Adaptive Multi-Cloud Architecture: An Intelligent, Secure and Policy-Aware Framework for Workload Orchestration
- 1R. C. Patel College of Engineering and Polytechnic, Shirpur, Maharashtra, India
Abstract
Multi-cloud computing enables organizations to distribute applications and data across services from multiple cloud providers. It can improve flexibility and reduce dependence on a single provider, but also creates operational complexity: providers expose different interfaces, performance varies over time, costs are difficult to compare, and security or data-residency rules may restrict where workloads can execute. This paper proposes an Adaptive Multi-Cloud Decision and Orchestration Framework (AMCDOF) that combines mandatory policy validation with multi-objective workload placement. The framework first classifies each workload and checks security, privacy, residency, and organizational constraints. Only eligible environments are ranked using a configurable score based on latency, expected cost, availability, performance, and operational risk. An orchestration controller deploys the workload, monitors runtime signals, and reevaluates placement when conditions change. The paper specifies a conceptual decision model, modular architecture, pseudocode, and an empirical evaluation plan. No measured performance improvement is claimed: the framework is a design proposal requiring implementation and controlled testing.
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BibTeX
@misc{jade2026adaptive,
title = {{Adaptive Multi-Cloud Architecture: An Intelligent, Secure and Policy-Aware Framework for Workload Orchestration}},
author = {Tanmay Jade},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23526485},
url = {https://zenodo.org/records/23526485},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Adaptive Multi-Cloud Architecture: An Intelligent, Secure and Policy-Aware Framework for Workload Orchestration
AU - Tanmay Jade
PY - 2026
DA - 2026/10/21
PB - Zenodo
DO - 10.5281/zenodo.23526485
UR - https://zenodo.org/records/23526485
AB - Multi-cloud computing enables organizations to distribute applications and data across services from multiple cloud providers. It can improve flexibility and reduce dependence on a single provider, but also creates operational complexity: providers expose different interfaces, performance varies over time, costs are difficult to compare, and security or data-residency rules may restrict where workloads can execute. This paper proposes an Adaptive Multi-Cloud Decision and Orchestration Framework (AMCDOF) that combines mandatory policy validation with multi-objective workload placement. The framework first classifies each workload and checks security, privacy, residency, and organizational constraints. Only eligible environments are ranked using a configurable score based on latency, expected cost, availability, performance, and operational risk. An orchestration controller deploys the workload, monitors runtime signals, and reevaluates placement when conditions change. The paper specifies a conceptual decision model, modular architecture, pseudocode, and an empirical evaluation plan. No measured performance improvement is claimed: the framework is a design proposal requiring implementation and controlled testing.
ER -