This is the accepted author's version of the paper. The final published version is available in the 2015 IEEE 39th Annual Com puter Software and Applications Conference, vol. Abstract -- This paper presents a strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as consideration s for the service migration costs. A prototype meta - heuristic load balancer is demonstrated and experiment al results are presented and discussed. We also propose a novel genetic algorithm, wher e population is seeded with the outputs of other meta - heuristic algorithms. Modern day applications are often designed in such a way that they can simultaneously use resources from different computer environments. System components are not just properties of individual machines and in many respects they can be viewed as though the y are deployed in a single application environment. Distributed computing differs from traditional computing in many ways.

Selecting the right algorithm is an important problem in computer science, because the algorithm often has to exploit the structure of the input to be efficient. The human mind faces the same challenge. Therefore, solutions to the algorithm selection problem can inspire models of human strategy selection and vice versa. Here, we view the algorithm selection problem as a special case of metareasoning and derive a solution that outperforms existing methods in sorting algorithm selection. We apply our theory to model how people choose between cognitive strategies and test its prediction in a behavioral experiment. We find that people quickly learn to adaptively choose between cognitive strategies. People's choices in our experiment are consistent with our model but inconsistent with previous theories of human strategy selection. Rational metareasoning appears to be a promising framework for reverse-engineering how people choose among cognitive strategies and translating the results into better solutions to the algorithm selection problem.

Being prompted to engage in reasoning has emerged as a core technique for using large language models (LLMs), deploying additional inference-time compute to improve task performance. However, as LLMs increase in both size and adoption, inference costs are correspondingly becoming increasingly burdensome. This work introduces a novel approach based on computational models of metareasoning used in cognitive science, training LLMs to selectively use intermediate reasoning steps only when necessary. We first develop a reward function that incorporates the Value of Computation by penalizing unnecessary reasoning, then use this reward function with Expert Iteration to train the LLM. Compared to few-shot chain-of-thought prompting and STaR, our method significantly reduces inference costs (20-37% fewer tokens generated across three models) while maintaining task performance across diverse datasets. Large language models (LLMs) rely on substantial computational power to handle complex problems (OpenAI et al., 2024; Chowdhery et al., 2022; de Vries, 2023). While initial studies mostly focused on the cost of training (Verdecchia et al., 2023), LLMs' widespread deployment has made inference-time costs an increasingly important factor. However, there is a fundamental tension between inference cost and task performance: while many of these methods reduce costs at the expense of performance, others, such as chain-of-thought prompting (CoT; Wei et al., 2023; Kojima et al., 2023), do the opposite, raising inference costs to enhance task performance (Snell et al., 2024). It is worth noting that none of the previous approaches are adaptive: model compression modifications and existing CoT methods tend to raise or lower the inference cost on all queries, regardless of task complexity.

Effective training of large Vision-Language Models (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient fine-tuning (PEFT) strategies. To this end, we demonstrate the impact of two factors \textit{viz.}, client-specific layer importance score that selects the most important VLM layers for fine-tuning and inter-client layer diversity score that encourages diverse layer selection across clients for optimal VLM layer selection. We first theoretically motivate and leverage the principal eigenvalue magnitude of layerwise Neural Tangent Kernels and show its effectiveness as client-specific layer importance score. Next, we propose a novel layer updating strategy dubbed F$^3$OCUS that jointly optimizes the layer importance and diversity factors by employing a data-free, multi-objective, meta-heuristic optimization on the server. We explore 5 different meta-heuristic algorithms and compare their effectiveness for selecting model layers and adapter layers towards PEFT-FL. Furthermore, we release a new MedVQA-FL dataset involving overall 707,962 VQA triplets and 9 modality-specific clients and utilize it to train and evaluate our method. Overall, we conduct more than 10,000 client-level experiments on 6 Vision-Language FL task settings involving 58 medical image datasets and 4 different VLM architectures of varying sizes to demonstrate the effectiveness of the proposed method.

