We propose remote direct code execution (RDX), which elevates the power of RDMA from memory access to code execution. We target runtime extension frameworks such as Wasm filters, BPF programs, and UDF functions, where RDX enables an agentless architecture that unlocks capabilities such as fast extension injection, update consistency guarantees, and minimal resource contention. We outline the roadmap for RDX around a new CodeFlow abstraction, encompassing programming remote extensions, exposing management stubs, remotely validating and JIT compiling code, seamlessly linking code to local context, managing remote extension state, and synchronizing code to targets. The case studies and initial results demonstrate the feasibility of RDX and its potential to spark the next wave of RDMA innovations.
Verifiable network telemetry is crucial for ensuring transparency and trust in network measurements. However, telemetry logs (e.g., NetFlow records) often contain sensitive data, making public verification challenging. Recent work has attempted to address this problem using Trusted Execution Environments (TEEs), such as Intel SGX, to provide confidentiality and integrity guarantees. However, TEEs are known to suffer from complex deployment requirements and limited scalability. In this paper, we introduce a software-based approach utilizing the latest advances in Zero-knowledge Proofs (ZKPs) to enable verifiable network telemetry without revealing the underlying sensitive logs or relying on special-purpose hardware. Our system employs a general-purpose ZKP virtual machine (RISC Zero) to generate cryptographic proofs over NetFlow data, enabling operators to securely attest to network flow metrics. Our preliminary results indicate that our ZKP-based design offers a viable path toward overcoming deployment and scalability limitations inherent in the solutions that require special-purpose hardware.
This paper systematically studies the cost impact of distributed in-memory caches on datacenter services. While memory used for these caches is often perceived to be expensive, we find that the resulting CPU savings from these in-memory caches far outweigh the cost of added memory. In fact, across a variety of both synthetic and production workloads, we find that adding distributed in-memory caches can lower total operating costs by 3 - 4x, even without considering their latency benefits. These cost savings can vary significantly across various architectures, such as storage layer caches, remote lookaside caches, and in-memory linked caches. We additionally evaluate cost for two emerging scenarios: caching rich application objects and strongly consistent cache. For the former, we find that caching application objects provides outsized benefits compared to their denormalized, key-value-style variants, up to 8x compared to reading from storage. For the latter, we observe that even a minimal version check for consistency can eliminate most of the cost benefits, calling for new designs for cost-effective consistent cache.
Emerging chiplet-based server platforms and the resulting server chiplet networking present a fundamental shift in the (intra-)host network. Unlike conventional monolithic servers, compute chiplets, I/O chiplets, off-chip memory, and peripheral devices communicate through a collection of heterogeneous interconnects and links, formalizing a new server chiplet network substrate that has not been explored before. This paper makes an initial step by characterizing two generations of AMD EPYC chiplet servers, identifying four communication idiosyncrasies, and summarizing the design implications. We outline some future directions under server chiplet networking and discuss how to build next-generation server systems and applications.
While ML can greatly aid network management, it often makes glaring mistakes that contradict common sense or domain-specific constraints, undermining its trustworthiness and hindering adoption. To address this mismatch, this paper advocates for enforcing logic during ML inference (or Just-In-Time), rather than during training or post-inference in prior work. We find that this approach offers correctness guarantees without sacrificing statistical fidelity, thereby maximizing the benefits of both ML and formal reasoning. To achieve Just-In-Time Logic Enforcement, we interleave an SMT solver into the language model's inference process, which guides generation step by step to enforce domain-specific rules. Our proof-of-concept implementation, LeJIT, turns a generic GPT-2 model at inference time into either a synthetic data generator or a telemetry imputer by applying different sets of logic rules and performs on par with task-specific SOTA systems. LeJit paves the way for a networking foundation model networking that can be repurposed through logic rules, instead of costly retraining or fine-tuning.
