System for cloud-native, AI-driven predictive analytics with elastic resource orchestration and real-time optimization

Abstract

A system (100) for cloud-native, AI-driven predictive analytics with elastic resource orchestration and real-time optimization, wherein the system (100) comprises: a cloud-native runtime layer (1) configured to provide and run analytics microservices as containerized workloads; a data acquisition and normalization layer (2) configured to ingest batch data and streaming data and convert the ingested data into a canonical, feature-enabled format; a predictive analytics layer (3) configured to generate one or more predictive outputs using at least one machine learning model; an elastic resource orchestration layer (4) configured to allocate, scale, and release compute resources for the cloud-native runtime layer (1) based on at least workload demand and policy constraints; a real-time optimization layer (5) configured to continuously optimize at least one of (i) the resource allocation performed by the elastic resource orchestration layer (4) and (ii) the inference configuration of the predictive analysis layer (3) to achieve one or more runtime goals; a telemetry and feedback layer (6) configured to collect runtime signals connected to the predictive analytics layer (3) and the cloud-native runtime layer (1), and to generate feedback signals for control; and a governance and security layer (7) configured to enforce access control, data protection and auditability for operations performed within the system (100).

Description

INVENTION AREA

[0001] The present invention relates to cloud computing and artificial intelligence (Cl). In particular, the invention relates to a cloud-native predictive analytics system that performs elastic resource orchestration and real-time optimization for the operation of predictive models for streaming and batch data with closed-loop telemetry feedback and governance. BACKGROUND OF THE INVENTION

[0002] The subject matter discussed in the "Background" section should not be considered prior art solely because it is mentioned in that section. Likewise, a problem mentioned in the "Background" section or related to the subject matter of the "Background" section should not be considered prior art. The subject matter in the "Background" section merely presents various approaches, which could themselves also be inventions.

[0003] The use of predictive analytics has increased rapidly in industries such as manufacturing, finance, retail, healthcare, transportation, energy, and telecommunications. Companies use predictive models to forecast demand, anticipate equipment failures, detect fraud, identify anomalies, optimize inventory, personalize services, and automate operational decisions. In many use cases, such predictive analytics must operate continuously and with low latency, as the underlying business processes—such as transaction approvals, machine monitoring, dynamic pricing, routing, or cyber threat detection—depend on near real-time predictions.

[0004] In traditional practice, predictive analytics solutions are implemented as model-based services that process batch datasets (e.g., daily extracts) and / or streaming event data (e.g., IoT telemetry, clickstreams, financial transactions). While these approaches can generate predictions, they face significant challenges when migrated to dynamic cloud environments where demand fluctuates and system behavior is affected by multi-tenant sharing, distributed latency, and cost constraints. A common limitation is that predictive inference workloads are not balanced. Instead, workload demand fluctuates due to user spikes, time-of-day patterns, seasonal business cycles, device telemetry bursts, marketing campaigns, and external events.Under these conditions, static provisioning of computing infrastructure leads to persistent inefficiencies: If resources are provisioned for peak loads, the system remains oversized during normal usage times, resulting in unnecessary costs. If resources are provisioned for average loads, the system is undersized during peak loads, leading to increased latency, queueing, lost requests, and degraded prediction responsiveness.

[0005] Traditional autoscaling mechanisms attempt to address this issue, but typical autoscaling is largely reactive and based on threshold triggers such as CPU utilization or the number of requests. Such reactive scaling often suffers from delayed responses, as scaling actions only occur after performance has already been impacted. Furthermore, threshold-based scaling can lead to fluctuations or "thrashing," where the system is repeatedly ramped up and down due to short-term fluctuations rather than stable changes in demand. This instability is particularly problematic for predictive analytics services, as inference throughput and latency must remain stable to meet service-level targets, and frequent scaling events can result in cold starts, cache errors, or temporary unavailability of model serving instances.

[0006] Another challenge with traditional predictive analytics implementations is the fragmentation of the operational pipeline. In many environments, data ingestion, schema management, feature generation, model storage, deployment, monitoring, and optimization are implemented with separate and loosely integrated components. Such fragmentation makes it difficult to achieve consistent end-to-end performance and reliability. For example, schema changes in upstream data sources can silently disrupt feature generation pipelines, leading to incorrect or incomplete model inputs.Similarly, a change in the feature distribution—caused by altered user behavior, sensor calibration changes, or market shifts—can gradually impair the model's effectiveness, but conventional systems may not immediately detect such deterioration due to the lack of unified monitoring and feedback. As a result, predictions may become less accurate or less reliable while the system continues to operate, adversely impacting downstream decisions.

