Janus: OS Support for a Secure, Fast Control-Plane
The emergence of the edge cloud, empowered by advanced wireless technologies such as 5G, is aimed at delivering predictable, interactive services within low milliseconds. Even in the traditional cloud, latency-sensitive services are pervasive. To enable low-latency software, much focus has been on data-plane optimizations for fast processing of requests. Unfortunately, these efforts alone are insufficient: effective, low-latency services also require advances in the control-plane. However, control-plane operations require strong spatial and temporal isolation between tenant computations, thus can result in significant overhead. This paper introduces Janus, an OS abstraction for a flexible control-plane which provides both strong isolation and low latency through the use of pervasive kernel-bypass. Janus leverages Memory Protection Key (MPK) hardware to enable low-cost control operations for protected procedure call (PPC) and thread dispatch - two essential building blocks of spatial and temporal isolation. By transparently improving these fundamental control-plane operations, Janus enables efficient and predictable user-defined and customizable system control policies and mechanisms, while maintaining strong isolation. We evaluate Janus's ability to extensibly define new control mechanisms, increase the efficiency of an existing RTOS, and support low-latency services in a memcached server. Compared to a Linux approach, a specialized latency-sensitive control plane using Janus provides over a 6x improvement in throughput, while providing 99th percentile tail latencies almost 3x lower. Demonstrating the utility of an efficient control plane, Janus improves tail latency in a multi-tenant system by orders of magnitude.
Authors
- Ettore Stella (ORCID: https://orcid.org/0000-0003-1770-1228)
- Gabriel Parmer (ORCID: https://orcid.org/0000-0003-1343-4492)
- Phani Kishore Gadepalli
- Wenyuan Shao
- Xihao Han
- Linnea Dierksheide
Institutions
- George Washington University (US)
Publication Details
- Published
- 2025-05-06
- DOI
- https://doi.org/10.1109/rtas65571.2025.00036
- Citations
- 1
- Primary Topic
- Parallel Computing and Optimization Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.27
Funders
- National Science Foundation