The proliferation of Internet of Things (IoT) devices operating in swarm-like formations—from autonomous device fleets to federated service networks— demands a computing continuum that spans edge nodes, fog gateways, and cloud back-ends. Service meshes such as Istio provide transparent traffic management, observability, and policy enforcement for the microservice fabrics that underpin these continuums, yet their scalability behaviour under diverse coordination patterns and increasing load intensity remains poorly characterised. This paper introduces a two-axis taxonomy for benchmarking service-mesh scalability in IoT-to-cloud microservice continuums: Axis A captures four scaling tiers (Baseline, Moderate, High, Extreme) derived from offered load and concurrency, while Axis B distinguishes two coordination categories—federated/consensusdriven rollout and peer-to-peer cooperative inference/control. We instantiate the taxonomy across six representative IoT scenarios, each exercised through multiphase Fortio load campaigns over an Istio-meshed Kubernetes cluster. Analysing 51 benchmark runs, we report percentile latency distributions (P50, P90, P99), load fidelity, and throughput–tail coupling. Results reveal that peer-to-peer cooperative workloads exhibit a 10× P99 inflation from Baseline to Extreme tiers, whereas federated workloads remain comparatively stable. The A×B grid exposes otherwise hidden interaction effects between coordination topology and scale, providing actionable design guidance for service-mesh capacity planning and auto-scaling in IoT microservice deployments.