Adaptive Task Scheduling And Resource Optimization Using Ai Middleware

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 13 | 01 | Page :
    By

    Pratik Danodia,

  • Kamlesh Lakhwani,

  • Bhavna Sharma,

  1. Student, Department of CSE, JECRC University, Jaipur, Rajasthan, India
  2. Professor, Department of CSE, JECRC University, Jaipur, Rajasthan, India
  3. Associate Professor, Department of CSE, JECRC University, Jaipur, Rajasthan, India

Abstract

Modern distributed and heterogeneous computing systems face significant challenges in dealing with dynamically changing workloads, resource fragmentation, and changing latencies; existing traditional, or rule-based, schedulers are no longer useful in achieving the best system performance. Such limitations highlight the importance of the adaptive scheduling paradigms that are able to learn, to forecast and reaction to the real red conditions in the system. The middleware of artificial-intelligence is also an attractive solution to this issue, as it allows to incorporate machine-learning and reinforcement-learning algorithms in the orchestration layer and, therefore, promote intelligent decision-making and improve resource distribution and tasks assignment in a progressive fashion. This paper suggests a flexible scheduling system, which runs on the AI middleware, incorporates workload forecasting models, reinforcement-learning-based scheduling policies, reactive scaling, and energy-sensitive decision making. In the approach, there would be a multi-layer middleware design, which would incorporate real time monitoring, state- action-reward learning, and feedback-based scheduler. The major contributions of this paper include the creation of an RL-supported adaptive scheduling algorithm, a single middleware architecture that can be applied in distributed settings and an in-depth performance analysis covering cloud and edge computing contexts. The experimental results show that the schedulers achieve considerable benefits in delay, throughput, resource exploitation and energy efficiency when compared with the baseline schedulers. This can apply to the cloud platform environments, IoT-edge environments and enterprise distributed systems hence providing a scalable and smart solution to modern workload-management issues.

Keywords: Adaptive Scheduling, AI Middleware, Resource Optimization, Distributed Systems, Edge Computing, Workload Management

How to cite this article:
Pratik Danodia, Kamlesh Lakhwani, Bhavna Sharma. Adaptive Task Scheduling And Resource Optimization Using Ai Middleware. Recent Trends in Parallel Computing. 2026; 13(01):-.
How to cite this URL:
Pratik Danodia, Kamlesh Lakhwani, Bhavna Sharma. Adaptive Task Scheduling And Resource Optimization Using Ai Middleware. Recent Trends in Parallel Computing. 2026; 13(01):-. Available from: https://journals.stmjournals.com/rtpc/article=2026/view=242302


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Ahead of Print Subscription Review Article
Volume 13
01
Received 11/02/2026
Accepted 23/03/2026
Published 30/04/2026
Publication Time 78 Days
19

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