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At least 19 recordsLinked to original sources

arXiv2026

Arcalís: Accelerating Remote Procedure Calls Using a Líghtweight Near-Cache Solution

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 hardware accelerators have explored NIC-attached and FPGA-based offload, these approaches remain farther from the cache hierarchy, so the frequent data accesses during RPC processing each pay an extra interconnect traversal cost that inflates RPC time. Therefore, RPC handling should occur as close as possible to the cache; however, a near-cache solution must be small, hence practical and deployable. Our key insight to enable such a solution is taking advantage of a reconfigurable accelerator that can be configured specifically for the services currently running on the CPUs. We present Arcal\'ıs, a near-cache RPC accelerator that positions a lightweight hardware engine adjacent to the last-level cache (LLC). Arcal\'ıs offloads RPC processing to dedicated microengines that operate with cache-line latency while preserving programmability. By decoupling RPC processing logic, enabling microservice-specific execution, and positioning itself near the LLC, Arcal\'ıs achieves a 1.72-4.91$\times$ end-to-end speedup compared to the CPU baseline, significantly reduces microarchitectural overhead by up to 88\%, and achieves up to a 1.67$\times$ higher throughput than prior solutions. These results highlight the potential of near-cache RPC acceleration as a practical solution for high-performance microservice deployment.

cs.AR↗

arXiv2026

AEGIS: Risk-Budgeted Online Scheduling for Resilient Continuous Edge Inference

Continuous edge inference requires sustained wireless and computing support across successive service instances. Under recurring channel degradation, transient edge overload, and multi-user contention, isolated deadline misses may accumulate into persistent service degradation. Existing schedulers mainly optimize instantaneous latency or per-timeslot utility and provide limited control over such cross-time effects. To address this issue, we propose AEGIS (Adaptive Exposure-Governed Inference Scheduling), a risk-budgeted online framework for service-level operational resilience. AEGIS regulates predicted deadline-risk exposure through dynamically replenished per-user risk budgets and establishes an explicit finite-horizon bound on cumulative admitted-risk exposure. One-step state estimation supports anticipatory delay and risk assessment, while the centralized bandwidth-computing allocation is transformed into an exact-potential formulation and solved through asynchronous coordinate updates. Simulation results under varying system scales and stress conditions show that adaptive replenishment improves service continuity and recovery relative to fixed replenishment, while risk budgeting reduces predicted-risk exposure compared with more aggressive scheduling alternatives.

cs.NI↗

arXiv2026

Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety

Large language models (LLMs) are increasingly being used in network operations (NetOps) and artificial intelligence for IT operations (AIOps) for tasks ranging from telemetry retrieval and incident diagnosis to configuration planning and bounded remediation. As these systems acquire greater access to operational tools, a central question is whether operational assurance develops commensurately with the authority granted to them. This survey examines that question through a structured, evidence-stratified review of agentic NetOps and AIOps, spanning directly evaluated LLM systems, operational mechanisms, transferred evidence, and normative guidance. We organise the field around autonomy, tool scope, evidence traces, assurance controls, evaluation, security, and governance, and introduce an operational assurance contract that links each autonomy level to permitted tools, required evidence, independent gates, execution budgets, rollout and rollback duties, and audit requirements. The synthesis indicates a capability-assurance gap: evidence is comparatively strong for read-oriented assistance and tool-grounded diagnosis, but becomes substantially less complete as systems approach configuration change, bounded execution, and closed-loop operation. We therefore develop a workflow-level evaluation framework covering evidence quality, tool use, policy and invariant compliance, staged execution, recovery, calibration, cost, and human intervention, together with a threat model for prompt-borne attacks, compromised operational evidence, excessive agency, privilege boundaries, and auditability. The resulting view treats agentic NetOps and AIOps as constrained operational control, in which increasing autonomy requires correspondingly stronger evidence, independently enforced safeguards, and recovery mechanisms.

