AsyncFlow is designed as a scenario-driven simulator for capacity planning. Its purpose is not to “predict the Internet,” but to give engineers and researchers a way to test how backend systems behave under controlled, reproducible what-if conditions. The roadmap reflects a balance between realism, clarity, and usability: each step extends the tool while keeping its scope transparent and focused.
The first milestone is to move beyond a purely abstract latency model and introduce a more realistic network layer. Instead of only attaching a fixed RTT, AsyncFlow will account for socket capacity and per-connection memory usage at each node (servers and load balancers). This brings the simulator closer to operational limits, where resource saturation, rather than bandwidth, becomes the bottleneck.
Impact: users will see how socket pressure and memory constraints affect latency, throughput, and error rates under different scenarios.
Next, the focus shifts to observability. The simulator will expose finer metrics such as RAM queue lengths, CPU waiting times, and service durations. Visualizations will be improved with richer charts, event markers, and streamlined dashboards.
Impact: enables clearer attribution of slowdowns whether they stem from CPU contention, memory limits, or network pressure and makes results easier to communicate.
Simulations are inherently variable. This milestone adds multi-run Monte Carlo support, allowing users to quantify uncertainty in latency, throughput, and utilization metrics. Results will be presented with confidence intervals and bands over time series, turning AsyncFlow into a decision-making tool rather than a single-run experiment.
Impact: supports risk-aware capacity planning by highlighting ranges and probabilities, not just averages.
Once the core network and metric layers are mature, AsyncFlow will expand into modeling stateful backends. Simple but powerful abstractions for databases and caches will be introduced: connection pools, cache hit/miss dynamics, and latency distributions.
Impact: this step unlocks realistic end-to-end scenarios, where system behavior is dominated not just by servers and edges, but by datastore capacity and caching efficiency.
With the main components in place, the simulator will introduce control policies: queue caps, deadlines, circuit breakers, rate limiting, and similar mechanisms. These features make it possible to test how systems protect themselves under overload, and to compare resilience strategies side by side.
Impact: users will gain insight into not just when a system fails, but how gracefully it degrades.
The final planned milestone is a research-oriented playground where AsyncFlow serves as a training and evaluation environment for intelligent load-balancing and autoscaling strategies. With a Gym-like interface, researchers can train RL agents and benchmark them against established baselines in controlled, reproducible conditions.
Impact: bridges capacity planning with modern adaptive control, turning AsyncFlow into both an educational tool and a research testbed.
At each step, AsyncFlow stays true to its philosophy: clarity over exhaustiveness, scenarios over prediction. The roadmap builds toward a platform that is useful across three domains:
- Education, to illustrate principles of latency, concurrency, and resilience.
- Pre-production planning, to evaluate system limits before deployment.
- Research, to test new algorithms and policies in a safe, transparent environment.