Roadmap
MySolvers is a discrete-event simulator built around one insight that sets it apart: you can tune arrival rates, service rates and queue capacities while the model runs and see the effect immediately — no stop, edit, re-run cycle. The roadmap below takes that foundation and turns it into a complete decision platform.
No release dates. A roadmap without dates ages well; one with dates does not.
Undo and redo
Undo and redo across every edit.
Results export
Take a full statistical summary away as CSV or JSON.
Routing rules
Route by request type or by priority, send work to the shortest queue, and try another branch when one is full.
Retries and backoff
Model the retry storms that turn a small timeout into a cascading failure.
Request types
Give entities a class and attributes, and let service demand depend on them.
Scenarios
Define parameter sets, run them all, and compare the outcomes side by side.
Sub-models and a component library
Collapse part of a model into a reusable block, and start from ready-made components.
Event trace
Step through a run event by event to see exactly why a model behaves as it does.
Cost modelling
Attach a cost to each resource and read cost next to latency in every result.
Cloud models
Save, share and version models against your account, and build one from your existing topology.
AI parameter optimisation
Search millions of configurations for the point where performance meets minimum cost.
Batch mode
Run a model at full speed for a set length of simulated time and collect results without watching it play out.
Replications and confidence intervals
Run a model many times and read every result as a mean with a 95% confidence interval, not a single sample.
Reproducible runs
Every batch starts from a seed; the same seed gives the same results, so two configurations can be compared on equal terms.
Warm-up control
Discard the start-up transient so results describe the system in steady state, and find how much from pilot runs.
Goodness-of-fit testing
Test the cycle time distribution against an exponential or normal with Kolmogorov–Smirnov.
Live traffic
Watch traffic move along every connection as the model runs, and see where a full queue turns it away.
Delay component
Model transport time or a fixed lead time between two stages.
Model library
Open the classic queueing models, prepared and ready to run.
Router component
Split traffic between branches round-robin, at random or by weight.
Distribution library
Choose the distribution behind every rate and duration, not just the exponential default.
Shared Server component
Model a database or downstream service that serves all its callers at once and slows the more requests it holds.
Resource pools
Model a connection pool or thread pool as a Resource Pool with a capacity, and let entities hold a unit of it across several steps between an Acquire and a Release.
Traffic profiles
Drive a Generator with an arrival rate that ramps, steps, spikes or waves over the run instead of staying constant, and read arrivals and drops stage by stage.
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