Machine learning-based system for cost and carbon footprint optimization in multi-cloud computing environments

Abstract

A machine learning-based system for optimizing costs and carbon footprint in multi-cloud computing environments, consisting of: a data aggregation layer (101) for collecting real-time data on costs, resource usage and carbon emissions from multiple cloud service providers; a machine learning engine (102) to predict future costs and Carbon emissions based on historical data and current cloud service parameters; a multi-objective optimization engine (103) that selects deployment strategies to minimize both costs and carbon emissions according to predefined constraints; and a workload orchestration layer (104) for executing the optimized deployment plan.

Description

Technical field:

[0001] The present invention generally relates to cloud computing and resource utilization. More specifically, it relates to a machine learning-based system and method for optimizing both costs and carbon emissions in multi-cloud computing environments. Background of the invention:

[0002] In recent years, there has been a rapid shift toward cloud computing, as businesses, institutions, and governments increasingly rely on cloud-based infrastructures to manage and process large-scale data and services. The proliferation of cloud service providers such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), IBM Cloud, and others has enabled organizations to pursue a multi-cloud strategy that leverages the unique capabilities and pricing models of each provider. The multi-cloud approach enables greater flexibility, performance optimization, geographic distribution, risk management, and vendor neutrality. However, it also introduces new challenges in managing costs, resource allocation, and environmental impact across different platforms.

[0003] Traditionally, the primary goal of cloud resource management has been to minimize operational costs while ensuring compliance with service-level agreements (SLAs) such as availability, throughput, and latency. Cloud cost optimization techniques have typically relied on static rules, simple scheduling policies, or predictive analytics to select resource types, regions, and usage durations. These approaches, while effective to some extent, often fail to adapt to the dynamic pricing models and complex interactions in real-time multi-cloud ecosystems.

[0004] Paralleling the growth of cloud computing, there is growing concern about the environmental impact of data centers. These facilities, which power the cloud, consume enormous amounts of electricity, often sourced from carbon-intensive power grids. Studies have shown that data centers contribute significantly to global greenhouse gas emissions, leading to increased scrutiny from governments, regulators, and the public. Organizations are now expected to not only optimize performance and costs but also integrate sustainability metrics, particularly carbon footprint, into their computing strategies.

[0005] Carbon-conscious computing has emerged as a critical research and development area aimed at reducing carbon emissions associated with digital infrastructure. However, implementing such strategies in practice remains challenging. The carbon intensity of energy sources varies by geography and time of day, and most cloud providers do not offer detailed visibility into the energy mix powering specific data centers. Furthermore, carbon-conscious decisions can conflict with cost-effective strategies, as clean energy regions may not align with the cheapest cloud resources or may incur higher latency.

[0006] The intersection of these challenges—multi-cloud complexity, cost variability, and sustainability mandates—creates the need for a sophisticated, intelligent system capable of making context-aware decisions. The static or rule-based systems that have dominated cost optimization are inadequate to balance the multifaceted demands of modern cloud usage. What is needed is a system that can dynamically evaluate the trade-offs between operational costs and environmental impacts, learn from past decisions, and adapt to real-time changes in resource availability, carbon intensity, and worker behavior.

[0007] Machine learning (ML), with its ability to model complex, non-linear relationships and learn from continuously evolving data, offers a powerful solution to these problems. ML can enable intelligent predictions about the future costs and carbon impacts of deployment decisions, even under uncertainty. It can detect usage patterns, uncover anomalies, and support multi-objective decision-making that requires balancing multiple, sometimes conflicting, criteria.

[0008] Despite the potential, there is a notable lack of integrated ML-based solutions that simultaneously consider cost and carbon footprint for deploying workloads in multi-cloud environments. Existing cloud management tools often address these objectives independently or narrowly focus on one aspect, failing to provide holistic optimization. Furthermore, very few systems integrate real-time carbon intensity data into workload placement decisions, nor do they use reinforcement learning to adapt and improve decision policies over time based on actual outcomes.