[0007] Furthermore, modern enterprises often operate predictive analytics platforms in multi-tenant or multi-application environments where multiple departments, customers, or services share cloud resources. In such environments, governance and security requirements are critical. Traditional systems often lack a unified mechanism to ensure that dataset access, model deployment, and scaling actions are fully auditable and compliant with corporate policies. Without robust governance, unauthorized data access, accidental deployment of incorrect model versions, and uncontrolled scaling decisions can occur, potentially leading to compliance risks, operational errors, or excessive spending.Furthermore, tenant isolation in traditional implementations is often incomplete, so that "noisy neighbors" can consume a disproportionate amount of resources and negatively impact the quality of other tenants' prediction services.

[0008] Another limitation is that most traditional predictive analytics systems treat model inference configuration and infrastructure resource allocation as separate aspects. For example, model-serving parameters such as batch size, concurrency, model version selection, and request routing are typically configured manually, while resource allocation is adjusted independently by automatic scaling rules. This separation prevents the system from achieving globally optimal performance. In practice, inference-level tuning and infrastructure-level scaling must be coordinated, as changes in concurrency or batch size directly impact latency and throughput, which in turn affects resource consumption and costs.The lack of a coordinated closed-loop approach leads to suboptimal operation, either through wasted resources to achieve performance targets or through reduced performance to cut costs.

[0009] Therefore, there is a need for an improved predictive analytics platform that is cloud-native from the ground up, supports both streaming and batch ingestion, and offers a closed-loop feedback mechanism for runtime monitoring, elastic resource orchestration, and real-time optimization. Such a system should proactively anticipate workload changes, stabilize performance during peak times, and dynamically optimize both compute allocation and inference configuration, considering multiple objectives such as cost, latency, throughput, and reliability. Furthermore, such a system should include integrated governance and security measures to ensure role-based access control, auditable operations, and tenant isolation, thereby enabling secure and compliant deployments at enterprise scale.

[0010] Accordingly, the present invention overcomes the aforementioned limitations by providing a system for cloud-native, AI-driven predictive analytics with elastic resource orchestration and real-time optimization, integrating data acquisition, model provisioning, telemetry feedback, proactive orchestration, and optimization in a unified, controlled architecture suitable for dynamic cloud environments.

[0011] The use of any examples or illustrative phrases (e.g., "as") relating to specific embodiments serves only to better illustrate the invention and does not constitute a limitation of the otherwise claimed scope of the invention. No wording in the description shall be construed as referring to an unclaimed element that is essential for carrying out the invention.

[0012] The information disclosed above in this "Background" section is provided solely for a better understanding of the background of the invention and may therefore contain information that is not part of the prior art already known to a person skilled in the art in this country. SUMMARY

[0013] Before describing the systems presented here, it should be noted that this application is not limited to the specific systems and methods described, as there may be several possible embodiments not expressly described in this disclosure. It should also be noted that the terminology used in the description serves only to describe the specific versions or embodiments and is not intended to limit the scope of this application.

[0014] In one embodiment, the present invention discloses a system (100) for cloud-native, AI-driven predictive analytics with elastic resource orchestration and real-time optimization. The system (100) comprises: a cloud-native runtime layer (1) for containerized analytics microservices, a data acquisition and normalization layer (2) for batch and streaming acquisition, a predictive analytics layer (3) for generating predictive outputs, an elastic resource orchestration layer (4) for allocating and scaling computational resources, a real-time optimization layer (5) for continuously adjusting the inference configuration and resource allocation, a telemetry and feedback layer (6) for collecting runtime signals and generating feedback signals for control, and a governance and security layer (7) for access control, data protection, and auditability.

[0015] In an implementation, the cloud-native runtime layer (1) includes a container orchestration controller (8) that schedules workloads across nodes and scales services. The data ingestion and normalization layer (2) may include a streaming message backbone (9) and a schema control component (10) for validating and quarantining non-compliant data. The predictive analytics layer (3) may include a feature store (11) and a model registry (12) to manage time-aligned features and model versions. The telemetry and feedback layer (6) may include a drift detection unit (13) to trigger corrective actions. The orchestration layer (4) may include a workload forecaster (14) and a policy engine (15).The optimization layer (5) can include an optimization controller (16) for updating adjustable parameters such as replica count, instance type, concurrency and batch size to improve multi-target evaluation. BRIEF DESCRIPTION OF THE DRAWING

[0016] To clarify various aspects of some embodiments of the present invention, a more detailed description of the invention is given with reference to specific embodiments shown in the accompanying drawing. It is understood that this drawing represents only illustrative embodiments of the invention and is therefore not to be considered a limitation of its scope. The invention is described and explained with additional accuracy and detail using the accompanying drawing.

