Despite a decade of research, most high-performance userspace network stacks remain impractical for public cloud tenants developing their applications atop Virtual Machines (VMs). We identify two root causes: (1) reliance on specialized NIC features (e.g., flow steering, deep buffers) absent in commodity cloud vNICs, and (2) rigid execution models ill-suited to diverse application needs. We present Machnet, a highperformance and flexible userspace network stack designed for public cloud VMs. Machnet uses only a minimal set of vNIC features that any major cloud provider supports. It also relies on a microkernel architecture to enable flexible application execution. We evaluate Machnet across three major public clouds and on production-grade applications, including a key-value store, an HTTP server, and a statemachine replication system. We release Machnet at https: //github.com/microsoft/machnet.

State Machine Replication (SMR) protocols form the backbone of many distributed systems. Enterprises and startups increasingly build their distributed systems on the cloud due to its many advantages, such as scalability and cost-effectiveness. Due to their prevalence in systems, the practical aspects of SMR algorithms, such as efficiency and performance, become hugely important. These practical considerations can impact capacity planning, deployment strategies, and, ultimately, the cost of running stateful systems in the cloud. In this paper, we consider various practical choices that impact the performance and efficiency of state machine replication in the cloud. To that order, we design a language-agnostic Multi-Paxos-based state machine architecture and implement it in several languages popular for cloud deployment. In the process, we investigate the impact of threading, communication, and memory management models on replicated state machines’ performance and resource efficiency. We also examine the high-level implications of virtualization on the performance of SMR implementations in various languages. We present our findings as a collection of practical lessons backed by our experimental data and analysis.

State machine replication (SMR) algorithms ensure redundancy in critical systems and, as a result, underpin fault-tolerant distributed databases. Good SMR protocol performance is essential for capacity planning and meeting desired performance objectives. However, many implementations of popular SMR algorithms, such as MultiPaxos and Raft, have issues that make their performance unpredictable. This unpredictability often arises from certain "bolt-on" additions to core protocols, such as external failure detectors and replication log compaction. In this paper, we argue that tighter integration of such traditionally ad-hoc mechanisms with the core replication protocols can stabilize performance, making the solutions more reliable and more accessible to accurate capacity planning. Moreover, we show that these integrations can be non-disruptive for the underlying consensus algorithm, resulting in systems that preserve the simplicity and safety of traditional single-leader consensus-based SMR. To that order, we integrate the failure and slowdown detectors inside the SMR and achieve better performance and faster fail-over under various network partitions and node slowdown events. We also illustrate that tight integration of replication log management, pruning, and snapshotting can reduce memory and CPU usage while avoiding performance fluctuations associated with traditional log compaction and cleanup approaches.

Serverless platforms rely on legacy networking stacks for communication and data movement. We quantitatively analyze the performance of these stacks and show their mismatch with highly consolidated, virtualized modern serverless environments, focusing on Firecracker, the most common serverless virtualization framework. As serverless applications grow in complexity and interaction, the resulting network bottleneck is a prime source of user-perceived, end-to-end latency. In this paper, we present a detailed vision of a new, sidecar-based networking stack for serverless environments. Our primary design goal is to provide low-overhead networking while maintaining existing security guarantees. We outline the research challenges in both the control and the data plane that the community needs to tackle before such a sidecar architecture can be used in practice.