INT Based Network-Aware Task Scheduling for Edge Computing

Edge computing promises low-latency computation for delay sensitive applications by processing data close to its source. Task scheduling in edge computing is however not immune to performance fluctuations as dynamic and unpredictable nature of network traffic can adversely affect the data transfer performance between end devices and edge servers. In this paper, we leverage In-band Network Telemetry (INT) to gather fine-grained, temporal statistics about network conditions and incorporate network-awareness into task scheduling for edge computing. Unlike legacy network monitoring techniques that collect port-level or flow-level statistics at the order of tens of seconds, INT offers highly accurate network visibility by capturing network telemetry at packet-level granularity, thereby presenting a unique opportunity to detect network congestion precisely. Our experimental analysis using various workload types and network congestion scenarios reveal that enhancing task scheduling of edge computing with high-precision network telemetry can lead up to 40% reduction in data transfer times and up to 30% reduction in total task execution times by favoring edge servers in uncongested (or mildly congested) sections of network when scheduling tasks.