Hybrid Redundancy for Reliable Task Offloading in Collaborative Edge Computing

Abstract

Collaborative edge computing enables task execution on the computing resources of geo-distributed edge nodes. One of the key challenges in this field is to realize reliable task offloading by deciding whether to execute tasks locally or delegate them to neighboring nodes while ensuring task reliability. Achieving reliable task offloading is essential for preventing task failures and maintaining optimal system performance. Existing works commonly rely on task redundancy strategies, such as active or passive redundancy. However, these approaches lack adaptive redundancy mechanisms to respond to changes in the network environment, potentially resulting in resource wastage from excessive redundancy or task failures due to insufficient redundancy. In this work, we introduce a novel approach called Hybrid Redundancy for Task Offloading (HRTO) to optimize task latency and reliability. Specifically, HRTO utilizes deep reinforcement learning (DRL) to learn a task offloading policy that maximizes task success rates. With this policy, edge nodes dynamically adjust task redundancy levels based on real-time network load conditions and meanwhile assess whether the task instance is necessary for re-execution in case of task failure. Extensive experiments on real-world network topologies and a Kubernetes-based testbed evaluate the effectiveness of HRTO, showing a 14.6% increase in success rate over the benchmarks.