cs.NI↗

arXiv2026

No-Regret Mixing of LRU and LFU with Optimal Switching Cost

Caching systems often rely on simple eviction policies such as Least Recently Used (LRU) and Least Frequently Used (LFU), which perform well in complementary request regimes. Recent policies such as LeCar and Cacheus combine LRU and LFU using ideas from the experts problem in online learning. Specifically, upon a miss, they randomize between the two eviction rules using probabilities derived from scores updated by tracking the history of past evictions. While these policies exhibit strong empirical performance, it remains unclear whether they are guaranteed, on every request sequence, to perform asymptotically as well as the better of LRU and LFU, i.e., whether they achieve sublinear regret with respect to this benchmark. We first show that LeCar suffers linear regret against an oblivious adversary, even with unbounded history. We then propose H-MC, a Hedge-based mixture of virtual LRU and LFU caches that preserves Hedge's selection probabilities, and hence its regret guarantees, while minimizing the switching cost among all joint selection rules with these marginals.

cs.LG↗

arXiv2026

Enhanced Dynamic Beamwidth Selection-based THz MAC Protocol for Wireless Data Center Networks

Terahertz (THz) wireless communication offers a promising alternative to traditional wired links in data centers (DCs), enabling ultra high data rates, low latency, and greater scalability. However, THz signals suffer from high path loss, necessitating the use of directional antennas (DAs). While DAs enhance signal strength, they introduce challenges such as deafness and synchronisation, typically addressed through receiver initiated MAC protocols. Most existing THz MAC protocols use fixed beamwidths, which results in a key performance trade off: narrow beams improve gain for long range links but reduce throughput for short distances due to increased alignment overhead, while wide beams benefit short links but degrade performance over longer distances. To overcome this limitation, we propose DBS-ADAPT, a dynamic beamwidth selection based MAC protocol that adjusts the antenna beamwidth according to the distance between nodes, maximising throughput without compromising link range. We also introduce an enhanced version, EDBS-ADAPT, which further reduces beamwidth switching and control overhead while preserving throughput gains. Both protocols are evaluated using the NS-3 THz module. Simulation results show that DBS-ADAPT improves average throughput by up to 22% and reduces delay up to 10% compared to the baseline ADAPT-3 protocol. EDBS-ADAPT further cuts beamwidth switching overhead by 95%, making it more efficient for scalable and high performance wireless DC environments.

cs.NI↗

arXiv2026

An FPGA-in-the-Loop Testbed for MU-MIMO OFDM Beamforming over Ray-Traced Wireless Channels

Wireless networks face ever expanding throughput demands from heterogeneous, high-density user populations, requiring beamforming algorithms that adapt to channel conditions with low latency. Validating such algorithms requires either costly over-the-air testbeds or simulation environments that lack the timing and resource constraints of real hardware, leaving a gap between algorithm design and hardware-realizable deployment. This work presents a frame-based hardware-in-the-loop (HIL) testbed that closes that gap by coupling an FPGA-based implementation of OFDM Waveforms with MU-MIMO beamforming to NVIDIA Sionna's ray-tracing channel simulator, enabling a physical base-station architecture to transmit and receive against a Sionna-rendered digital-twin propagation environment. Unlike prior work that validates beamforming algorithms either purely in simulation or on full RF testbeds, this architecture allows beamforming logic running on actual FPGA fabric to be evaluated under realistic, controllable, and repeatable channel conditions, including UE mobility and site-specific multi-path, without requiring an anechoic chamber or live RF front end. We detail the FPGA OFDM transmit/receive pipeline, the synchronization and data interface between the FPGA and the Sionna environment, and validation of signal quality under AWGN and ray-traced channel conditions, establishing this testbed as a platform for hardware-validated beamforming research.

cs.NI↗

arXiv2026

xTRUCE: A Provably Safe Arbiter for Multi-xApp Conflict Mitigation in Agentic O-RAN