[0009] Given the evolving demands of enterprise cloud computing and the urgent need for environmentally responsible technology, there is a compelling need for an intelligent, adaptive system that can seamlessly integrate cost optimization with carbon-conscious decision-making. Such a system must be able to operate across multiple cloud providers, adapt to changing conditions, and continuously evolve to deliver optimal results. The present invention addresses this critical gap by introducing a machine learning-based system that delivers both financial and environmental efficiency in the complex landscape of multi-cloud computing. Summary of the invention:

[0010] The present invention provides a novel, intelligent system that optimizes both the cost and carbon footprint of workload deployments in multi-cloud computing environments. By leveraging advanced machine learning techniques, integrating real-time data, and multi-objective optimization strategies, the system enables organizations to make informed and dynamic decisions about where and how to operate their cloud-based applications and services. Unlike conventional systems that focus exclusively on cost or performance, this invention introduces a dual model that integrates environmental impact as a central decision criterion.

[0011] At the heart of the invention is a predictive modeling engine that uses supervised reinforcement learning algorithms to estimate future costs and carbon emissions associated with various deployment strategies. The engine is trained with a wide variety of data, including historical cloud usage patterns, pricing structures from multiple providers, regional energy grid data, and real-time carbon intensity metrics. By incorporating this diverse data, the model is able to predict the impact of workload placement across different geographic regions, providers, and timeframes.

[0012] The invention includes a data aggregation module responsible for interfacing with APIs from cloud providers, carbon tracking services, and internal telemetry systems. This module continuously collects and updates data on compute and storage prices, regional energy consumption, emission factors, and workload behavior. This maintains an up-to-date contextual environment that reflects the current state of both market conditions and environmental drivers.

[0013] Once the data has been collected and modeled, the decision-making process is performed by the system's optimization engine, which applies a multi-objective optimization algorithm. This algorithm evaluates potential deployment strategies based on weighted objectives defined by the user—typically cost versus carbon emissions—while ensuring that operational constraints such as latency, availability, and compliance are met. The optimization process creates a deployment policy that strikes a balance between economic efficiency and environmental responsibility, according to the organization's objectives or regulatory requirements.

[0014] After determining the optimal deployment plan, the workload orchestration module executes the necessary actions to assign or migrate workloads to the selected cloud providers. This is done seamlessly through orchestration tools, APIs, and automation frameworks, ensuring minimal disruption to operations. The system can dynamically reroute or reschedule workloads based on changing price or carbon data, allowing it to respond to real-world events such as fluctuations in the power grid or changes in the spot market.

[0015] To continuously improve the effectiveness of its predictions and recommendations, the invention integrates a feedback loop mechanism. This component monitors the actual costs and carbon impacts of each deployment decision and uses this information to retrain and refine the machine learning models. Over time, the system becomes more accurate, efficient, and better tailored to the user's unique operating environments and sustainability goals.

[0016] This invention is particularly advantageous in enterprise scenarios where workloads are distributed across multiple cloud providers, each with its own pricing tiers, service-level policies, and energy procurement strategies. It enables organizations to adapt to emerging carbon disclosure requirements, optimize IT spending, and proactively manage their environmental impact without sacrificing performance or availability. Furthermore, the system's adaptability allows it to be configured for different industry contexts, such as healthcare, finance, and e-commerce, each of which may have unique compliance, latency, or regional requirements.

[0017] Unlike existing tools that offer static reporting or fragmented optimization, this invention provides a unified, intelligent, and proactive solution for managing cloud resources. The integration of machine learning with real-time carbon data and cost optimization represents a significant technological advancement. It transforms workload placement from a rule-based, reactive process into a dynamic, learning-driven strategy aligned with modern economic and environmental imperatives.

[0018] In summary, the invention redefines companies' approach to cloud resource management. By simultaneously optimizing costs and carbon footprint using intelligent machine learning algorithms, it offers a path to more sustainable, financially savvy, and technologically advanced cloud computing practices. Short description of the drawing Fig. shows a block diagram of the system according to the invention. Detailed description of the invention

[0019] The present invention relates to a novel system and method that leverages machine learning algorithms to optimize both the costs and carbon footprint associated with the deployment and operation of workloads in multi-cloud computing environments. As organizations increasingly distribute their computing loads across multiple public and private cloud platforms to benefit from cost variability, geographic availability, and performance characteristics, the need for a unified, intelligent optimization framework becomes critical. The proposed system addresses this need by integrating a variety of data inputs, applying sophisticated learning models, and dynamically orchestrating workload deployment to minimize both operational costs and environmental impact.

