Adaptive resource scaling system for multi-cloud data pipelines based on workflow latency

Abstract

An adaptive resource scaling system for multi-cloud data pipelines based on workflow latency, comprising: a) a workflow latency monitoring module configured to collect and track execution latency metrics of data pipeline workflows across multiple cloud environments; b) an intelligent latency analysis engine operatively coupled to the workflow latency monitoring module and configured to analyze latency trends, predict performance degradations, and identify potential SLA violations using machine learning techniques; c) an adaptive scaling decision unit configured to calculate optimal resource scaling actions based on the analyzed latency data and the criticality of the workflow; d) a multi-cloud resource orchestration layer configured to interface with different cloud provider platforms to dynamically provision, scale, and manage resources across cloud environments; (e) a workflow dependency and priority manager configured to manage relationships between workflows and allocate resources based on task urgency and execution dependencies; (f) a cost optimization and policy enforcement module configured to enforce corporate policies, control spending, and select cost-effective resource options while meeting latency targets; and (g) a feedback and learning loop configured to monitor the results of scaling actions and continuously refine the predictive models to improve system performance over time; h) where the system dynamically adapts to workload fluctuations and optimizes the execution of data pipelines by proactively managing resources based on real-time workflow latency across heterogeneous cloud infrastructures.

Description

[0001] The present invention relates to the field of cloud computing and data pipeline orchestration. In particular, it relates to an adaptive system for dynamically scaling computing resources in multi-cloud environments. The invention focuses on optimizing resource allocation based on real-time workflow latency metrics to ensure performance efficiency and cost-effectiveness.

[0002] In the evolving cloud computing landscape, enterprises increasingly rely on complex data pipelines spanning multiple cloud providers to process, transform, and analyze large volumes of data. These pipelines are often subject to unpredictable workloads, resulting in latency spikes and inefficient resource utilization. Traditional approaches to resource scaling are either static or reactive, with delayed response times, resulting in missed service level agreements (SLAs), degraded performance, and increased operational costs.

[0003] Existing solutions fail to address the heterogeneity of multi-cloud environments, where varying infrastructures, network latencies, and service limitations create bottlenecks. Static threshold-based scaling or manually configured autoscaling cannot adapt to real-time fluctuations in workflow execution. Furthermore, these systems often overlook workflow-level latency as a key indicator, instead focusing on infrastructure metrics such as CPU or memory, which do not fully represent end-to-end data processing performance.

[0004] To address these limitations, the present invention introduces an intelligent, adaptive resource scaling system that proactively monitors workflow latency in multi-cloud data pipelines. By analyzing latency patterns and dynamically adjusting resources based on predictive models, the system ensures optimal performance and responsiveness. This approach provides a scalable, cost-effective, and SLA-compliant solution that autonomously adapts to workload fluctuations, enabling resilient and efficient data operations in heterogeneous cloud environments.

[0005] An objective of the present disclosure is to enable real-time adaptive resource scaling based on actual workflow latency.

[0006] Another goal of this disclosure is to optimize performance in heterogeneous multi-cloud environments.

[0007] Another objective of this disclosure is to reduce cloud costs by enforcing intelligent scaling and policy constraints.

[0008] Another goal of this disclosure is to minimize SLA violations through predictive latency analysis.

[0009] Another goal of this disclosure is to dynamically prioritize critical workflows based on dependencies and urgency.

[0010] Another goal of this disclosure is to support seamless integration with APIs from multiple cloud providers.

[0011] Another goal of the present disclosure is to continuously improve the scaling accuracy through a feedback learning loop.

[0012] Another objective of this disclosure is to provide end-to-end visibility and control over the performance of the data pipeline

[0013] The present invention relates to an adaptive resource scaling system for multi-cloud data pipelines. It leverages real-time workflow latency metrics to dynamically manage cloud resources.

[0014] Another embodiment of the present invention is the workflow latency monitoring module, which captures execution times, delays, and throughput data. This enables granular tracking of performance at each pipeline stage. Another embodiment of the present invention is

[0015] Another embodiment of the present invention is the latency analysis engine, which applies machine learning models to predict potential SLA violations. It analyzes trends, anomalies, and historical patterns across workflows.

[0016] Another embodiment of the present invention is the Adaptive Scaling Decision Unit, which calculates the required optimal resource adjustments. It supports both vertical and horizontal scaling strategies in cloud environments.

[0017] Another embodiment of the present invention is the Multi-Cloud Orchestration Layer, which manages resource provisioning and migration and interfaces with multiple cloud providers to efficiently perform scaling operations.

[0018] Another embodiment of the present invention is the Workflow Dependency and Priority Manager, which coordinates the execution of tasks based on their importance. It dynamically adjusts scheduling for time-critical or critical workflows.

[0019] Another embodiment of the present invention is the cost optimization and policy enforcement module, which regulates the economics of resource utilization. It integrates cloud pricing data and business rules to prevent budget overruns.