The open radio access network (O-RAN) is evolving toward agentic operation, where large language model (LLM)-driven xApps/rApps generate control proposals under operator intents. However, such proposals may be conflicting, infeasible, or hallucinated, and no existing system jointly provides proposal-independent safety, priority-aware reconciliation, and traceable feedback. To this end, we propose a provably safe arbiter, namely xTRUCE, in the near-real-time (Near-RT) RAN intelligent controller for mitigating multi-xApp conflicts in gNB control. We first develop a structured xApp proposal interface and a three-layer constraint hierarchy that places physical limits and operator-defined rules above relaxable performance targets, alongside a dual-timescale control action space. A two-stage arbitration mechanism then minimizes target shortfalls in the operator-priority order to finalize safe E2 actions within the Near-RT latency budget, while returning conflict certificates to xApps and the operator for renegotiation. Finally, we implement xTRUCE in a multi-cell O-RAN use case, and evaluate its multi-process prototype through simulations with live API-backed LLM xApps and over-the-air experiments on OpenAirInterface/FlexRIC-based O-RAN stacks. Results show that xTRUCE ensures gNB control safety with $100\%$ protected services despite severe proposal hallucinations, achieves priority-consistent performance satisfaction under overload, efficiently guides LLM intent renegotiation via certificates, and keeps a delay-safe E2 control loop.

cs.NI↗

arXiv2026

MILAAP: Mobile Link Allocation via Attention-based Prediction

Channel hopping (CS) communication systems must adapt to interference changes in the wireless network and to node mobility for maintaining throughput efficiency. Optimal scheduling requires up-to-date network state information (i.e., of channel occupancy) to select non-overlapping channels for links in interference regions. However, state sharing among nodes introduces significant communication overhead, especially as network size or node mobility scale, thereby decreasing throughput efficiency of already capacity-limited networks. In this paper, we eschew state sharing while adapting the CS schedule based on a learning-based channel occupancy prediction. We propose the MiLAAP attention-based prediction framework for machine learning models of spectral, spatial, and temporal dependencies among network nodes. MiLAAP uses a self-attention mechanism that lets each node capture the temporospectral CS pattern in its interference region and accordingly predict the channel occupancy state within that region. Notably, the prediction relies only on locally and passively observed channel activities, and thus introduces no communication overhead. To deal with node mobility, MiLAAP also uses a multi-head self-attention mechanism that lets each node locally capture the spatiotemporal dependencies on other network nodes that can interfere with it and accordingly predict the motion trajectory of those nodes. Detecting nodes that enter or move outside the interference region is used to further improve the prediction accuracy of channel occupancy. We show that for dynamic networks that use local CS sequences to support relatively long-lived flow traffics, the channel state prediction accuracy of MiLAAP is remarkably ~100% across different node mobility patterns and it achieves zero-shot generalizability across different periods of CS sequences.

cs.NI↗

arXiv2026

Continuum: Efficient and Robust Multi-Turn LLM Agent Scheduling with KV Cache Time-to-Live

KV cache management is essential for efficient LLM inference. To maximize utilization, existing inference engines evict finished requests' KV cache if new requests are waiting. This policy breaks for agentic workloads, which interleave LLM calls with tools, introducing pauses that prevent effective KV reuse across turns. Since many tool calls have much shorter durations than human response multi-turn chatbot, it would be promising to retain the KV cache in during these tools. However, many challenges remain. First, we need to consider both the potential cost of recomputation or reloading (if offloading enabled) as well as the increasing queueing delays after eviction from GPU. Second, due to the internal variance of tool call durations, the method needs to remain robust under limited predictability of tool call durations. We present Continnum, a serving system to optimize job completion time for multi-turn agent workloads by introducing time-to-live mechanism for KV cache retention. For requests that generate tool calls, Continnum selectively pins the KV cache in GPU memory with a time-to-live value determined by the reload cost and potential queueing delay induced by eviction. When the TTL expires, the KV cache can be automatically evicted to free up GPU memory, providing robust performance under edge cases. When combined with program-level first-come-first-serve, Continnum preserves multi-turn continuity, and reduces delay for agentic workflows. Evaluations on real-world agents (SWE-Bench, BFCL, OpenHand) with Llama-3.1 8B/70B, Gemma-3 12B, and GLM-4.5 355B shows that Continnum improves the average job completion times by over 8x while improving throughput.

