Conflict-Aware Autonomous Resource Orchestration for 5G Edge Slicing Using Multi-Agent Systems and LLMs
Authors: Nathan R. Prediger, Rodrigo Moreira, Juliano Araujo Wickboldt, Tereza C. Carvalho, Pedro Oliveira, Paulo Novais, Flávio de Oliveira Silva
Organizations: Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil · Federal University of Viçosa (UFV), Minas Gerais, Brazil · University of São Paulo (USP), São Paulo, Brazil · University of Minho (UMinho), Braga, Portugal
5G edge network slicing must reconcile two coupled requirements: understanding high-level operator intent and reallocating scarce computing and bandwidth resources under dynamic contention. Centralized optimization can face computational and responsiveness challenges under rapidly changing edge contention, and Multi-Agent System (MAS)-based auctions can support resource arbitration but still depend on operator-defined economic parameters and priorities. Embedding a Large Language Model (LLM) directly in a recurring control loop, by contrast, may introduce non-negligible inference latency and non-deterministic behavior. This paper proposes a two-timescale architecture that addresses this gap: an LLM-based Strategist translates natural-language network intents into economic policy parameters (budget, income, and bidding-aggressiveness) at the macro scale, while a deterministic, Vickrey-inspired second-price mechanism run by SPADE-based slice agents resolves recurring resource contention at the operational scale, with the resulting allocations enforced by patching Kubernetes CPU limits and Calico CNI bandwidth annotations directly on User Plane Function pods. We evaluate the architecture end-to-end on a real Kubernetes/Open5GS edge cluster across two complementary scenarios, each repeated 30 times: a Smart-City scenario, in which natural-language public-safety intents propagate into measurable changes in slice bids and bandwidth allocations within a few auction rounds, and a Hierarchical-slicing scenario, in which the mechanism provides priority-dependent resource and QoE differentiation, adapts to abrupt demand changes, and preserves configured minimum guarantees while maintaining near-complete allocation of the shared bandwidth pool.
Figures & tables
Fig. 1: Problem Positioning.
Slice
Bitrate (kbps)
Buffer (s)
Stalls
Quality switches
Mean bid
BW (Mbps)
CPU (cores)
Ambulance
5828 ± 3401
12.6
0.45
17.5
62.3
9.8
0.067
Firefighters
9748 ± 4381
14.1
0.57
13.0
59.7
15.0
0.105
Police
6024 ± 3462
12.7
0.56
19.5
62.0
9.9
0.067
TABLE I: Smart-City scenario – aggregated statistics over 30 runs (900 s each)
Fig. 2: Bid, allocated bandwidth, and bitrate per slice over the four Smart-City intents (mean of 30 runs; shaded band is ±1 std. dev.). Events: (1) fire report, t=180 s, firefighters →9 ; (2) police chase, t=420 s, police →9 ; (3) medical emergency and end of fire, t=600 s, ambulance →10 , firefighters →5 ; (4) stand-down, t=700 s, police →5 .
Slice
Bitrate (kbps)
Buffer (s)
Stalls
Quality switches
Mean bid
BW (Mbps)
CPU (cores)
Bronze
3673 ± 2885
11.6
0.65
19.7
25.6
5.3
0.030
Silver
4149 ± 2936
12.1
0.47
12.9
32.1
9.7
0.026
Gold
11347 ± 4477
16.0
0.09
10.6
52.8
19.7
0.088
TABLE II: Hierarchical slicing scenario – aggregated statistics over 30 runs (900 s each)
Fig. 3: Allocated bandwidth and bitrate per slice (mean of 30 runs; shaded band is ±1 std. dev.). Events: (1) Gold paused, t≈121 s; (2) Silver paused, t≈272 s; (3) Gold resumed, t≈422 s; (4) Bronze paused, t≈541 s; (5) Silver resumed, t≈662 s; (6) Bronze resumed, t≈782 s.
