Science AI Journal
Computer Science · Research topic

Open research questions in Cloud Computing and Resource Management

190 gap statements mined from Cloud Computing and Resource Management papers in our 7.9M-paper local library, which holds 1,980 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

Choosing where to publish on Cloud Computing and Resource Management? See the ranked Computer Science journals.

What the literature leaves open

  • Prevailing schedulers rely on static heuristics that cannot adapt to uncertainty. Online reinforcement learning is unsafe and sample-inefficient in production.

    Uncertainty-aware offline reinforcement learning for LLM inference serving with heterogeneous SLOs · 2026 · DOI

  • The gap is the lack of efficient algorithms for solving the graph coloring problem in distributed peer-to-peer networks. The gap also includes the need for understanding the influence of graph density on the efficiency of greedy algorithms. The paper aims to fill this gap by investigating the application of graph theory and variants of greedy graph coloring algorithms.

    Application of Graph Theory and Variants of Greedy Graph Coloring Algorithms for Optimization of Distributed Peer-to-Peer Blockchain Networks · 2025 · DOI

  • The lack of predictive scaling mechanisms. The need for more efficient auto-scaling techniques that can handle fluctuating demands. The requirement for advanced machine learning techniques to improve auto-scaling.

    Auto-Scaling Techniques in Cloud Computing: Issues and Research Directions · 2024 · DOI

  • The lack of robust solutions to ensure stability in the energy supply due to the intermittent nature of renewable energy sources. The need for innovative approaches in fuel technology and computational modeling to drive the transition toward sustainable combustion. The challenge of optimizing process parameters in PtX systems.

    Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review · 2024 · DOI

  • Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.

    Hybrid spotted hyena and whale optimization algorithm-based dynamic load balancing technique for cloud computing environment · 2025 · DOI

  • Despite extensive research on service reliability, the impact of network load and routing on service reliability remains understudied, leading to suboptimal models and unsatisfactory performance.

    Network-Aware Reliability Modeling and Optimization for Microservice Placement · 2025 · DOI

  • The issue of digitalization in chemical technology production is currently quite pressing, and the available computational infrastructure is insufficient for assessing the technological properties of the products obtained through mathematical modeling tools.

    Development of a Cloud Service for Comprehensive Research of Polymer Synthesis Processes · 2024 · 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 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

  • 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 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

  • 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 behavior. Error analysis also reveals that the proposed method still underestimates the largest spikes, indicating that accurate prediction of rare extreme jobs remains an open challenge.

    Log-Transformed Regime-Based Prediction of Cloud Job Length 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

  • 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.

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

  • 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

  • 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

  • Investigating the application of other optimization algorithms for distributed peer-to-peer blockchain networks, - Analyzing the impact of different graph densities on the efficiency of greedy algorithms, - Exploring the use of greedy algorithms in other distributed…

    Application of Graph Theory and Variants of Greedy Graph Coloring Algorithms for Optimization of Distributed Peer-to-Peer Blockchain Networks · 2025 · DOI

  • Handling sudden workload fluctuations. Defining rules for threshold-based auto-scaling. Optimizing resource allocation in multi-cloud environments. Predicting future workload patterns.

    Auto-Scaling Techniques in Cloud Computing: Issues and Research Directions · 2024 · DOI

  • This review synthesized findings from 21 studies and also noted several limitations.

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

Most-cited papers in Cloud Computing and Resource Management

Most recent work

Find a gap in your own Cloud Computing and Resource Management sub-topic

This page shows what the Cloud Computing and Resource Management literature already flags as unresolved. To narrow it to your specific question, search the Research Gap Finder: the search is free with a free account and lists the papers closest to your topic first. Unlocking that topic (50 credits, charged once) fills the comparison table from our 7.9M-paper local library and writes the gaps from its rows.

Open the Research Gap Finder →

Related topics in Computer Science

Online Learning and Analytics
1,253 open questions
Advanced Neural Network Applications
1,236 open questions
AI in Service Interactions
1,161 open questions
Virtual Reality Applications and Impacts
1,060 open questions
Computational Drug Discovery Methods
1,019 open questions
Quantum Computing Algorithms and Architecture
877 open questions
AI in cancer detection
835 open questions
Topic Modeling
780 open questions
Open questions raised by papers listed on this page include: (1) 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); (2) The gap is the lack of a comprehensive overview of the challenges and strategies for optimizing ML performance on parallel computing clusters (“Scientific and practical basis for implementing machine learning on a parallel…”, 2026); (3) 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 behavior (“Log-Transformed Regime-Based Prediction of Cloud Job Length Using Machine…”, 2026); (4) The lack of integrated dynamic models of data centers for power system stability studies (“Dynamic Modeling of a Data Center for Power System Stability Studies”, 2026); (5) The gap is the lack of efficient algorithms for solving the graph coloring problem in distributed peer-to-peer networks (“Application of Graph Theory and Variants of Greedy Graph Coloring Algorithms…”, 2025). 190 gap statements have been mined from Cloud Computing and Resource Management papers in our 7.9M-paper local library, which holds 1,980 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

Tools for your next paper

Gaps & Literature SearchFind open questions in any field — 1,100,000+ open gaps mined from our own library.Pre-CheckIs your paper ready? Acceptance odds for top, mid and open/emerging journals, in seconds.Journal FinderA ranked shortlist of target journals from a 17,500-venue index.Duplicate Publication CheckerCheck for prior publication across 7 sources and our local library.Citation GeneratorFreeA DOI or title → APA, MLA, Vancouver, Chicago, BibTeX, RIS. No signup.Graphical Abstract MakerFreeTurn your findings into a colorblind-safe graphical abstract.

How Pre-Check worksHow AI Review works

Compare the category — Honest roundups of the AI research tools, ours listed alongside the alternatives.

Command palette

Jump anywhere, run any action.