trade-off analysis

Multi-objective trade-offs—such as performance versus cost or security versus usability—are pervasive in software engineering, yet systematic tool support for exploring them remains lacking, hindering both research and practice. To address this, we introduce MOOT: the first domain-specific, empirically grounded repository of multi-objective optimization tasks for software engineering. MOOT systematically curates and standardizes over 120 real-world, evidence-based tasks drawn from configuration tuning, cloud resource management, project health assessment, and other SE domains. Hosted openly on GitHub under the MIT license, MOOT employs rigorous empirical methods for task extraction, formal modeling, and validation, enabling reproducible benchmarking and community-driven extension. Its key innovation lies in establishing the first structured, computationally tractable, and application-driven multi-objective benchmark tailored to software engineering. MOOT has already inspired dozens of new research questions and is advancing the field’s decision-making paradigm—from anecdotal judgment toward data-driven, quantitative trade-off analysis.

This study addresses the challenge of efficiently generating and managing Pareto-optimal solution sets (SOS) in heterogeneous multi-task environments. It proposes an evolutionary multi-task optimization framework to construct compact, task-specific SOS repositories for real-world applications such as engineering design, inventory management, and hyperparameter optimization. The work introduces a novel similarity metric between Pareto sets and, for the first time, systematically validates the cross-domain applicability of SOS. Through visualization and objective space analysis, it reveals dynamic patterns in solution set performance across diverse task contexts. Experimental results demonstrate that the proposed approach effectively captures inter-task differences in solution sets and significantly enhances decision-making support across varying scenarios.

This study addresses the complex trade-offs among cost, performance, and latency in mobile edge computing scenarios, where existing research lacks a systematic modeling framework for edge–cloud architectures. The authors present the first closed-form queueing network model to quantitatively analyze how workload mobility and system dynamics jointly impact end-to-end latency and operational cost. Through both simulation and real-world validation, the model’s accuracy and practical relevance are demonstrated. The work highlights the critical roles of mobility patterns and system utilization, offering theoretical foundations and actionable insights for resource provisioning and optimization in 5G and future mobile edge computing systems.

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.

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.

This work addresses the challenge of efficiently and accurately approximating the Pareto front in stochastic multi-objective optimization (SMOO), where probabilistic reasoning often impedes precise characterization. To this end, we propose XOR-SMOO, the first algorithm that integrates hashing techniques, XOR constraints, and randomized sampling to achieve a tight constant-factor approximation of a #P-hard problem with high probability, using only a polylogarithmic number of SAT oracle queries. By drastically reducing computational complexity, XOR-SMOO generates Pareto fronts that are more comprehensive, uniformly distributed, and superior in objective values compared to existing baselines, as demonstrated in real-world applications such as road network fortification and supply chain design.

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.

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 work addresses the inefficiency and suboptimality of manually tuned weight assignments in multi-objective reinforcement learning for industrial automation. It proposes the first integration of multi-objective Bayesian optimization with reinforcement learning, leveraging a sampling strategy based on the expected hypervolume improvement (qEHVI) to efficiently explore the Pareto front between energy consumption and control performance. Evaluated on the Quanser Aero 2 platform in a one-degree-of-freedom pitch control task, the method significantly outperforms uniform grid search by achieving superior hypervolume metrics and broader policy distribution coverage with substantially fewer evaluations. This demonstrates a sample-efficient approach to automatically discovering diverse Pareto-optimal policies without manual trade-off tuning.

This work addresses the optimization of multiple tasks that share identical monotone submodular objective functions but are subject to distinct knapsack constraints. The authors propose a multi-task Pareto optimization approach that jointly solves all tasks in a single run. By integrating multi-task learning into the Pareto optimization framework for the first time, the method leverages a shared population to facilitate solution transfer across tasks and introduces a novel mechanism to generate compact Pareto fronts, thereby enhancing computational efficiency. Theoretical analysis establishes that the algorithm achieves a (1−1/e)-approximation guarantee for each task within expected polynomial runtime. Empirical results demonstrate significant performance gains over independent optimization baselines when element costs are uniform, while also revealing limitations under heterogeneous cost settings.