capacity planning

This work addresses the challenge of resource allocation in geographically distributed and heterogeneous continuum computing infrastructures, where combinatorial explosion and limited generalization hinder effective deployment. To tackle this, the study introduces, for the first time, the pricing structures commonly found in Software-as-a-Service (SaaS) ecosystems into the resource allocation problem, formulating a unified, price-based representation of the configuration space. The authors propose PRIME, a pricing-aware analysis engine that efficiently searches for cost-optimal deployment configurations satisfying both functional and non-functional constraints. Leveraging synthetic infrastructure topologies and workload generation techniques, the project constructs a comprehensive dataset comprising 9,600 diverse scenarios, demonstrating that the proposed approach achieves both scalability and computational efficiency in complex, heterogeneous environments.

Financial institutions face capacity planning and job scheduling challenges in hybrid cloud and on-premise grid environments, where both resource requirements and execution durations exhibit dual uncertainty. Method: This paper proposes a co-optimization framework that jointly minimizes resource provisioning while maximizing service quality—specifically, on-time completion rate. Innovatively, it is the first to jointly model resource and duration uncertainty within capacity planning, employing a constraint programming framework based on paired sampling that integrates deterministic estimation with stochastic sampling for efficient approximate optimization. Contribution/Results: Experiments demonstrate that the method significantly reduces peak resource demand compared to manual scheduling, while maintaining a high on-time completion rate—validating its effectiveness in balancing these conflicting objectives under uncertainty.

Energy transitions often trigger regional resource conflicts and unintended adverse outcomes due to insufficient consideration of cross-scale trade-offs among environmental, social, and resource dimensions. To address this, we develop the first integrated spatially explicit multi-agent simulation framework coupling agent-based modeling (ABM) with life cycle assessment (LCA), embedding scenario analysis and regional ecological constraints to jointly quantify impacts across resource competition, ecosystem carrying capacity, and community equity. Our methodological advance lies in uncovering dynamic feedback mechanisms between individual decision-making and system resilience, alongside spatially heterogeneous trade-off patterns. Applied to Southern California, the framework identifies cumulative environmental stress hotspots and critical resource bottlenecks under distinct transition pathways, thereby enabling differentiated deployment optimization and adaptive policy design grounded in empirical ecological and socio-spatial constraints.

This work addresses the physical constraints—such as energy availability, cooling capacity, and network bandwidth—that challenge the sustainable operation of AI infrastructure, which traditional software-level optimizations alone cannot resolve. The authors propose a joint compute-network optimization framework that explicitly incorporates carbon intensity, water usage, and power capacity as hard constraints within a closed-loop system to co-schedule computing and optical networking resources. A key innovation is the introduction of the “Feasible Sovereign Operating Region” (FSOR), which transforms infeasible solutions into precise decision signals for infrastructure expansion or load curtailment. By integrating task scheduling with optical circuit routing through a scenario-driven approach and embedding multidimensional sustainability constraints, the framework significantly reduces environmental impact, demonstrating its effectiveness in enhancing the sustainability of AI infrastructure under real-world physical limitations.

This paper addresses the joint online optimization of service price $p$ and service capacity $mu$ in a queueing system where demand and service duration distributions are unknown, aiming to maximize cumulative expected profit (revenue minus capacity cost and delay penalty). Departing from the conventional two-stage “predict-then-optimize” paradigm, we propose an end-to-end online learning framework that intrinsically incorporates parameter estimation error into the decision process, enabling error-aware robust optimization. Our algorithm integrates stochastic approximation, queueing-theoretic modeling, and online convex optimization, with theoretical guarantees on convergence and an $O(sqrt{T})$ regret upper bound. Extensive simulations demonstrate that our approach improves profit by 12%–28% over benchmark policies across diverse representative scenarios.

Traditional capacity market mechanisms—relying on expected loss-of-load expectation (LOLE)—fail to adequately price reliability risks under high renewable penetration, where volatile wholesale electricity prices undermine system reliability. Method: This paper pioneers a risk-sensitive capacity premium pricing framework by modeling capacity commitments as financial put options written on wholesale electricity prices. It introduces a Markov regime-switching model (MRSP) to capture structural price jumps, moving beyond static expected-value metrics. Contribution/Results: Integrating historical price time-series analysis with multi-regional, cross-market empirical validation, the proposed framework generates a risk-calibrated capacity premium interval that significantly enhances price stability and improves systemic risk coverage. The approach provides both theoretical foundations and actionable design principles for next-generation reliability mechanisms in modern electricity markets.

Traditional scaling law estimation suffers from high computational costs due to the absence of efficient budget allocation strategies. This work proposes a novel approach that, for the first time, integrates surrogate-guided pruning into scaling law modeling by combining the Successive Halving algorithm with both parametric and non-parametric surrogate models. This integration enables proactive allocation of computational resources and efficient construction of loss-compute Pareto frontiers. The method substantially improves resource utilization efficiency, achieving relative performance gains of up to 2.84% on real datasets and 5.47% on synthetic datasets, while reducing computational costs by as much as 98.7%.

This study addresses the challenge of efficiently identifying practically meaningful treatment effects under resource constraints and concurrent experimentation, where conventional resource allocation strategies—optimized to minimize mean squared error (MSE)—often prove suboptimal. The authors propose a novel framework that shifts the objective toward minimizing the worst-case Type II error (i.e., miss rate) by leveraging statistical power. They develop a variance inflation mechanism with a correction factor, tailored to scenarios where outcome standard deviations are either known or estimated from pilot data, and formulate optimization models under three distinct risk criteria. A fully data-driven Surrogate-S algorithm is introduced to implement the approach without requiring ground-truth variance information. Theoretical analysis demonstrates the potential inefficiency of MSE-oriented strategies in detection tasks, while numerical experiments show that the proposed method achieves near-optimal performance using only pilot-based variance estimates.

Climate policy modeling confronts high-dimensional uncertainty, hindering robust assessment of power system transition strategies. This paper proposes an efficient uncertainty analysis framework based on statistical emulators, integrated with the complex energy-system model Ftt:Power, to conduct large-scale policy–technology–economy scenario simulations at global and India-specific scales. Methodologically, it advances uncertainty quantification by systematically characterizing the breadth of transition outcomes and identifying plant construction lead times and grid interconnection delays as dominant uncertainty sources. Key findings reveal that stringent climate policies substantially narrow prediction intervals, with solar PV exhibiting the highest resilience. Critically, accelerating construction timelines and phasing out coal power emerge as the most effective levers for enhancing both transition speed and reliability. The framework balances computational efficiency with analytical rigor, offering a scalable, robustness-assessment tool for multi-scale climate policy design.

This study addresses the challenge of accurately estimating resource block (RB) capacity prior to satellite communication system deployment, where unknown spatial fading correlations pose significant overload risks. The work presents the first RB-dimensioning rule tailored for satellite coverage areas, modeling the spatial fading covariance structure via a Gaussian random field. By integrating stochastic user sampling, Monte Carlo simulations, and concentration inequality analysis, the proposed framework delivers a robust RB budget estimate under a prescribed target overload probability. Furthermore, it derives a conservative analytical upper bound on the overload probability for the resulting RB allocation. This approach provides system planners with a reliable foundation that combines simulation-based accuracy with rigorous theoretical guarantees for capacity planning in satellite networks.