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The increasing number of connected IoT devices produces massive amounts of sensed data at the network edge. A new computing paradigm, called Things-Edge-Cloud (TEC) collaboration, has been proposed to meet real-time and high-throughput requirements. Most of the existing work focuses on workload scheduling across the computing nodes in TEC with ad-hoc implementations using runtime and management frameworks designed for the cloud. In this paper, we propose RSEP to model the entities and their relationship in the TEC computing continuum (TEC<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>). We then design and implement Tide—a distributed runtime management framework for TEC<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> based on the RSEP model, which enables elastic resource allocation and seamless computation offloading. It employs runtime environment isolation and physical resource binding to enforce strong isolation without incurring performance penalties. To decouple runtime and framework, Tide provides a set of portable application interfaces that allow the management of variety runtimes. We implement Tide from scratch and compare its latency and throughput with KubeEdge, Ray, and bare-metal implementations. Experimental results show that Tide improves throughput by 2x and reduces average latency, 95th percentile latency, and latency standard deviation by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${4 5. 8 2 \%, 4 8. 3 6 \%}$</tex>, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${2 8. 9 6 \%}$</tex>, respectively. Specifically, Tide achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${8 7. 3 \%}$</tex> of the ideal goodput, exceeding other platforms more than 10x.