Search arXivSearch

arXiv · 2501.06716

Symbol Resolution MatRs: Make it Fast and Observable with Stable Linking

Abstract

Dynamic linking is the standard mechanism for using external dependencies since it enables code reuse, streamlines software updates, and reduces disk/network use. Dynamic linking waits until runtime to calculate an application's relocation mapping, i.e., the mapping between each externally referenced symbol in the application to the dependency that provides the symbol. Unfortunately, it comes with two downsides. First, dynamic linking limits the performance of current systems since it can take seconds to calculate a relocation mapping for a large program. Second, dynamic linking limits the dependency management of applications since it prevents a developer from accurately observing a relocation mapping except at runtime. This paper makes the key insight that the benefits conventionally attributed to dynamic linking: code reuse, streamlined software updates, and reduced disk/network use are actually benefits of shared libraries. Thus, we present stable linking, a new mechanism for using dependencies that uses shared libraries to retain their benefits but eliminates the downsides of dynamic linking. Stable linking separates a system's state into management times; when the system can be modified, and epochs when it cannot. Stable linking calculates each application's relocation mapping at the beginning of each epoch, allows developers to inspect the relocation mapping during the epoch, and reuses the mapping for subsequent executions in the epoch. We design and build MatR, the first stable linker. We use MatR in three workloads and show that it improves upon dynamic linking performance by a factor of 2.19 on average. Additionally, we use the system in three vignettes, or case-studies, that illustrate the system's improvements to dependency management.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Farid Zakaria, Andrew Quinn, Thomas R. W. Scogland. 2025-01-12. Symbol Resolution MatRs: Make it Fast and Observable with Stable Linking. https://arxiv.org/abs/2501.06716

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Don't Let AI Agents YOLO Your Files: Information and Control in Agent-Native Filesystems

AI coding agents regularly misuse their filesystem access, causing data corruption, loss, and leakage. We conduct the first systematic study of this problem through an analysis of 290 public reports. Our study reveals two fundamental gaps: users and agents have limited information about filesystem effects and insufficient control over them. To close these gaps, we propose to shift information and control from agents to filesystems. We introduce agent-native filesystems and identify three primitives they should provide: introspect effects, undo mutations, and gate accesses. These primitives let agents operate autonomously while reserving user interaction for sensitive accesses and final review. We build YoloFS, an agent-native filesystem. YoloFS stages mutations until the user commits them, snapshots intermediate states for agent self-correction, and uses progressive permission to let users adapt access rules during execution. We evaluate YoloFS with a new methodology that captures interactions among the user, agent, and filesystem. On 11 tasks with hidden side effects, YoloFS enables agents to self-correct in 8 and stages all mutations for user review. On 112 routine tasks, YoloFS reduces user interaction while matching the baseline success rate. YoloFS is open-sourced at https://github.com/YoloFS/YoloFS.

cs.OS

Netkit: Specializing Linux Packet Delivery for Container Networks

Cloud-native microservices architectures rely on network namespaces for isolation, with the overhead of container communications remaining a critical performance bottleneck. While colocating containers on the same host mitigates some of this overhead, it cannot match the performance of communication within a single network namespace. Existing solutions either require application rewrites or fail to support the full Linux network stack expected by containerized applications. In this paper, we present netkit, an eBPF-based datapath that specializes the Linux networking stack to eliminate redundant backlog queue traversals during network namespace transitions. netkit leverages eBPF to transparently redirect packets between namespaces, bypassing unnecessary buffering while preserving compatibility with existing container applications. Our implementation in the Linux kernel, integrated with minimal changes to the Cilium network plugin for Kubernetes, improves throughput by up to 37\% and achieves parity between container-to-container and process-to-process communications, effectively closing the performance gap introduced by namespace isolation.

cs.OS

Grouper: Scheduling Groups for Multi-Tenant Microsecond-Scale Microservices

Microsecond-scale core allocation makes colocating latency-critical services with batch work worthwhile. A thread that finds no work parks within microseconds and its core goes to a batch task. Putting one back costs $\sim$18 $μ$s, as the allocator must discover that a core is wanted and then take it from the batch task holding it. A monolith pays that tax once per request, a microservice chain pays it at every hop in both directions, and a multi-tenant host multiplies it again, because every tenant's hops queue at the same allocator. On our port of DeathStarBench's hotelReservation, going from two tenants to ten takes a hop from 39 to 222 $μ$s and a 10-RPC path's median from 456 to 2,445 $μ$s, a fivefold degradation even though no tenant's own load changed. We introduce Grouper and the scheduling group, a set of isolated runtimes that the allocator treats as one allocation and accounting unit, whose members may hand cores directly to one another. A service sending an RPC donates its core to the peer through an unprivileged kernel fast path, so the core follows the request through the call graph. The allocator retains control through reconciliation, core-addressed revocation and a pooled budget but leaves the critical path; its load falls from $Θ(R \cdot H)$ to $Θ(R)$ in request rate $R$ and hop count $H$. Over a grid of two to ten tenants at 1,000-30,000 requests per second each, Grouper outperforms Caladan (the allocator Junction also builds on) and Linux by up to 7.9$\times$ and 3.4$\times$ at the median and 4.1$\times$ and 14.2$\times$ at the tail, and leaves batch work more throughput than Caladan at over 70% of load points.

cs.OS