[0020] The system architecture is modular and consists of four main components: (1) the data aggregation and monitoring layer, (2) the machine learning and predictive modeling engine, (3) the multi-objective optimization engine, and (4) the workload orchestration and execution layer. These components work together in a coordinated manner to enable real-time decision-making and continuous adaptation to changing cost, performance, and carbon parameters.

[0021] The data aggregation and monitoring layer serves as the fundamental data collection framework for the entire system. It is designed to collect, normalize, and store diverse datasets essential for predictive modeling and decision-making. The system integrates APIs and telemetry data from various cloud service providers, including but not limited to AWS, Azure, GCP, and IBM Cloud. It extracts current and historical data on resource usage (such as CPU, memory, and bandwidth), billing rates (on-demand, reserved, and spot), geographic availability zones, and service tier offerings.In parallel, it interacts with external carbon tracking and energy network information services that provide location-based, time-varying data on electricity consumption, energy source breakdown (coal, gas, renewables) and associated carbon intensity metrics in kilograms of CO2 equivalent per kilowatt-hour (kgCO2e / kWh).

[0022] This layer also captures internal, workload-specific metadata such as task priority, task duration, compliance constraints, latency sensitivity, and regional restrictions. It also captures performance metrics such as job success rates, SLA violations, and system anomalies. By storing all data in a consistent format within a time-series database or distributed data lake, the system ensures fast access for downstream machine learning components. Importantly, this layer is equipped with a fault-tolerant and scalable infrastructure to handle varying data loads and ensure high availability.

[0023] The machine learning and predictive modeling engine is central to the invention's intelligent optimization capabilities. This component uses a combination of supervised learning and reinforcement learning models to estimate the future cost and carbon footprint impacts of alternative workload deployment strategies. The supervised learning models are trained on historical workload deployment data enriched with contextual information such as resource costs at the time, energy grid emissions, and actual post-deployment metrics (costs incurred, energy consumed, carbon emitted). The models learn complex relationships between the input features and can make forward-looking predictions such as: "If a particular workload is deployed in region X with provider Y at time T, what are the expected costs and environmental impacts?"

[0024] These supervised models include regression algorithms such as gradient boosting, random forest regression, and deep neural networks capable of modeling high-dimensional data. Furthermore, the engine includes classification models to categorize workloads based on behavior, risk level, and resource severity, which is useful for determining their optimization priorities.

[0025] The reinforcement learning (RL) component, on the other hand, plays a crucial role in continuously adapting deployment policies through trial-and-feedback mechanisms. The system represents the cloud environment as a state space, where each state captures the current configuration of cloud providers, resource availability, carbon intensity, and the workload queue. The RL agent interacts with this environment by making decisions (deployment or migration decisions), which then result in rewards calculated as weighted functions of cost savings and CO2 emission reductions. Over time, the RL policy is refined to favor actions that achieve a better trade-off between the objectives. The RL framework can use Q-learning, deep Q-networks (DQN), or policy gradient methods, depending on the complexity of the environment.

[0026] The next key component is the Multi-Objective Optimization Engine, which transforms model predictions into actionable decisions. This module accepts optimization objectives specified by users or predefined through policy templates. Users can choose to minimize costs, minimize carbon footprint, or optimize a composite metric that combines both using user-defined weights. For example, a user can define an objective function such as: minimize (0.7 * normalized_cost + 0.3 * normalized_carbon_emission), where the weights reflect business priorities or sustainability targets. Constraints can also be defined, such as compliance with data protection laws, minimum availability requirements, or region-specific downtime.

[0027] The optimization problem is solved using advanced mathematical programming techniques such as linear programming, nonlinear optimization, constraint satisfaction, and evolutionary algorithms such as genetic algorithms or NSGA-II (Non-dominated Sorting Genetic Algorithm II). These methods enable the engine to efficiently explore the solution space and identify near-optimal deployment strategies in real time. The optimization engine returns a ranked list of deployment plans, each annotated with its expected cost, carbon footprint, and risk level.

