Fine-Grained Service Lifetime Optimization for Energy-Constrained Edge-Edge Collaboration

Collaborative edge computing has been widely advo-cated by network operators and service providers to promote the quality of service (QoS), provisioning diverse delay-sensitive and computation-intensive applications. Existing studies mainly focus on cloud-edge collaboration, since cloud servers have massive resources to provide diverse services and edge servers can provide low-delay services with close proximity to end users. However, in scenarios that capture privacy, e.g., personal bioinformation and business areas, there is a great need for zero cloud involvement. Moreover, current edge servers are typically energy-constrained, which poses great challenges in enabling high-QoS services in ever-densely deployed edge networks. To tackle these issues, in this paper, we study the energy-constrained edge-edge collaboration problem. First, we formulate the edge-edge collaboration with delay minimization and energy reduction aims and prove its NP-hardness. Second, we propose a novel Fine-Grained Service Lifetime Optimization (FGSLO) scheme as a possible solution. The problem is then transformed and decoupled into three sub-problems, namely service placement, service lifetime decision, and task scheduling, which are solved by our proposed method, respectively. Finally, real-world data-driven experimental results show that FGSLO is capable of reducing 21.4%~90.1 % system delay in different energy-constrained scenarios, compared to baselines without service lifetime control.