cs.OS↗

arXiv2026

MEO: Mining Reliable Expert Signals for Offline Reinforcement Learning in Wireless Networks

The operation of next-generation wireless networks increasingly hinges on Deep Reinforcement Learning (DRL) to optimize critical network decisions. Offline RL trains agents using accessible datasets and thus eliminates costly online interactions, rendering it particularly advantageous in dynamic wireless environments that demand low latency and high safety. However, its capability remains underexplored in wireless settings, where offline data are often limited and subject to partial observability. As a result, the learned policy fails to fully capture the informative decision patterns hidden in the dataset. To bridge this gap, we propose MEO, a framework that mines reliable expert signals from wireless datasets for offline RL deployment. MEO first identifies reliable expert samples from imperfect offline data, and then learns a structured latent representation to better distinguish expert and non-expert behaviors. Based on the representation, MEO further derives a compensable reward signal that helps offline RL algorithms more effectively exploit valuable expert knowledge. We evaluate MEO in three practical wireless network scenarios using datasets collected from real-world deployments. The results show that MEO consistently outperforms existing methods across all three scenarios. Notably, MEO can be seamlessly integrated with any offline RL algorithms, highlighting its generalizability and potential for wide adoption in wireless networks. Code is available at https://github.com/lpzu/MEO.

cs.NI↗

arXiv2026

Traffic Chunk Sizing vs. Optical Switching Speed in Future All-Optical Satellite Networks

To enable efficient resource utilization under stringent Size, Weight, and Power (SWaP) constraints through transparent and all-optical switched satellites transmission, various switching paradigms can be considered, including packet, burst, or circuit. To this end, the traffic assembly and algorithmic design for path computations at the ground stations play a key role in determining the switching fabric design. Generally, traffic can be buffered and assembled in chunks at the ground stations and forwarded over the pre-computed optical path in space, similar to terrestrial optical burst switching or fast circuit switching. Regardless of the chosen paradigm, the switching fabric must satisfy specific latency performance requirements. This paper studies the performance of all-optical satellite networks based on the maximum traffic chunk sizes that can be scheduled and the performance of optical switching fabrics in the future over all-optical constellations. We consider various optical switching technologies, including MEMS- and integrated photonic-based solutions, in the context of switching speed, power consumption, and insertion loss. Simulation results indicate that traffic chunk size critically impacts the performance required by optical switching fabrics onboard a satellite.

cs.NI↗

arXiv2026

Semantics-Aware Communication:A Differentiated Allocation Perspective

We study the joint optimization of timeliness and reliability in semantics-aware Wireless Networked Control Systems (WNCS) under computation resource constraints. The sampled data are categorized into regular and critical tasks based on the semantic states, facilitating differentiated resource allocation. Task-aware Age of Actuation (AoA) and Cost of Missing Actuation (CoMA), are used to characterize the task-level freshness and the reliability penalty of missed actuations, respectively. By modeling the controller as a discrete-time multi-rate Geo/D/C/C queue, we evaluate the performance of regular and critical tasks, the latter imposing higher computational demands. Results confirm that differentiated resource allocation across heterogeneous tasks effectively guarantees the actuation reliability of critical tasks in severely constrained environments.