[0028] After the decision phase, the workload orchestration and execution layer implements the chosen plan by interacting with cloud APIs and infrastructure-as-code platforms such as Terraform, Ansible, or Kubernetes. This layer translates high-level deployment instructions into vendor-specific API calls for provisioning resources, migrating workloads, or scheduling containers. It includes security checks to avoid disrupting active sessions or SLA violations during transitions. Furthermore, this layer supports rollbacks and version control in case of deployment errors or policy violations.

[0029] Critically, the system supports live migration of containerized workloads using technologies such as Kubernetes with CRIU (Checkpoint / Restore in Userspace) or hypervisor-assisted virtual machine migration. If real-time carbon or cost metrics indicate that a different region or provider is now more optimal, and if latency or data persistence requirements allow, the system can trigger live or scheduled migrations. Migration decisions are evaluated for risk and benefit by ML models before execution.

[0030] A key component of the invention is the feedback loop and continuous learning mechanism. After each deployment or migration, the system monitors the actual results in terms of incurred costs and carbon emissions data and compares them with the predicted values. These results are logged into the central dataset and regularly used to retrain the machine learning models, enabling continuous improvement in prediction accuracy and policy quality. Anomalies such as unexpected cost spikes or energy grid surges are flagged and incorporated into the model learning through adversarial training or anomaly detection layers.

[0031] Security and privacy considerations are integral to the design. The system anonymizes sensitive customer data and enforces role-based access controls. It ensures that compliance mandates such as HIPAA, GDPR, and ISO / IEC 27001 are not violated during data transfer or workload migration. A policy compliance module validates deployment decisions against these legal and regulatory frameworks before execution. From a user experience perspective, the invention includes a dashboard and a policy configuration interface. The dashboard provides visualizations of historical and projected cost savings, carbon savings, deployment distribution, and model confidence scores. Users can simulate "what-if" scenarios to explore the impact of different weightings in the optimization function.The policy interface allows administrators to set governance rules, override automated decisions, and review decision logs.

[0032] The system can be deployed as a SaaS platform or integrated into an organization's existing cloud management suite. It supports plug-ins and adapters for custom data sources or private cloud environments and is built on a modular microservices architecture to ensure scalability and fault tolerance. It also supports versioned APIs to enable future enhancements without compromising backward compatibility.

[0033] In summary, the invention introduces a machine learning-based, data-driven framework that transforms the way organizations make decisions about deploying workloads in multi-cloud environments. By unifying cost and sustainability targets, applying intelligent prediction and optimization techniques, and enabling autonomous execution with feedback learning, the system not only reduces financial expenditures but also significantly contributes to companies' carbon reduction goals. It represents a significant advance in cloud computing by balancing technological efficiency with environmental responsibility. List of reference symbols 100 machine learning-based optimization system 101 Data aggregation layer 102 Machine Learning Engine 103 Multi-lens optimization engine 104 Workload Orchestration Layer

Claims

[1] A machine learning-based system for optimizing costs and carbon footprint in multi-cloud computing environments, consisting of: a data aggregation layer (101) for collecting real-time data on costs, resource usage and carbon emissions from multiple cloud service providers; a machine learning engine (102) to predict future costs and Carbon emissions based on historical data and current cloud service parameters; a multi-objective optimization engine (103) that selects deployment strategies to minimize both costs and carbon emissions according to predefined constraints; and a workload orchestration layer (104) for executing the optimized deployment plan. [2] The system of claim 1, wherein the machine learning engine uses a combination of supervised learning models for cost and carbon prediction and reinforcement learning for dynamic optimization in response to changing cloud conditions. [3] The system of claim 1, wherein the data aggregation layer integrates APIs from cloud service providers and third-party environmental monitoring services to collect real-time data on energy grid emissions and cloud service resource pricing. [4] The system of claim 1, wherein the multi-objective optimization engine enables user-defined weightings to minimize costs and reduce carbon footprint based on the organization's priorities. [5] The system of claim 1, wherein the workload orchestration layer interacts with infrastructure-as-code platforms such as Terraform or Kubernetes to automate the provisioning, migration, and scaling of cloud resources based on the optimized deployment plan. [6] The system of claim 1, further comprising a continuous feedback loop for refining machine learning models based on actual deployment results, thereby enabling ongoing adaptation to changing cloud service pricing and carbon emission factors.

Images