  Deploying data- and computation-intensive applications such as large-scale AI
into heterogeneous dispersed computing networks can significantly enhance
application performance by mitigating bottlenecks caused by limited network
resources, including bandwidth, storage, and computing power. However, current
resource allocation methods in dispersed computing do not provide a
comprehensive solution that considers arbitrary topology, elastic resource
amount, reuse of computation results, and nonlinear congestion-dependent
optimization objectives. In this paper, we propose LOAM, a low-latency joint
communication, caching, and computation placement framework with a rigorous
analytical foundation that incorporates the above aspects. We tackle the
NP-hard aggregated cost minimization problem with two methods: an offline
method with a 1/2 approximation and an online adaptive method with a bounded
gap from the optimum. Through extensive simulation, the proposed framework
outperforms multiple baselines in both synthesis and real-world network
scenarios.
