A global, auto-scaling, preemptive scheduler using work-balancing.

What? Another executor?

smolscale is a work-balancing executor based on [async-task], designed to be a drop-in replacement to smol and async-global-executor. It is designed based on the thesis that work-stealing, the usual approach in async executors like async-executor and tokio, is not the right algorithm for scheduling huge amounts of tiny, interdependent work units, which are what message-passing futures end up being. Instead, smolscale uses work-balancing, an approach also found in Erlang, where a global "balancer" thread periodically balances work between workers, but workers do not attempt to steal tasks from each other. This avoids the extremely frequent stealing attempts that work-stealing schedulers generate when applied to async tasks.

smolscale's approach especially excels in two circumstances:

• When the CPU cores are not fully loaded: Traditional work stealing optimizes for the case where most workers have work to do, which is only the case in fully-loaded scenarios. When workers often wake up and go back to sleep, however, a lot of CPU time is wasted stealing work. smolscale will instead drastically reduce CPU usage in these circumstances --- a async-executor app that takes 80% of CPU time may now take only 20%. Although this does not improve fully-loaded throughput, it significantly reduces power consumption and does increase throughput in circumstances where multiple thread pools compete for CPU time.
• When a lot of message-passing is happening: Message-passing workloads often involve tasks quickly waking up and going back to sleep. In a work-stealing scheduler, this again floods the scheduler with stealing requests. smolscale can significantly improve throughput, especially compared to executors like async-executor that do not special-case message passing.

Furthermore, smolscale has a preemptive thread pool that ensures that tasks cannot block other tasks no matter what. This means that you can do things like run expensive computations or even do blocking I/O within a task without worrying about causing deadlocks. Even with "traditional" tasks that do not block, this approach can reduce worst-case latency. Preemption is heavily inspired by Stjepan Glavina's previous work on async-std.