manage budget constraints

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.

This work addresses the challenge of maximizing end-to-end success probability in structured agent workflows under hard constraints on budget and deadline. The authors propose Monte Carlo Combinatorial Planning (MCPP), a lightweight closed-loop planner that dynamically replans during execution in response to observations. MCPP employs a finite-horizon stochastic online allocation model with parallel sampling and leverages Monte Carlo simulation to estimate, in real time, the probability of successful task completion under the given constraints. Experimental results demonstrate that MCPP significantly outperforms strong baseline methods on the CodeFlow and ProofFlow benchmarks, consistently achieving higher task completion rates across diverse budget–deadline configurations. These findings validate MCPP’s effectiveness and robustness in resource-constrained scenarios.

In multicore real-time systems, joint allocation of memory bandwidth and cache resources remains challenging, leading to unpredictable task execution times. Method: This paper proposes a co-optimization framework for preemptive EDF scheduling, formulating a 0–1 linear programming model and designing a two-level heuristic: an outer-layer Pareto-pruning search for multi-objective trade-offs, and an inner-layer dynamic programming algorithm to efficiently solve the coupled bandwidth-and-cache-partitioning knapsack problem. Contribution/Results: It is the first work to unify memory bandwidth control (MemGuard) and cache set partitioning into a single multi-objective co-allocation scheme, implemented and validated on the Jailhouse virtualization platform. Experimental evaluation on the Xilinx ZCU102 platform demonstrates that our approach significantly improves schedulability and resource utilization over state-of-the-art MIP-based methods, yields a superior Pareto-optimal solution set, and achieves higher computational efficiency—outperforming all existing approaches comprehensively.

To address resource supply-demand imbalances—manifesting as shortages and surpluses—across globally distributed heterogeneous computing clusters, this paper proposes a resource rationing mechanism grounded in real-world market economics. Methodologically, it introduces a periodic simulated-clock auction framework integrating utilization-driven reserve-price setting, long-term resource quota modeling, and supply-demand equilibrium pricing, enabling dynamic price signals to guide users’ autonomous job placement decisions. Its key contribution lies in being the first to systematically embed microeconomic market mechanisms into large-scale distributed resource allocation, replacing static quota or immediate-scheduling paradigms. Evaluated on the Google experimental market, the mechanism significantly incentivizes user migration toward underutilized clusters: resource utilization variance decreases by 32%, and shortage rate drops by 41%. These results empirically validate that price-based incentives can effectively drive system-level behavioral optimization and achieve global resource equilibrium.

Existing scheduling theory struggles to handle multi-resource job scenarios with continuously distributed resource demands, as it relies on the assumption of finitely many job types—a simplification inconsistent with the high heterogeneity observed in real-world workloads. This work proposes the first family of throughput-optimal scheduling policies for continuous multi-resource job models, encompassing both preemptive and non-preemptive variants. The approach employs an adaptive discretization mechanism that dynamically adjusts granularity based on system load and demand distribution. By integrating throughput-optimal control, distribution-aware scheduling, and queueing optimization, the method achieves theoretical optimality while substantially improving computational efficiency. Experiments demonstrate superior performance over state-of-the-art index-based policies under both parametric distributions and real-world Google Borg traces, attaining industry-leading results.

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.

This study addresses the single-machine scheduling problem of minimizing the number of tardy jobs (1||∑Uⱼ). The authors propose a greedy algorithm based on Shortest Job First (SJF), which accepts jobs in non-decreasing order of processing time while preserving schedule feasibility. The key contribution lies in uncovering a nested matroid structure across job levels, enabling the construction of a layered matroid model. By modeling deadline prefix constraints via a flow network, they derive the rank function of the associated feasibility polyhedron. The algorithm achieves O(n log n) time complexity using a balanced augmented binary search tree keyed by deadlines, without requiring preemption or amortized analysis. This work not only yields an efficient deterministic solution but also establishes a deep theoretical connection among scheduling feasibility, matroid theory, and network flows.

This study addresses the Resource-Constrained Project Scheduling Problem (RCPSP) by proposing a modeling approach based on timed Petri nets, wherein scheduling decisions are represented as transitions in the state space triggered by relative delay tokens. Building upon this formulation, the authors design an admissible A* heuristic that integrates critical path information with resource-constrained lower bounds to enable efficient optimal search. Experimental results on the PSPLIB benchmark suite demonstrate that the proposed method outperforms state-of-the-art mixed-integer programming solvers such as SCIP and CBC in both solution success rate and computational time. Furthermore, the findings reveal a complementary performance relationship between heuristic search and mixed-integer programming approaches across varying problem scales.