Extended user interrupts (xUI) is a set of processor extensions that builds on Intel's UIPI model of user interrupts, for enhanced performance and flexibility. This paper deconstructs Intel's current UIPI design through analysis and measurement, and uses this to develop an accurate model of its timing. It then introduces four novel enhancements to user interrupts: tracked interrupts, hardware safepoints, a kernel bypass timer, and interrupt forwarding. xUI is modeled in gem5 simulation and evaluated on three use cases -- preemption in a high-performance user-level runtime, IO notification in a layer3 router using DPDK, and IO notification in a synthetic workload with a streaming accelerator modeled after Intel's Data Streaming Accelerator. This work shows that xUI offers the performance of shared memory polling with the efficiency of asynchronous notification.

Serverless computing is an emerging cloud computing paradigm where users offload functions to serverless platforms that manage their own execution environments. Despite recent advancements on efficient serverless management, we find that cloud providers' solutions lead to unnecessary memory overheads, and significant memory cost by assuming a single tier of memory (DRAM). In this paper, we evaluate previous serverless works and offer insights about enabling memory tiering for serverless. Based on our insights, we introduce Tiering of Serverless Snapshots (TOSS), a heterogeneous memory mechanism that aims to reduce the total memory cost on serverless platforms, while achieving comparable performance to single-tier solutions. We show that TOSS achieves near optimal memory cost for most functions, while offloading on average 92% of the memory to the slow tier. In addition, TOSS achieves$52 \times$lower setup time and up to$4.2 \times$lower invocation time than the state-of-the-art DRAM-only mechanism.

Serverless computing (Function-as-a-Service) provides fine-grain resource sharing by running functions (or Lambdas) in containers. Data-dependent functions are required to be invoked following a pre-defined logic, which is known as serverless workflows. However, our investigation shows that the traditional master-worker based workflow execution architecture performs poorly in serverless context. One significant overhead results from the master-side workflow schedule pattern, with which the functions are triggered in the master node and assigned to worker nodes for execution. Besides, the data movement between workers also reduces the throughput.