Stream-processing systems increasingly operate across heterogeneous mobile edge--cloud infrastructures, where workload volatility, resource contention, and stringent quality-of-service (QoS) requirements complicate decentralized scheduling. This paper proposes MAS-DecStream, whose main contribution is LLM-MR-CNP: an extension of the classical Contract Net Protocol with semantic CFP formulation, progressive context disclosure, multi-round proposal revision, negotiation memory, and deterministic validation. Edge-cluster agents refine natural-language offloading proposals from local observations, predicted resource states, and qualitative runtime context, while hard resource and QoS constraints remain deterministic. Experiments derived from the Alibaba ASI Trace evaluate the extension at three levels: single- versus multi-round CNP, rule-based versus LLM-assisted refinement, and fixed-model single- versus multi-round negotiation. Under the evaluated configurations, MAS-DecStream reduces latency violations to 3%, eliminates resource overcommitment, reaches a conflict-resolution rate of 0.91 with 20 agents, and improves utility by up to 22% over the multi-round rule-based baseline. A separate 25-case evaluation shows model- and prompt-dependent accuracy--cost trade-offs. The results provide initial evidence that multi-round CNP refinement is the principal protocol-level gain, with LLM assistance adding value for qualitative and uncertain runtime context.
This paper presents an autonomous agentic resource negotiation framework designed to enable zero-touch network slicing in 6G architectures using Large Language Model (LLM) agents. While LLMs offer powerful reasoning capabilities, we demonstrate that such agents inherently suffer from anchoring bias, rigidly adhering to initial heuristic proposals and causing severe network over-provisioning. To systematically mitigate this cognitive bias, we propose a novel randomized anchoring strategy modeled via a Truncated 3-Parameter Weibull distribution. This mathematically bounded approach seamlessly integrates with burst-aware Digital Twins (DTs) employing Conditional Value at Risk (CVaR) to rigorously guarantee strict Service Level Agreement (SLA) tail-latencies. To validate our methodology, we introduce and prove the Bimodal Constraint-Avoidance Utility Theorem, demonstrating that while feasible negotiations follow classical convex bounds, highly constrained scenarios undergo a phase transition governed by an inverse rational decay envelope. Empirical results generated using a locally hosted 1B-parameter model otel-llm-1b-it confirm these dual-regime bounds. Our cognitive de-biasing successfully dismantles rigid negotiation patterns, forcing agents into active exploration to safely ride SLA boundaries and boost system energy savings up to 25%. Crucially, the lightweight 1B LLM achieves sub-second inference latencies (0.95s mean), ensuring our multi-agent framework is compatible with the operational timescales of the O-RAN non-Real-Time RAN Intelligent Controller (non-RT RIC)\footnote{Our source code is available for non-commercial use at https://github.com/HatimChergui.
i2CAT Foundation, 08034 Barcelona, Spain · Universitat Polit`ecnica de Catalunya (UPC), 08034 Barcelona, Spain · Research Institute for Digital Future, Khalifa University, 127788 Abu Dhabi, UAE
Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference mainly relies on two approaches: Autoregressive decoding (AD) generates output tokens sequentially, resulting in long latency; Speculative decoding (SD) accelerates inference by using a small language model (SLM) to generate multiple draft tokens for LLM verification, but incurs extra memory costs. Due to this latency-memory tradeoff, neither approach alone can efficiently serve users with heterogeneous demands under limited edge computing resources. To address this challenge, we propose a hybrid autoregressive-speculative inference (BALANCE) framework for edge LLM inference. In BALANCE, an edge server hosts both an SLM and an LLM, assigns each user to AD or SD, and performs the two modes simultaneously. To maximize the number of served users, we formulate a task throughput maximization problem to jointly determine user scheduling and computing resource allocation between AD and SD under user latency requirements and server memory constraints. Since the problem is NP-hard, we develop a polynomial-time algorithm that transforms the original problem into two sub-problems and obtains a sub-optimal solution with a constant approximation guarantee. Experiments demonstrate that BALANCE consistently outperforms conventional AD and SD and significantly improves task throughput.
Guanqiao Qu, Shuo Chen, Qian Chen +2
Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong SAR, China · Electrical and Electronic Engineering Department, Imperial College, London SW7 2BT, U.K. · Electrical and Electronic Engineering, and Computing Departments, Imperial College, London SW7 2BT, U.K.