significantly reduces the total delay by no less than 18.72% compared to state-of-the-art baseline methods while preserving system stability.