[0017] To make the advantages of the present invention easily understandable, a detailed description of the invention is given below in conjunction with the accompanying drawing, which, however, should not be regarded as limiting the scope of the invention to the accompanying drawing, in which: Fig. Figure 1 shows a block diagram representation of the system (100) for cloud-native, AI-driven predictive analytics with elastic resource orchestration and real-time optimization. DETAILED DESCRIPTION

[0018] The present invention relates to a system (100) for cloud-native, AI-driven predictive analytics with elastic resource orchestration and real-time optimization.

[0019] Fig. shows a detailed block diagram representation of the system (100) for cloud-native, AI-driven predictive analytics with elastic resource orchestration and real-time optimization.

[0020] Although the implementations of the invention have been described in language relating to structural features and / or methods, it should be noted that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations of the invention.

[0021] For the purposes of the present invention, the term “System (100)” refers to an integrated cloud-native platform that runs predictive analytics workloads as distributed services and also performs closed-loop control for elastic resource orchestration and real-time optimization. The term “cloud-native” means that the functional components of System (100) are provided as containerized and / or service-based workloads that are scalable, fault-tolerant, serviceable, and isolable within a cloud environment.

[0022] The term "runtime signals" includes, but is not limited to, infrastructure utilization metrics, service-level metrics, application logs, request rate patterns, functional statistics, and inference quality proxies. The term "runtime objectives" encompasses one or more operational goals such as inference latency, throughput, reliability, and infrastructure costs. The term "policy constraints" includes rules and limits such as budget ceilings, latency ceilings, availability requirements, tenant quotas, compliance restrictions, and geographic location restrictions. The term "corrective actions" includes, but are not limited to, scaling decisions, failover measures, throttling, routing changes, inference configuration changes, and data processing measures to maintain stable operations.

[0023] The embodiments described here serve for illustration and are not limiting. Any functionally equivalent arrangement of modules, interfaces, and control processes that achieves the same technical effect can be implemented without deviation from the scope of the claims.

[0024] According to the invention, the system (100) is configured as a multi-layered architecture comprising: a cloud-native runtime layer (1), a data acquisition and normalization layer (2), a predictive analytics layer (3), an elastic resource orchestration layer (4), a real-time optimization layer (5), a telemetry and feedback layer (6), and a governance and security layer (7).

[0025] In the system (100), data flows from the input side through the data acquisition and normalization layer (2) to the predictive analytics layer (3), while operational signals are acquired via the telemetry and feedback layer (6) and used to drive adaptive decisions through the elastic resource orchestration layer (4) and the real-time optimization layer (5). The governance and security layer (7) enforces access control and auditability for actions performed throughout the system (100), including actions related to data, models, and resource orchestration.

[0026] The cloud-native runtime layer (1) is configured to provide and execute analytics microservices as containerized workloads. In one implementation, the runtime layer (1) hosts services for ingestion processing, feature processing, inference provisioning, telemetry processing, optimization execution, and orchestration execution as separable workloads. Such decomposition allows each workload type to scale independently and improves fault isolation, as the failure of one workload type does not necessarily disrupt other workloads within the system (100).

[0027] The cloud-native runtime layer (1) is also configured to support service routing, service health monitoring, restart and recovery, and controlled update deployment. The runtime layer (1) performs scaling and placement actions according to the instructions of the elastic resource orchestration layer (4) under the constraints imposed by the governance and security layer (7). In certain deployments, the runtime layer (1) can distribute workload replicas across multiple zones () to improve availability and reduce the risk of correlated failures.

[0028] The data acquisition and normalization layer (2) is configured to ingest both batch and streaming data from multiple sources and convert the ingested data into a canonical, feature-enabled format. In one embodiment, layer (2) receives real-time events such as telemetry, transactions, logs, or user interactions, and also receives batch data sets such as periodic extracts and historical data sets.

[0029] The data acquisition and normalization layer (2) performs validation and normalization operations to ensure the consistency and reliability of downstream processing. Such operations may include schema validation, type validation, missing value handling, timestamp matching, deduplication, enrichment, and conversion to standardized feature representations. Non-conforming or suspicious inputs may be rejected or isolated to prevent the propagation of errors into feature generation and inference. By generating canonical feature-capable outputs, layer (2) reduces feature incongruities and improves the stability and reproducibility of the predictions generated by the predictive analytics layer (3).

