Edge computing places computation closer to users and data sources, but introduces heterogeneous hardware, limited resources, and variable network conditions. WebAssembly (Wasm) is increasingly considered for these environments because modules are compact, portable, and quick to start. However, cold-start latency remains a challenge: when a worker node does not yet hold the required artifact, it must fetch it before execution, adding transfer overhead that directly increases the end-to-end (E2E) latency, especially for large artifacts or slow network links. At the same time, the geographical distance between the requester and the assigned worker independently contributes to latency through network routing overhead. Together, artifact locality and network proximity do not always point to the same placement decision, and naive scheduling strategies fail to account for either. This work extends a Wasm-native edge orchestrator with state-aware scheduling strategies that jointly optimize for both objectives. Evaluation on a Raspberry Pi testbed with emulated regional network latency shows that a hybrid scheduler combining cache-state and network-proximity signals reduces mean E2E latency by up to 40.8% compared to a default round-robin baseline. Artifact locality-aware placement is most effective when artifacts are large or repeatedly requested, while network-aware placement dominates when artifacts are small and routing latency has a larger relative effect, but combining both objectives consistently yields the lowest E2E latency across diverse edge workloads.

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