Serverless computing is an excellent fit for big data processing because it
can scale quickly and cheaply to thousands of parallel functions. Existing
serverless platforms isolate functions in ephemeral, stateless containers,
preventing them from directly sharing memory. This forces users to duplicate
and serialise data repeatedly, adding unnecessary performance and resource
costs. We believe that a new lightweight isolation approach is needed, which
supports sharing memory directly between functions and reduces resource
overheads.
We introduce Faaslets, a new isolation abstraction for high-performance
serverless computing. Faaslets isolate the memory of executed functions using
software-fault isolation (SFI), as provided by WebAssembly, while allowing
memory regions to be shared between functions in the same address space.
Faaslets can thus avoid expensive data movement when functions are co-located
on the same machine. Our runtime for Faaslets, Faasm, isolates other resources,
e.g. CPU and network, using standard Linux cgroups, and provides a low-level
POSIX host interface for networking, file system access and dynamic loading. To
reduce initialisation times, Faasm restores Faaslets from already-initialised
snapshots. We compare Faasm to a standard container-based platform and show
that, when training a machine learning model, it achieves a 2x speed-up with
10x less memory; for serving machine learning inference, Faasm doubles the
throughput and reduces tail latency by 90%.

Faaslets 是一种高性能服务器计算的隔离抽象，它使用 WebAssembly 提供的软件故障隔离来隔离已执行函数的内存，同时允许在相同的地址空间中共享内存区域，从而避免产生昂贵的数据移动。与标准容器平台的比较表明，Faasm 能够在训练机器学习模型时实现 2 倍的加速和 10 倍的内存减少，在提供机器学习推理方面，Faasm 将吞吐量提高了一倍，并将尾延迟降低了 90%。