[0030] The predictive analytics layer (3) is configured to generate one or more predictive outputs using at least one machine learning model. Predictive outputs can include, among other things, risk assessments, anomaly assessments, forecast values, classification labels, probability estimates, or ranking outputs. The predictive analytics layer (3) can run continuously for online inference and optionally support batch assessments for offline processing and periodic evaluation.

[0031] The predictive analytics layer (3) is configured to use the canonical feature-ready representation provided by the data collection and normalization layer (2) and to apply the selected model and inference configuration to generate predictive forecasts. The predictive analytics layer (3) is further configured to provide predictive results to downstream consumers such as operational controls, alert systems, dashboards, and external enterprise applications. In one embodiment, the predictive analytics layer (3) supports model version control and controlled model updates to ensure that prediction changes are traceable and reversible under governance.

[0032] The telemetry and feedback layer (6) is configured to collect runtime signals connected to the predictive analytics layer (3) and the cloud-native runtime layer (1). The collected signals can include request rate, queue depth, latency statistics, throughput, error rate, availability indicators, resource utilization indicators, and system health signals. The telemetry and feedback layer (6) can also collect feature distribution summaries and predictive behavior indicators, which serve as proxies for inference quality.

[0033] The telemetry and feedback layer (6) is configured to generate feedback signals for the control system. Such feedback signals can include scaling or shrinkage indicators, overload indicators, stability indicators, drift or anomaly indicators, and optimization triggers. The telemetry and feedback layer (6) can compute rolling aggregates and percentiles over defined windows to reduce noise and ensure that the control actions performed by the elastic resource orchestration layer (4) and the real-time optimization layer (5) are stable, repeatable, and resistant to transient spikes.

[0034] The elastic resource orchestration layer (4) is configured to allocate, scale, and release compute resources for the cloud-native runtime layer (1) based on at least workload demand and policy constraints. In one embodiment, workload demand can be derived from telemetry signals such as request rate, backlog, latency, and utilization. Policy constraints can include budget constraints, performance constraints, tenant quotas, reliability requirements, and compliance constraints.

[0035] The elastic resource orchestration layer (4) can perform reactive orchestration by scaling resources in response to measured demand, and can also perform proactive orchestration by estimating near-future demand based on historical patterns and telemetry trends. Orchestration actions include increasing or decreasing the number of running workload instances, resizing allocated compute capacity, and adjusting placement strategies to improve availability. The elastic resource orchestration layer (4) is configured to avoid unstable fluctuations by applying stabilization logic such as hysteresis, cooling intervals, and incremental changes, thus preventing frequent scale reversals that can degrade runtime performance.

[0036] The real-time optimization layer (5) is configured to continuously optimize at least one of the following components: (i) the resource allocation performed by the elastic resource orchestration layer (4) and (ii) the inference configuration of the predictive analytics layer (3) to achieve one or more runtime goals. These runtime goals may include latency goals, throughput goals, cost ceilings, reliability goals, and resource efficiency goals.

[0037] The real-time optimization layer (5) uses telemetry feedback from the telemetry and feedback layer (6) to compute an optimization decision and apply control actions. Such control actions may include adjusting inference parameters such as batch size, parallelism, request parallelism, routing strategy, and model selection, and may also include requesting resource scaling actions from the elastic resource orchestration layer (4). In one embodiment, the real-time optimization layer (5) computes a composite goal score from multiple goals and selects tuning actions that improve the composite goal score while adhering to policy constraints enforced by the governance and security layer (7). The real-time optimization layer (5) may further include safeguards to prevent unstable behavior, such as...the limitation of the rate of change of voting and the requirement of convergence conditions before the application of subsequent adjustments.

[0038] The governance and security layer (7) is configured to enforce access control, data protection, and auditability for operations within the system (100). In one embodiment, the governance and security layer (7) enforces role-based access restrictions for access to datasets, feature definitions, model artifacts, prediction outputs, and orchestration controls. The governance and security layer (7) ensures that only authorized roles can perform sensitive actions, such as deploying a new model, changing optimization policies, or overriding scaling boundaries.

[0039] The governance and security layer (7) is further configured to maintain auditability by logging operational events such as data access events, model deployment events, reconciliation actions, and scaling actions. In multi-tenant deployments, the governance and security layer (7) enforces tenant isolation through quotas and runtime limits to prevent one tenant's workload from consuming excessive resources or impacting another tenant's performance. The governance and security layer (7) can additionally enforce encryption requirements for data in transit and at rest, and enforce credential management policies in accordance with the organization's compliance requirements.

