Search papers, labs, and topics across Lattice.
This paper presents a method for migrating microservices across virtual machines using eBPF for real-time discovery of inter-service dependencies without requiring application instrumentation. By analyzing 13,615 network events, the authors constructed a dependency graph that informed a traffic-aware migration plan, achieving a 27% reduction in cross-VM traffic exposure compared to a naive alphabetical migration order. The study highlights the trade-offs in performance overhead during data collection, suggesting optimal conditions for deployment to minimize latency impacts.
Real-time dependency discovery using eBPF can significantly optimize microservice migration, cutting cross-VM traffic exposure by 27% without needing application changes.
Migrating microservices across virtual machines (VMs) without knowledge of their runtime communication patterns risks creating cross-VM hotspots and latency spikes that are difficult to predict from static analysis alone. We use extended Berkeley Packet Filter (eBPF) kernel-level network tracing to automatically discover inter-service dependencies at runtime, with no application instrumentation, and use the resulting dependency graph to produce a traffic-aware migration plan ranked by return on investment (ROI). A two-pass process-identifier (PID) to port correlation algorithm recovers the identity of all 20 services in a shared-runtime testbed where processes are otherwise indistinguishable, matching the known ground-truth topology. The system discovers 32 dependency edges from 13,615 network events captured in three minutes, and applies spectral graph clustering with Kernighan-Lin refinement to partition services into VM-coherent groups. In simulation over the discovered graph, our ROI-ranked migration order reduces cumulative cross-VM traffic exposure during the migration window by 27% relative to alphabetical ordering, a deterministic proxy for arbitrary dependency-blind ordering. Collection overhead is mixed: in a controlled A/B test at near-saturation load on a host with two virtual CPUs (vCPUs), throughput fell by only 4.4%, but median (p50) latency rose by 383% and 99th-percentile (p99) latency by 1,050%. We therefore recommend running captures off-peak or on dedicated sampling nodes rather than under production saturation. All results are from a single 20-service testbed that we authored; we make no claim about behavior on production dependency graphs.
