JUST: Cost-efficient joint cluster upgrade sequencing and task scheduling for containerized edge computing

Abstract

The rise of Edge Computing (EC) has led to an increased deployment of applications on edge servers using containers, which rely on multi-layer image files that often need to be upgraded. While these upgrades are essential, traditional cloud upgrade strategies are not suitable for EC due to resource constraints, geographical distribution, and latency sensitivity. Consequently, efficiently updating containers in EC is a significant challenge. In this paper, we present the online layer-aware cost-efficient Joint cluster Upgrade Sequencing and Task scheduling (JUST) algorithm to quickly and efficiently address the upgrades in EC. Our JUST algorithm comprises two main mechanisms. First, we model the edge cluster upgrade problem by evaluating both short-term and long-term node upgrade costs along with task priorities, establishing a cluster upgrade sequencing mechanism to minimize disruptions to ongoing tasks. Second, we introduce a layer-aware task scheduling mechanism that reduces initial scheduling and upgrade-related rescheduling latency by optimizing layer transmission, downloads, and computation time. This approach minimizes upgrade disruptions by integrating node resources, layer distribution, and task characteristics through reinforcement learning and adaptive factorization networks. Finally, experiments with simulated and real datasets show that our JUST algorithm outperforms baseline methods, achieving a 47% improvement in total latency.