cs.NI↗

arXiv2026

Efficient and Secure Discovery for AI Agents: The Case for DNS

As Artificial Intelligence (AI) agents enter their next stage of ubiquitous deployment throughout the Internet, their discoverability will become a central challenge. The information AI agents need to discover one another, how they will locate it, how to facilitate authentication, integrity, and authorization, how to connect across different platforms, and how to scale across organizational boundaries form a set of unanswered challenges that will be central to deployment success. These are challenges for which one of the Internet's most venerable, solid, and ubiquitous infrastructures is ideally suited: The Domain Name System (DNS). Such a rich, already ubiquitous, and programmatically flexible foundation is an ideal option for discovery of AI agents. In this work, we propose an illustration and rationale for the basic semantics that discovery for AI agents will require. We argue that three key evaluation criteria will become paramount: navigational completeness (the extent to which the necessary metadata, with elements like trust, is included in a discovery solution), lookup complexity, and transaction performance (e.g., latency, speed, or recency). Using 119,757 real-world service endpoints, 62,739 indexed MCP server entries, and multiple agent tooling ecosystems, we empirically evaluate the first of these considerations to illustrate the appropriateness of using DNS for AI agent discovery. We find that the necessary and sufficient discovery metadata can be encoded into a single DNS resource record that fits within one DNS UDP response, enabling single-RTT discovery. Our evaluations illustrate a promising path toward enabling AI agent discoverability at the Internet's scale, and thereby accelerating secure, stable, and resilient deployments.

cs.NI↗

arXiv2026

Information-Entropy-Driven Fault Propagation Modeling for Probabilistic Network Performance Prediction

Network faults can trigger cascading effects that cause abrupt and nonstationary performance degradation. Existing learning-based performance predictors mainly focus on normal operation or treat fault-induced topology and routing changes as static inputs, and typically produce deterministic point estimates. They overlook fault-propagation dynamics and uncertainty in performance evolution. The predefined-rule and purely data-driven propagation models lack a unified representation of fault definition, propagation mechanism, and impact quantification. Additionally, generic denoisers in conditional diffusion models fail to incorporate fault propagation into uncertainty modeling. To address these limitations, we propose an information-entropy-driven fault propagation paradigm (IEFP) that characterizes fault propagation via relative entropy, mutual information and transfer entropy. We then design a fault-aware graph message-passing mechanism that propagation contexts modulate network representation learning. We further develop FEMNet, which employs this mechanism as a tailored denoiser within a conditional diffusion model to enable probabilistic network performance prediction under complex fault scenarios. Compared with the strongest baselines, IEFP improves fault-prediction performance, while FEMNet reduces errors in both point and probabilistic KPI prediction.

cs.NI↗

arXiv2026

QoS-Aware RACH Preamble Slicing via Quota-Projected Branching Deep Reinforcement Learning

Quality-of-service (QoS)-aware random access requires adaptive allocation of a finite random access channel (RACH) preamble budget across heterogeneous traffic and access procedures. This paper proposes QP-BD3QN-RACH, a quota-projected branching deep reinforcement learning controller for mixed two-step (2RA) and four-step (4RA) contention-based random access. Four action branches correspond to the delay-sensitive and delay-tolerant 2RA/4RA preamble pools. A branching dueling Double DQN selects pool-specific multipliers, and deterministic quota projection converts them to nonnegative integer allocations that preserve the preamble budget. With five actions per branch, the controller represents 625 pre-projection branch-action tuples using 20 branch-action outputs. Evaluation covers five arrival loads, cross-method comparison under nominal seed 42, six-seed sensitivity of QP-BD3QN-RACH, and targeted ablations. Across the five-load grid, its mean direction-aligned differences relative to four comparators are positive: 5.74 to 8.21 percentage points for success/collision, 1.23 to 1.92 percentage points for fallback, 0.35 to 0.68 percentage points for blocking, and 0.128 to 0.456 decision intervals for successful-access delay. Load-wise results exhibit metric-dependent tradeoffs, particularly under intermediate and overload conditions.

cs.NI↗

arXiv2026

AI-Native Orchestration in the 6G Continuum: Evolving Operator Platforms with Agentic AI