Trust is a crucial factor affecting the adoption of machine learning (ML) models. Qualitative studies have revealed that end-users, particularly in the medical domain, need models that can express their uncertainty in decision-making allowing users to know when to ignore the model's recommendations. However, existing approaches for quantifying decision-making uncertainty are not model-agnostic, or they rely on complex statistical derivations that are not easily understood by laypersons or end-users, making them less useful for explaining the model's decision-making process. This work proposes a set of class-independent meta-heuristics that can characterize the complexity of an instance in terms of factors are mutually relevant to both human and ML decision-making. The measures are integrated into a meta-learning framework that estimates the risk of misclassification. The proposed framework outperformed predicted probabilities in identifying instances at risk of being misclassified. The proposed measures and framework hold promise for improving model development for more complex instances, as well as providing a new means of model abstention and explanation.

We can achieve significant gains in the value of computation by metareasoning about the nature or extent of base-level problem solving before executing a solution. However, resources that are irrevocably committed to metareasoning are not available for executing a solution. Thus, it is important to determine the portion of resources we wish to apply to metareasoning and control versus to the execution of a solution plan. Recent research on rational agency has highlighted the importance of limiting the consumption of resources by metareasoning machinery. We shall introduce the metareasoning-partition problem–the problem of ideally apportioning costly reasoning resources to planning a solution versus applying resource to executing a solution to a problem. We exercise prototypical metareasoning-partition models to probe the relationships between time allocated to metareasoning and to execution for different problem classes.

Selecting the right algorithm is an important problem in computer science, because the algorithm often has to exploit the structure of the input to be efficient. The human mind faces the same challenge. Therefore, solutions to the algorithm selection problem can inspire models of human strategy selection and vice versa. Here, we view the algorithm selection problem as a special case of metareasoning and derive a solution that outperforms existing methods in sorting algorithm selection. We apply our theory to model how people choose between cognitive strategies and test its prediction in a behavioral experiment.

Today, image classification is a common way for systems to process visual content. Although neural network approaches to classification have seen great progress in reducing error rates, it is not clear what this means for a cognitive system that needs to make sense of the multiple and competing predictions from its own classifiers. As a step to address this, we present a novel framework that uses meta-reasoning and meta-operations to unify predictions into abstractions, properties, or relationships. Using the framework on images from ImageNet, we demonstrate systems that unify 41% to 46% of predictions in general and unify 67% to 75% of predictions when the systems can explain their conceptual differences. We also demonstrate a system in "the wild" by feeding live video images through it and show it unifying 51% of predictions in general and 69% of predictions when their differences can be explained conceptually by the system. In a survey given to 24 participants, we found that 87% of the unified predictions describe their corresponding images.

When minimizing makespan during off-line planning, the fastest action sequence to reach a particular state is, by definition, preferred. When trying to reach a goal quickly in on-line planning, previous work has inherited that assumption: the faster of two paths that both reach the same state is usually considered to dominate the slower one. In this short paper, we point out that, when planning happens concurrently with execution, selecting a slower action can allow additional time for planning, leading to better plans. We present Slo'RTS, a metareasoning planning algorithm that estimates whether the expected improvement in future decision-making from this increased planning time is enough to make up for the increased duration of the selected action. Using simple benchmarks, we show that Slo'RTS can yield shorter time-to-goal than a conventional planner. This generalizes previous work on metareasoning in on-line planning and highlights the inherent uncertainty present in an on-line setting.

While optimal metareasoning is notoriously intractable, humans are nonetheless able to adaptively allocate their computational resources. A possible approximation that humans may use to do this is to only metareason over a finite set of cognitive systems that perform variable amounts of computation. The highly influential "dual-process" accounts of human cognition, which postulate the coexistence of a slow accurate system with a fast error-prone system, can be seen as a special case of this approximation. This raises two questions: how many cognitive systems should a bounded optimal agent be equipped with and what characteristics should those systems have? We investigate these questions in two settings: a one-shot decision between two alternatives, and planning under uncertainty in a Markov decision process. We find that the optimal number of systems depends on the variability of the environment and the costliness of metareasoning. Consistent with dual-process theories, we also find that when having two systems is optimal, then the first system is fast but error-prone and the second system is slow but accurate.