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Open research questions in Cloud Computing and Resource Management

125 unresolved questions extracted from the limitations and future-work sections of 508 Cloud Computing and Resource Management papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • The work does not provide a comprehensive comparison with other state-of-the-art methods - The experiments are limited to a real-world data set, and may not generalize to other scenarios

    Learning Virtual Machine Scheduling in Cloud Computing Through Language Agents · 2026 · DOI

  • Further research is needed to improve the scalability and efficiency of MiCo - Investigating the application of MiCo to other complex scheduling problems

    Learning Virtual Machine Scheduling in Cloud Computing Through Language Agents · 2026 · DOI

  • The need to limit user access to the CWDU database. The lack of a testing environment for code changes.

    Microsoft Designing and Implementing Cloud Data Platform Solutions PDF · 2026 · DOI

  • Classical heuristics are not efficient in convergence and get stuck in local optima. The presence of the so-called dimensionality curse is a major limitation of conventional optimization algorithms.

    A Quantum-Inspired Evolutionary Framework for Multi-Objective Task Scheduling in Sustainable Cloud Computing Environments · 2026 · DOI

  • The paper claims the QPU identifies the global optimum four times faster than classical search strategies but does not provide detailed comparative analysis or theoretical justification for this claim.

    A Quantum-Inspired Evolutionary Framework for Multi-Objective Task Scheduling in Sustainable Cloud Computing Environments · 2026 · DOI

  • The limitations of SQL systems in distributed and big-data contexts. The need for a flexible data model that can handle large volumes of semi-structured or unstructured data. The importance of selecting an appropriate database architecture based on the application’s requirements.

    Analyzing the Impact of Database Architecture on Performance: SQL vs NoSQL · 2026 · DOI

  • The limitations of SQL systems in distributed and big-data contexts have become more evident. There is a need for a comparative analysis of SQL and NoSQL database systems.

    Analyzing the Impact of Database Architecture on Performance: SQL vs NoSQL · 2026 · DOI

  • Traditional statistical methods often struggle with the high dynamic, nonlinear, and burst workloads of modern cloud environments. Machine learning and deep learning models have limitations in handling complex and variable workloads.

    An Adaptive Deep Learning Framework with Enhanced Attention for Precise Load Forecasting in Cloud Computing Environments · 2026 · DOI

  • The CNN-Bi-LSTM-Attention architecture uses fixed hyperparameters (CNN kernel sizes, Bi-LSTM hidden dimensions, attention heads) across all experiments. The sensitivity of the model to hyperparameter variations and whether architecture components should be automatically tuned based on workload characteristics or time-series properties is not addressed.

    An Adaptive Deep Learning Framework with Enhanced Attention for Precise Load Forecasting in Cloud Computing Environments · 2026 · DOI

  • The gap is the lack of a comprehensive overview of the challenges and strategies for optimizing ML performance on parallel computing clusters. The review highlights the need for further research on the implementation of ML on parallel computing clusters. The paper identifies areas for improvement in the implementation of ML on parallel computing clusters, including communication optimization and scheduling.

    Scientific and practical basis for implementing machine learning on a parallel computing cluster: a systematic mapping review · 2026 · DOI

  • This review synthesized findings from 21 studies and also noted several limitations. spanning multiple databases, subscription-only literature. longer time windows, and 7.3 Future research directions Future work should extend the existing frameworks to heterogeneous and dynamic environments by incorporating adaptive load balancing and dynamic scheduling techniques.

    Scientific and practical basis for implementing machine learning on a parallel computing cluster: a systematic mapping review · 2026 · DOI

  • Cloud computing continues to evolve rapidly, driven by advancements in artificial intelligence, networking technologies, and distributed systems. Quantum computing is expected to integrate with cloud platforms.

    Cloud Computing: Benefits and Challenges · 2026 · DOI

  • Security concerns, data privacy issues, regulatory compliance, and vendor lock-in remain significant obstacles. Compliance with regional data laws can be complex.

    Cloud Computing: Benefits and Challenges · 2026 · DOI

  • Most prior studies focus on workload-level or system-level prediction, with limited attention to job-level prediction under heavy-tailed distributions. Existing approaches rely on a single global predictor, which may be less robust.

    Log-Transformed Regime-Based Prediction of Cloud Job Length Using Machine Learning · 2026 · DOI

  • Future work may focus on incorporating richer contextual features, exploring uncertainty-aware prediction, and extending evaluation to multiple datasets in order to improve generalizability and better capture extreme-tail be- havior. Error analysis also reveals that the proposed method still underestimates the largest spikes, indicating that accurate prediction of rare extreme jobs remains an open chal- lenge. 500 that the regime-aware model consistently achieves the best mean RMSE and mean R² across temporal folds, suggesting that its advantage is not limited to a single data partition.

