design rpc frameworks

Transport-layer protocol development is hindered by environmental heterogeneity and the absence of high-level, target-agnostic programming abstractions, impeding automated analysis, formal verification, and programmable transport research. To address this, we propose TINF—the first event-driven, state-aware, high-level programming abstraction specifically designed for transport protocols—achieving full decoupling between protocol logic and execution targets. TINF employs a C-like restricted syntax to express protocol behavior and introduces a target-agnostic instruction set supporting core operations including data reassembly, packet generation, scheduling, and timer management. It features dual backends: DPDK and Linux XDP. Experimental evaluation demonstrates that TINF significantly improves development efficiency, ensures cross-platform semantic consistency, and establishes a unified foundation for formal modeling and automated verification of transport protocols.

Modern microservices increasingly depend on high-performance remote procedure calls (RPCs) to coordinate fine-grained, distributed computation. As network bandwidths continue to scale, the CPU overhead associated with RPC processing, particularly serialization, deserialization, and protocol handling, has become a critical bottleneck. This challenge is exacerbated by fast user-space networking stacks such as DPDK, which expose RPC processing as the dominant performance limiter. While prior work has explored software optimizations and FPGA-based offload engines, these approaches remain physically distant from the CPU's memory hierarchy, incurring unnecessary data movement and cache pollution. We present Arcalis, a near-cache RPC accelerator that positions a lightweight hardware engine adjacent to the last-level cache (LLC). Arcalis offloads RPC processing to dedicated microengines on receive and transmit paths that operate with cache-line latency while preserving programmability. By decoupling RPC processing logic, enabling microservice-specific execution, and positioning itself near the LLC to immediately consume data injected by network cards, Arcalis achieves 1.79-4.16$\times$ end-to-end speedup compared to the CPU baseline, while significantly reducing microarchitectural overhead by up to 88%, and achieves up to a 1.62$\times$ higher throughput than prior solutions. These results highlight the potential of near-cache RPC acceleration as a practical solution for high-performance microservice deployment.

This study addresses the lack of standardized practices in Model Context Protocol (MCP) regarding configuration, communication, and human oversight, noting that prior research has predominantly focused on server-side implementations while neglecting application-level usage. To bridge this gap, we introduce MCPAppTax, the first taxonomy for MCP applications, and conduct a large-scale empirical analysis of 1,723 MCP applications on GitHub, leveraging large language model–assisted annotation and static code analysis. Our findings reveal both convergent practices—such as 85.2% adopting file-based configuration and 81.1% using official SDKs—and divergent ones, notably the absence of standardized naming conventions for configuration parameters. Furthermore, while human supervision mechanisms are prevalent (90.8% log interactions and 77.2% offer start/stop controls), only 37.2% implement blocking-style human approval, highlighting significant variability in safety-critical oversight.

This work proposes a semantic-aware, intent-based wireless resource orchestration approach tailored for Open RAN architecture and evaluates its performance under realistic observability constraints. To this end, the authors develop a scalable ns-3-based simulation framework integrating a RAN Intelligent Controller (RIC) with distributed applications (dApps), enabling intent-driven orchestration across multiple time scales, validated through a wireless resource management use case. A novel Intent Satisfaction Score (ISS) metric is introduced, combining distortion and perception-oriented measures to achieve, for the first time in Open RAN simulations, semantic-aware closed-loop control. Experimental results demonstrate that the proposed method significantly reduces radio resource consumption and computational overhead while effectively improving intent satisfaction, at the cost of only moderate degradation in packet delivery ratio and throughput.

This work proposes a fully decentralized web-based multiparty video conferencing system that eliminates the need for centralized media servers, thereby addressing the high costs and architectural complexity inherent in traditional approaches. By leveraging only lightweight notification services—such as email or push notifications—for signaling, the system establishes peer-to-peer (P2P) connections directly between clients to transmit audio, video, screen sharing, and text messages. Built upon WebRTC data channels and media streams, and implemented via browser extensions or Progressive Web Apps (PWAs), the solution significantly reduces deployment and operational overhead. This approach offers an efficient and practical decentralized conferencing alternative, particularly well-suited for resource-constrained environments.

This study addresses the lack of systematic understanding regarding the implementation and maintenance of the Model Context Protocol (MCP) in real-world open-source projects. To bridge this gap, we introduce a transparent, reproducible multi-stage validation pipeline that integrates GitHub REST/GraphQL APIs with custom Python scripts to systematically annotate structural evidence, classify repository roles, and filter out non-functional examples from 3,238 candidate repositories. This process yields a high-quality dataset of 2,297 verified MCP projects, achieving a validation precision of 83% at 95% confidence. Our analysis reveals Python and TypeScript as the dominant implementation languages and identifies hybrid architecture as the most prevalent design pattern, thereby establishing the first large-scale empirical benchmark for MCP ecosystem research.

This work addresses the debugging gap between high-level functional reactive programming (FRP) abstractions and their low-level C/C++ implementations in Emfrp, where debugging traditionally requires inspecting generated code. To bridge this gap, the authors propose a multi-modal debugging framework that precisely maps Emfrp language constructs to their corresponding locations in compiled code using source-code mapping techniques. The framework integrates multi-level debugging interfaces with embedded systems, enabling coordinated debugging across the FRP abstraction layer and platform-specific I/O layers. It maintains compatibility with conventional debuggers such as GDB and supports cross-language debugging. Experimental evaluation on the ESP32 microcontroller demonstrates that the approach significantly improves debugging efficiency for FRP-based embedded applications.