Dynamic resource allocation for machine learning workloads in cloud environments remains challenging due to competing objectives of minimizing training time and operational costs while meeting Service Level Agreement (SLA) constraints. Traditional approaches employ static resource allocation or single-objective optimization, leading to either SLA violations or resource waste. We present SLA-MORL, an adaptive multi-objective reinforcement learning framework that intelligently allocates GPU and CPU resources based on user-defined preferences (time, cost, or balanced) while ensuring SLA compliance. Our approach introduces two key innovations: (1) intelligent initialization through historical learning or efficient baseline runs that eliminates cold-start problems, reducing initial exploration overhead by 60%, and (2) dynamic weight adaptation that automatically adjusts optimization priorities based on real-time SLA violation severity, creating a self-correcting system. SLA-MORL constructs a 21-dimensional state representation capturing resource utilization, training progress, and SLA compliance, enabling an actor-critic network to make informed allocation decisions across 9 possible actions. Extensive evaluation on 13 diverse ML workloads using production HPC infrastructure demonstrates that SLA-MORL achieves 67.2% reduction in training time for deadline-critical jobs, 68.8% reduction in costs for budget-constrained workloads, and 73.4% improvement in overall SLA compliance compared to static baselines. By addressing both cold-start inefficiency and dynamic adaptation challenges, SLA-MORL provides a practical solution for cloud resource management that balances performance, cost, and reliability in modern ML training environments. Our code is open-source and available at https://github.com/big-data-lab-umbc/SLA-MORL.

This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. It is constructed such that unimodal models struggle and only multimodal models can succeed: difficult examples (“benign confounders”) are added to the dataset to make it hard to rely on unimodal signals. The task requires subtle reasoning, yet is straightforward to evaluate as a binary classification problem. We provide baseline performance numbers for unimodal models, as well as for multimodal models with various degrees of sophistication. We find that state-of-the-art methods perform poorly compared to humans, illustrating the difficulty of the task and highlighting the challenge that this important problem poses to the community.

Big data is fast becoming a ubiquitous term in both academia and industry and there is a strong need for new data-centric workflow tools and techniques to process and analyze large-scale complex datasets that are growing exponentially. On the other hand, the unbound resource leasing capability foreseen in the cloud facilitates data scientists to wring actionable insights from the data in a time and cost efficient manner. In the data-centric workflow environment, scheduling data processing tasks onto appropriate resources are often driven by the constraints provided by the users. Enforcing a constraint while executing the workflow in the cloud adds a new optimization challenge on how to meet the objective while satisfying the given constraint. In this paper, we propose a new Big dAta woRkflow schEduler uNder budgeT constraint known as BARENTS that supports high-performance workflow scheduling in a heterogeneous cloud computing environment with a single objective to minimize the workflow makespan under a provided budget constraint. Our case study and experiments show the competitive advantages of our proposed scheduler. The proposed BARENTS scheduler is implemented in a new release of DATA VIEW, one of the most usable big data workflow systems in the community.

Workflow makespan is the total execution time for running a workflow in the Cloud. The workflow makespan significantly depends on how the workflow tasks and datasets are allocated and placed in a distributed computing environment such as Clouds. Incorporating data and task allocation strategies to minimize makespan delivers significant benefits to scientific users in receiving their results in time. The main goal of a task placement algorithm is to minimize the total amount of data movement between virtual machines during the execution of the workflows. In this paper, we do the following: 1) formalize the task placement problem in big data workflows; 2) propose a task placement strategy (TPS) that considers both initial input datasets and intermediate datasets to calculate the dependency between workflow tasks; and 3) perform extensive experiments in the distributed environment to demonstrate that the proposed strategy provides an effective task distribution and placement tool.

The popularity of Web 2.0 has resulted in a large number of publicly available online consumer reviews created by a demographically diverse user base. Information about the authors of these reviews, such as age, gender and location, provided by many on-line consumer review platforms may allow companies to better understand the preferences of different market segments and improve their product design, manufacturing processes and marketing campaigns accordingly. However, previous work in sentiment analysis has largely ignored these additional user meta-data. To address this deficiency, in this paper, we propose parametric and non-parametric User-aware Sentiment Topic Models (USTM) that incorporate demographic information of review authors into topic modeling process in order to discover associations between market segments, topical aspects and sentiments. Qualitative examination of the topics discovered using USTM framework in the two datasets collected from popular online consumer review platforms as well as quantitative evaluation of the methods utilizing those topics for the tasks of review sentiment classification and user attribute prediction both indicate the utility of accounting for demographic information of review authors in opinion mining.