    Log-Transformed Regime-Based Prediction of Cloud Job Length Using Machine Learning · 2026 · DOI

  • Orchestration and reactive auto-scaling systems cannot support dynamic workloads of heterogeneous resources and complex service-level objectives (SLOs). Elastic scaling and tenant isolation are still important issues in multi-tenant SaaS systems.

    AI-Agent–Based Autonomous Cloud Orchestration for Multi-Tenant and Scalable SaaS Platforms · 2026 · DOI

  • Over-provisioning, where resources remain idle and increase operational cost. Under-provisioning, which results in performance degradation and poor user experience. The need for a real-time intelligent system to optimize cloud resource allocation.

    Intelligent Cloud Resource Optimization System Using Machine Learning · 2026 · DOI

  • Integrating real-time cloud platforms and exploring advanced deep learning models. Extending the concept of predictive resource allocation to other areas of cloud computing.

    Intelligent Cloud Resource Optimization System Using Machine Learning · 2026 · DOI

  • The lack of integrated dynamic models of data centers for power system stability studies. The need for accurate modeling of data center power consumption and temperature dynamics.

    Dynamic Modeling of a Data Center for Power System Stability Studies · 2026 · DOI

  • The lack of high-performance distributed data frame solutions that can support ML data-intensive applications. The need for a serverless communicator to tackle communication and performance issues associated with serverless functions.

    Combining serverless and high-performance computing paradigms to support ML data-intensive applications · 2026 · DOI

  • While our serverless communicator design demonstrates performance comparable to traditional EC2 infrastructure, several limitations remain. First, Cylon currently implements only about 30% of the operators supported by Pandas, limiting its applicability for certain data processing workflows. Second, AWS Lambda imposes an upper limit of fifteen minutes for function invocations, which, combined with our BSP-styled architecture, restricts the applicability of data parallel processing for large datasets that require extended processing time. Third, the lack of checkpointing and fault tolerance mechanisms limits the ability to recover from failures or time-constrained execution boundaries in serverless environments. To address these limitations, we would like to converge Cylon and Pandas operators using a comparative, high-performance API built using Cython. For example, we have begun to design the windowing operator and plan to complete that work soon. Utilizing the cost model (detailed by Perera in “Towards Scalable High Performance Data Engineering Systems”), we can evaluate data parallel operator patterns to identify bottlenecks in communication operations. We believe we can significantly improve performance by designing other algorithms with lower latency (Perera et al., 2023). Additionally, related to communicator support, we would also like to add libfabric as a communicator based on implementations across cloud services based on GPU hardware supported by cloud providers. To address the time limitation and lack of fault tolerance, we want to design a checkpointing feature similar to that implemented in the Twister2 library. This will allow for the recovery of unfinished executions based on upper-limit time constraints imposed by AWS Lambda. This will also provide general fault tolerance for MPI workloads outside of the serverless use case. Lastly, we would like to investigate real-world applications and detail the performance and implications of serverless as a possibility for deep learning and machine learning inference on clouds. For example, we would like to implement hydrology and earthquake prediction deep learning on both serverful and serverless AWS to demonstrate the applicability and potential scale offered by cloud providers like as AWS.

    Combining serverless and high-performance computing paradigms to support ML data-intensive applications · 2026 · DOI

  • Existing systems have limitations in mitigating interference and improving throughput. There is a need for a novel LLM serving system that efficiently harnesses CPU and GPU resources.

    Serving Hybrid LLM Loads with SLO Guarantees Using CPU-GPU Attention Piggybacking · 2026 · DOI

  • Existing automatic clustering approaches lack flexibility in dynamic environments. There is a need for a workload-aware algorithm that can identify and recluster boundary micro-partitions. The gap is in proposing a practical automatic reclustering framework that can improve query performance and reduce the overall cost.

    Workload-Aware Incremental Reclustering in Cloud Data Warehouses · 2026 · DOI

  • The lack of consideration for energy efficiency and environmental impact in traditional cloud scheduling systems. The need for energy-efficient and carbon-aware scheduling methods to improve sustainability.

    Carbon-Aware Intelligent Scheduling Framework for Energy-Efficient Green Cloud Computing · 2026 · DOI

  • Optimizing energy use in cloud computing environments remains a significant challenge. Task scheduling is a critical component of cloud efficiency, aiming to assign tasks to appropriate virtual machines.

    Optimized Scheduling of Jobs using Load Balancing in Cloud Computing With Economic Perspectives · 2026 · DOI

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125 open questions have been extracted from the limitations and future-work passages of 508 Cloud Computing and Resource Management papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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