As Sixth-Generation (6G) networks evolve towards a seamless Cloud-Edge-Internet of Things (IoT) continuum, autonomous orchestration across distributed compute and network domains becomes critical. Future 6G services will span multiple administrative and operator domains, making federation essential for ubiquitous, ultra-low-latency service continuity beyond individual footprints. This complexity demands AI-native mechanisms supporting intent-driven automation and closed-loop management. While the GSMA Operator Platform (OP) provides the architectural blueprint for multi-operator federation and network capability exposure, and the ETSI Software Development Group OpenOP (SDG OOP) offers a primary open-source reference implementation, current frameworks are limited by stateless API interactions and lack native intelligence. This paper proposes an Agentic-driven Intelligence extension for the GSMA OP architecture, using the OOP as the reference framework. We introduce an AI-native orchestration layer where autonomous agents manage persistent service contexts and enable closed-loop control via CAMARA APIs. By integrating a Declarative Monitoring and Alerting System (DeMAS) into the OOP stack and establishing a decentralised agent negotiation protocol, the proposed architecture enables real-time, intent-driven resource optimisation and autonomous cross-domain conflict resolution across federated domains. We validate our approach through a representative 6G use case involving Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile Broadband (eMBB) coexistence, demonstrating that an agentic OP framework autonomously reconciles stringent Service Level Agreements (SLAs) while enhancing infrastructure energy efficiency. Our findings establish a scalable blueprint for cross-domain Network-as-a-Service (NaaS) models that align standardised exposure with 6G autonomous requirements.

cs.NI↗

arXiv2026

CleanCity-BinSense: An IoT-Enabled Smart Waste Management System with Configurable Real-Time Fill Monitoring and Nearest-Neighbor Route Optimization

CleanCity-BinSense addresses inefficiencies in urban waste management in developing cities, where fixed-schedule collection routes lead to overflowing bins and wasted fuel. This paper presents CleanCity-BinSense, a low-cost, end-to-end IoT-enabled smart waste management system for scalable real-time waste monitoring and demand-driven collection. The system integrates a solar-powered sensor node with an ultrasonic sensor for real-time bin fill-level monitoring. A key contribution is a configurable sensing model based on two calibration parameters, FULL_DISTANCE and EMPTY_DISTANCE, enabling deployment across bins of varying sizes and geometries without firmware modification. Fill percentage is computed using a geometry-configurable linear normalization algorithm, validated through hardware experiments with a mean absolute error (MAE) of 0.38 cm, within the manufacturer-specified sensor tolerance. Sensor readings are transmitted via Wi-Fi to a centralized web platform providing role-based dashboards for administrators, operators, and drivers, along with a public real-time bin-status map. The system also incorporates a lightweight nearest-neighbor route planning algorithm using SQL Server's spatial function to generate proximity-based collection routes with low computational overhead. Experimental evaluation shows an average end-to-end system latency of 5.3 seconds, dominated by the sensing interval rather than network overhead, while route generation for typical urban collection zones completes in under 100 ms. These results demonstrate the feasibility of a low-cost, configurable, infrastructure-light smart waste management system for heterogeneous urban waste networks in resource-constrained environments such as Dhaka, Bangladesh.

cs.NI↗

arXiv2026

Hybrid Continuous DoA Estimation with Shared-Radius Co-Prime Circular Arrays

This paper proposes a shared-radius co-prime circular array for high-resolution, continuous 2D Direction-of-Arrival (DoA) estimation in 3D space, jointly estimating azimuth and elevation angles. The proposed architecture consists of two uniform circular sub-arrays with co-prime antenna counts sharing a common radius RR, a design that intrinsically suppresses mutual coupling leakage compared to dense uniform arrays. Unlike existing works that rely on complex phase-mode transformations to map circular structures to virtual linear arrays, we introduce a hybrid continuous-recovery framework operating directly in the physical spatial domain. By integrating a fast, discrete coarse-grid search with a swarm-intelligence continuous refinement stage, the proposed method completely bypasses discrete grid-mismatch limitations and computationally expensive eigenvalue decompositions. A rigorous theoretical analysis using Nivens Theorem establishes the spatial uniqueness of the true source direction, effectively resolving phase ambiguities. Simulation results demonstrate that this hybrid scheme achieves superior resolution and lower Root Mean Square Error (RMSE) at low Signal-to-Noise Ratios (SNR) compared to uniform configurations, while asymptotically converging to the theoretical Cramer-Rao Bound (CRB) at high SNRs.

cs.IT↗