Machine Learning-Driven Resource Orchestration in Cloud-Native Environments: Toward Self-Optimizing Systems
DOI:
https://doi.org/10.15662/IJARCST.2025.0802007Keywords:
Machine Learning in Cloud Computing, Resource Orchestration, Cloud-Native Systems, Self-Optimizing Systems, Autonomous Cloud ManagementAbstract
Cloud-native environments are fast changing how organizations design, deploy and manage digital infrastructures. The fundamental element of this change is the difficulty of coordinating resources in which, in very dynamic and distributed ecosystems, computational, storage, and networking resources have to be optimally distributed. Conventional orchestration systems cannot be said to respond easily to the challenges of large scale scalability, real-time adaptation and security threats. In this regard, machine learning (ML) has become an effective facilitator of self-optimizing systems with the ability to make autonomous decisions. Recent publications assert the role of ML-based practices in augmenting the cyber security strategies in cloud-based computing through the provision of predictive functions that counter the vulnerabilities of industries. These improvements are not only providing a stronger resilience of the system, but also are creating opportunities of smarter coordination of cloud-native resources. This study analyses the place of ML-based orchestration in the cloud-native setting through the synthesis of theoretical basics, available frameworks, and practical implementations. A synthesized survey of thirty academic papers offers the understanding of the way ML methods facilitate optimization of various cloud services, such as security-conscious allocation to energy-efficient resource schedule. The paper also examines the development of self-optimizing architecture, in which ML allows feedback loop adaptation. It has been found that the inclusion of ML in orchestration mechanisms allows improving the scalability, reliability, and sustainability in addition to solving major problems like unpredictability of workloads and heterogeneity of systems. The paper also leads to the further development of the knowledge of ML-driven orchestration, as it creates a conceptual framework of autonomous cloud-native systems. It finds the gaps in research on interoperability, ethical AI use, and cross-layer coordination issues. Finally, the paper claims that ML-based orchestration is a preliminary step on the way to the fully self-optimizing systems that will be able to transform the future of cloud computing.
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