## 6 Discussion
### Decision Criteria for Applying the Proposed Approach  
The decision to apply the proposed approach to large, heterogeneous datasets must be based on an assessment of several interrelated factors. First, the structure and composition of the data--including tabular records, semi-structured formats (JSON, XML), and unstructured content (text, images, time series)--directly influences preprocessing requirements, memory allocation, and network bandwidth utilization [94]. Second, the characteristics of available computational and networking resources--namely, the heterogeneity of compute nodes [95] (CPU versus GPU capabilities, clock speeds, and memory capacities) and the throughput and latency of inter-node communication--determine the feasibility of workload balancing and the frequency of gradient synchronizations without incurring excessive delays [96]. Third, performance and reliability requirements--such as stringent training-time constraints in real-time monitoring scenarios and the necessity for fault tolerance in case of node or network failures--define the trade-off between latency minimization and system resilience [97]. Fourth, economic and organizational constraints--including budgetary limits on resource provisioning and energy consumption, as well as project timelines and future scalability needs--must be weighed against anticipated improvements in training efficiency and model accuracy [98]. Finally, a multi-objective optimization framework employing Pareto-based analysis and user-defined weightings for cost, performance, and availability provides the methodological basis for navigating these trade-offs. Integration of these considerations enables identification of an optimal distributed computing configuration for artificial neural network modeling on any given large, heterogeneous dataset. The relationship between cost, performance, and availability in the selection of a DCS can be characterized as a tripartite trade-off, in which improvement along one dimension frequently entails compromise in one or both of the others. Cost is typically quantified in terms of capital and operational expenditures--covering hardware acquisition, energy consumption, and maintenance overhead--while performance is assessed by metrics such as throughput (e.g., examples per second), latency of individual tasks, and overall time-to-solution. Availability, by contrast, reflects the system's ability to sustain service in the face of component failures and transient network disruptions, often expressed as mean-time-between-failures (MTBFs) or expected uptime percentage [99]. From an optimization standpoint, these three objectives conflict: lowering cost by employing fewer or less powerful nodes generally degrades performance and may reduce redundancy mechanisms crucial for high availability; conversely, over-provisioning for peak performance or fault tolerance increases both capital outlay and ongoing energy costs. To navigate these interdependencies, a multi-objective formulation is adopted whereby each candidate DCS configuration is evaluated along the axes of total cost, benchmarked performance, and modeled availability. Pareto-optimal fronts are then constructed to identify configurations for which no single metric can be improved without degrading another [100]. Within this framework, user-defined weightings enable explicit prioritization--e.g., \"minimize cost subject to a minimum 99.9% uptime\" or \"maximize throughput within a fixed budget\". Sensitivity analysis on these weights reveals the regions of diminishing returns; for instance, doubling node count may yield only marginal latency gains beyond a certain scale, while the incremental cost of additional redundancy may plateau in terms of availability improvement once required reliability thresholds are surpassed.