[0040] In an exemplary operation, the data acquisition and normalization layer (2) ingests streaming and / or batch data and transforms the ingested data into canonical, feature-enabled outputs. The predictive analytics layer (3) uses the canonical outputs and generates predictive outputs using the selected model and inference configuration. Runtime signals corresponding to workload behavior and predictive behavior are captured by the telemetry and feedback layer (6), which generates feedback signals that provide insights into performance, stability, and quality trends.

[0041] Based on feedback signals, the real-time optimization layer (5) adjusts the inference configuration and / or requests changes to resource allocation to meet runtime objectives. The elastic resource orchestration layer (4) allocates and scales resources for the cloud-native runtime layer (1) according to workload demand and policy constraints. It also implements stabilization logic to prevent fluctuations. Throughout operation, the governance and security layer (7) enforces authorized access, auditability, and isolation rules for all actions performed within the system (100), enabling stable, compliant, and cost-effective predictive analytics in dynamic cloud environments.

Claims

[1] A system (100) for cloud-native, AI-driven predictive analytics with elastic resource orchestration and real-time optimization, wherein the system (100) comprises: a cloud-native runtime layer (1) configured to provide and run analytics microservices as containerized workloads; a data acquisition and normalization layer (2) configured to ingest batch data and streaming data and convert the ingested data into a canonical, feature-enabled format; a predictive analytics layer (3) configured to generate one or more predictive outputs using at least one machine learning model; an elastic resource orchestration layer (4) configured to allocate, scale, and release compute resources for the cloud-native runtime layer (1) based on at least workload demand and policy constraints; a real-time optimization layer (5) configured to continuously optimize at least one of (i) the resource allocation performed by the elastic resource orchestration layer (4) and (ii) the inference configuration of the predictive analysis layer (3) to achieve one or more runtime goals; a telemetry and feedback layer (6) configured to collect runtime signals connected to the predictive analytics layer (3) and the cloud-native runtime layer (1), and to generate feedback signals for control; and a governance and security layer (7) configured to enforce access control, data protection and auditability for operations performed within the system (100). [2] System (100) according to claim 1, wherein the cloud-native runtime layer (1) comprises a container orchestration controller (8) configured to distribute the containerized workloads across a plurality of nodes and to apply horizontal and / or vertical scaling based on declared service targets. [3] System (100) according to claim 1, wherein the data acquisition and normalization layer (2) comprises a streaming message backbone (9) and a schema control component (10), wherein the schema control component (10) is configured to validate incoming data against versioned schemas and reject or quarantine non-compliant data prior to feature generation. [4] System (100) according to claim 1, wherein the predictive analysis layer (3) comprises a feature store (11) configured to manage time-aligned features and a model register (12) configured to store model artifacts with version identifiers, and wherein the predictive analysis layer (3) is configured to perform online inference using a selected model version referenced from the model register (12). [5] System (100) according to claim 1, wherein the telemetry and feedback layer (6) comprises a drift detection unit (13) configured to detect at least one of data drift and concept drift by comparing actual feature distributions and / or prediction residuals with basic distributions, and triggers at least one corrective action when a drift threshold is reached. [6] System (100) according to claim 1, wherein the elastic resource orchestration layer (4) comprises a workload forecaster (14) configured to predict resource requirements in the near future using at least one forecasting model, and wherein the elastic resource orchestration layer (4) proactively scales compute resources based on the predicted resource requirements in the near future. [7] System (100) according to claim 1, wherein the elastic resource orchestration layer (4) further comprises a policy engine (15) configured to enforce a multi-target allocation policy that includes at least (i) a cost constraint, (ii) latency constraints and (iii) reliability constraints, and wherein the compute resources are selected from at least one of several availability zones and several cloud providers according to the multi-target allocation policy. [8] System (100) according to claim 1, wherein the real-time optimization layer (5) comprises an optimization controller (16) configured to calculate a target score using at least two runtime targets selected from inference latency, prediction accuracy proxy, infrastructure cost and throughput, and updates at least one adjustable parameter selected from model selection, batch size, concurrency, replica count and instance type to improve the target score. [9] System (100) according to claim 1, wherein the telemetry and feedback layer (6) is configured to collect distributed traces, metrics and logs from the cloud-native runtime layer (1) and generates a control signal that is used by the elastic resource orchestration layer (4) and / or the real-time optimization layer (5) to implement closed-loop stabilization during workload peaks. [10] System (100) according to claim 1, wherein the governance and security layer (7) comprises role-based access control and an audit ledger configured to immutably record at least one of the following events: data access events, model deployment events and resource allocation events, and wherein the system (100) enforces tenant isolation by applying tenant-specific quotas and namespace-level resource constraints to the containerized workloads.

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