ELIMINATING CACHE-MISS STALLS IN REAL-TIME GRID TRAVERSAL

The platform performs real-time grid traversal across approximately 20,000 smart meters, substations and other grid nodes.

The dominant problem was not simply the computational complexity of graph traversal. The physical memory representation caused substantial processor stalls during access to traversal data, contributing to latency-boundary violations under peak telemetry activity.

Profiling identified memory-access behavior as the dominant performance constraint rather than the asymptotic complexity of the graph traversal algorithm.

The original Java implementation represented the graph through an object-heavy collection of Node and Relationship objects. Traversal consequently required repeated pointer chasing across the JVM heap instead of accessing compact, contiguous primitive data.

The graph was manageable in algorithmic terms, but its physical memory representation imposed substantial overhead on the execution path.

The profiling results changed the optimization target. Rather than attempting to optimize traversal while preserving the original object representation, the redesign addressed how graph data was physically laid out and accessed by the JVM and processor.

The resulting architecture separated two different workloads: frequent metric updates and relatively rare structural changes to the graph topology. Each could then use a representation and update strategy suited to its requirements.

The object-heavy graph representation was replaced with Compressed Sparse Row (CSR) data layout. Graph connectivity is represented by two primitive arrays:

int[] offsetArray int[] edgeArray

For a node, the offset array identifies the contiguous range of its outgoing edges: start = offsetArray[nodeId] end = offsetArray[nodeId + 1] The corresponding range in edgeArray contains neighboring node identifiers. Traversal therefore uses array indexing and sequential access through primitive data instead of following chains of Java object references.

Frequently accessed metric values are stored separately in a primitive long[] metricsArray. This array serves metric access; it is not part of the CSR connectivity structure defined by offsetArray and edgeArray.

The CSR representation improves spatial locality and provides a more predictable access pattern for the processor's memory hierarchy and hardware prefetching. It does not imply that every neighborhood fits into a single cache line.

Dynamic topology changes are handled separately from metric refreshes. A structural change builds a new CSR topology snapshot asynchronously. The topology arrays are not modified after publication, so a traversal can continue using its existing snapshot while the next version is being prepared.

Once the new topology snapshot is ready, an AtomicReference publishes it through an atomic reference replacement. Each traversal retains a local reference to the snapshot with which it started, so the topology it reads remains structurally stable throughout that traversal.

Retired topology snapshots become eligible for garbage collection after no active references remain. This avoids requiring traversal threads to wait for in-place reconstruction of the graph topology.

Frequently changing metrics are updated in-place using atomic primitives rather than rebuilding the topology for every telemetry update. Individual atomic updates do not imply that all metrics represent one transactionally consistent instant in time. The design preserves structural topology consistency while allowing telemetry values to evolve continuously.

Traversal state was moved into preallocated primitive working buffers. Temporary collections, iterator objects and other per-execution helper allocations were removed from the measured hot path.

The allocation result is scoped to measured application-level traversal allocation. It does not mean that the JVM or the entire application performed zero allocation; the quantified result is reported separately in the measured results.

The traversal path was evaluated against explicit latency thresholds so that its measured execution time could be compared with the downstream timing requirement.

The internal 470 ms threshold was a latency target, not an active timeout or a mechanism that interrupted traversal when the threshold was reached. It therefore did not guarantee that every traversal would complete within 470 ms. The observed maximum below 480 ms should not be interpreted as proof that the internal threshold was always met.

No breaches of the external 500 ms end-to-end boundary were observed during validation. Java on a general-purpose operating system does not provide hard real-time execution guarantees; the measurements describe the tested workload rather than a deterministic worst-case execution time.

The redesign aligned the physical representation of graph data with the traversal workload. Structural topology changes no longer required in-place reconstruction of the graph being traversed, while frequent metric updates remained independent of topology replacement.

The resulting architecture established a more efficient execution path by addressing memory access, update isolation and traversal working-state management together, rather than relying on algorithmic changes alone.