Matheus Stolet

Matheus Stolet, Liam Arzola, Simon Peter et al.

Shared software datapaths underpin modern datacentre networking. They implement mechanisms such as virtual switching, network virtualisation tunneling, or reliable transport, and enforce policies, such as tenant rate limits, virtual network isolation, or congestion control. However, because multiple applications, containers, or VMs share them, often across tenants, they pose a tail latency isolation challenge. Current isolation approaches either sacrifice efficiency via coarse-grained core partitioning or provide weak tail latency isolation when sharing cores with basic rate limits. This paper presents Virtuoso, a time protection mechanism for shared software datapaths that provides strong cross-tenant tail latency isolation while preserving low overhead and microsecond-scale latency. Our key insight is that tail latency is fundamentally a time metric, so byte or packet throughput is the wrong metric for controlling interference when packet processing costs vary. Our design instead enforces isolation through per-tenant CPU-time budgets at datapath intervention points within run-to-completion loops, without relying on preemption. In a case study, we instantiate Virtuoso in the TAS TCP stack and demonstrate a 7.8X reduction in victim tail latency under adversarial interference while keeping throughput within 5% of unmodified TAS. We also observe a 3X per-core efficiency improvement compared to siloed datapaths under bursty workloads.

Matheus Stolet, Simon Peter, Antoine Kaufmann

Conventional cloud network virtualization sends packets through multiple guest and host layers, inflating CPU cost and tail latency. Shared host datapaths collapse this layering into one optimized path across tenants, but existing shared stacks are fixed-function: tenants cannot specialize their protocols. eBPF is the natural vehicle for restoring programmability to a shared datapath, but today's extensions are hook-sized, and its verifier provides safety -- not performance isolation: one tenant's per-packet work can inflate every other tenant's tail latency. Chamelio is a programmable shared network stack that lets tenants implement full protocols through a bounded eBPF fast path and a tenant slow path, while approaching the performance and preserving the strong isolation of fixed shared stacks. It combines three ideas: a shared-stack architecture for tenant-defined protocols; joint optimisation of tenant handlers with provider infrastructure and co-resident tenants in the shared fast path; and a bounded fast path contract with runtime cycle accounting that keeps tenant programmability compatible with strong performance isolation. A tenant programmable TCP on Chamelio reaches 9.2 Mreq/s, matching the hand-tuned TAS stack; joint compilation shrinks the programmability tax from 23.9% to 3.8%; and under a scaling TCP adversary that drives uninstrumented stacks to 154 microseconds, Chamelio bounds victim tail latency at 46 microseconds.

Vaastav Anand, Matheus Stolet, Thomas Davidson et al.

Performance issues in cloud systems are hard to debug. Distributed tracing is a widely adopted approach that gives engineers visibility into cloud systems. Existing trace analysis approaches focus on debugging single request correctness issues but not debugging single request performance issues. Diagnosing a performance issue in a given request requires comparing the performance of the offending request with the aggregate performance of typical requests. Effective and efficient debugging of such issues faces three challenges: (i) identifying the correct aggregate data for diagnosis; (ii) visualizing the aggregated data; and (iii) efficiently collecting, storing, and processing trace data. We present TraVista, a tool designed for debugging performance issues in a single trace that addresses these challenges. TraVista extends the popular single trace Gantt chart visualization with three types of aggregate data - metric, temporal, and structure data, to contextualize the performance of the offending trace across all traces.