Q-GARS: Quantum-inspired Robust Microservice Chaining Scheduling

2026-03-24

AI Summary

This work addresses the stochastic latency challenges in microservice chain scheduling arising from tail latency and heterogeneous resource constraints by introducing quantum optimization to this domain for the first time. The authors propose a hybrid scheduling framework based on Quadratic Unconstrained Binary Optimization (QUBO) modeling and Simulated Quantum Annealing (SQA). By integrating global ordering, online rescheduling, and a closed-loop trust-weight mechanism, the framework adaptively combines quantum priors with proportional fairness allocation to enhance system robustness. Experimental results demonstrate that, compared to the SRPT baseline, the proposed approach reduces average weighted completion time by 2.1%, achieving up to a 16.8% improvement under heavy-tailed latency conditions. Furthermore, it attains an average node resource utilization of 0.817, outperforming the baseline by 1.1 percentage points.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Machine Learning: Quantum Machine Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Microservice-based applications are characterized by stochastic latencies arising from long-tail execution patterns and heterogeneous resource constraints across computational nodes. To address this challenge, we first formulate the problem using Quadratic Unconstrained Binary Optimization (QUBO), which aligns the problem with emerging quantum-optimization paradigms. Building upon this, we propose Q-GARS (Quantum-Guided Adaptive Robust Scheduling), a hybrid framework that integrates the QUBO model with Simulated Quantum Annealing (SQA) based combinatorial search and online rescheduling mechanisms, enabling global microservice rank generation and real-time robust adjustment. We treat the SQA-produced rank as a soft prior, and update a closed-loop trust weight to adaptively switch and mix between this prior and a robust proportional-fairness allocator, maintaining robustness under prediction failures and runtime disturbances. Simulation results demonstrate that Q-GARS achieves an average weighted completion time improvement of 2.1\% relative to a greedy baseline of the remaining shortest processing-time (SRPT), with performance gains reaching up to 16.8\% in heavy-tailed latency. The adaptive mechanism reduces tail latency under high-variance conditions. In addition, Q-GARS achieves a mean node resource utilization rate of 0.817, which is 1.1 percentage points above the robust baseline (0.806).

Problem

• microservice chaining
• stochastic latency
• long-tail execution
• heterogeneous resources
• robust scheduling

Innovation

• QUBO
• Simulated Quantum Annealing
• Microservice Scheduling
• Robust Optimization
• Adaptive Rescheduling