[0020] The present invention relates to an adaptive resource scaling system designed for optimizing multi-cloud data pipelines based on real-time workflow latency. It comprises key modules such as a latency monitoring module, an intelligent analytics engine, and a scaling decision engine for dynamic resource management. A multi-cloud orchestration layer handles provisioning across different cloud platforms, while a workflow priority manager ensures that critical tasks are prioritized. The system also includes a cost optimization module for enforcing budget policies and a feedback loop for continuous learning. Together, these modules enable efficient, intelligent, and latency-aware resource management in complex cloud environments. Workflow Latency Monitoring Module:

[0021] This module continuously tracks and logs workflow execution latencies at various stages of the data pipeline in multi-cloud environments. It captures real-time data on task start and completion times, queue delays, and throughput metrics. By creating latency profiles per workflow and per cloud region, the module provides critical insights into performance bottlenecks and execution delays, which serve as key triggers for adaptive resource scaling decisions. Intelligent latency analysis engine:

[0022] This engine analyzes the latency data collected by the monitoring module to identify workload patterns, anomalies, and trends. It uses machine learning models to predict future latency deviations based on historical behavior and current load. The engine distinguishes between temporary latency spikes and sustained delays, allowing the system to make accurate and timely predictions about potential SLA violations and resource saturation points. Adaptive scaling decision unit:

[0023] Based on the insights of the latency analysis engine, this module formulates optimal resource scaling strategies. It calculates the necessary adjustments to compute, memory, and storage capacities required to meet latency thresholds across workflows. The module prioritizes latency-sensitive workflows and makes decisions based on workload urgency, task dependencies, and cloud-specific deployment constraints. Scaling actions include horizontal scaling (adding / removing instances) and vertical scaling (upgrading / downgrading resources). Multi-cloud resource orchestration layer:

[0024] This layer interfaces with the APIs of various cloud providers (e.g., AWS, Azure, GCP) to make scaling decisions. It abstracts the complexity of heterogeneous cloud infrastructures and manages resource provisioning, release, and migration across cloud environments. The orchestration layer ensures that resources are deployed in the lowest-latency regions, taking data localization, pricing, and network throughput into account, while maintaining cross-cloud consistency and failover capabilities. Workflow Dependency and Priority Manager:

[0025] This module tracks the dependencies and execution priorities of the various workflows within the data pipeline. It dynamically adjusts scheduling based on workflow criticality, deadlines, and input-output dependencies. In situations where resource conflicts occur, this manager ensures that high-priority or time-critical workflows are allocated optimal resources first, thereby reducing overall processing time and ensuring compliance with workflow SLAs. Cost optimization and policy enforcement module:

[0026] To balance performance and operational costs, this module continuously evaluates the cost impact of scaling decisions across different cloud providers. It applies enterprise-defined policies to limit spending, avoid overprovisioning, and ensure compliance with usage regulations. The module integrates real-time pricing data and billing APIs to select cost-effective resource options without compromising latency or throughput requirements. Feedback and learning loop:

[0027] This module closes the loop by feeding the results of scaling actions back into the latency analysis engine. It continuously learns from the effectiveness of previous scaling decisions and updates its predictive models accordingly. This feedback mechanism ensures that the system becomes increasingly intelligent and accurate over time, adapting to changing workload patterns and evolving infrastructure conditions. EXAMPLE 1: How the system works

[0028] The Adaptive Resource Scaling system first monitors workflow latencies in distributed multi-cloud data pipelines in real time using the Workflow Latency Monitoring module, which collects execution metrics and latency data at each stage. This data is fed into the Intelligent Latency Analysis Engine, where machine learning models analyze trends, detect anomalies, and predict potential SLA violations. Based on these insights, the Adaptive Scaling Decision Unit calculates the optimal resource adjustments required to meet performance thresholds, taking into account the current load, workflow urgency, and system constraints.The Multi-Cloud Resource Orchestration Layer then communicates with various cloud provider APIs to provision, scale, or migrate resources across regions, ensuring minimal latency and optimal resource distribution. At the same time, the Workflow Dependency and Priority Manager assesses task criticality and coordinates scheduling to prioritize important workflows. The Cost Optimization and Policy Enforcement module ensures that all resource actions comply with predefined cost, usage, and compliance policies and selects the most cost-effective cloud resources. Finally, the Feedback and Learning Loop monitors the results of the scaling actions, refines the predictive models, and adapts future decisions.This allows the system to continuously evolve, respond intelligently to changing workload conditions, and ensure latency-aware, cost-effective performance in heterogeneous cloud infrastructures.

Claims

[1] An adaptive resource scaling system for multi-cloud data pipelines based on workflow latency, comprising: a) a workflow latency monitoring module configured to collect and track execution latency metrics of data pipeline workflows across multiple cloud environments; b) an intelligent latency analysis engine operatively coupled to the workflow latency monitoring module and configured to analyze latency trends, predict performance degradations, and identify potential SLA violations using machine learning techniques; c) an adaptive scaling decision unit configured to calculate optimal resource scaling actions based on the analyzed latency data and the criticality of the workflow; d) a multi-cloud resource orchestration layer configured to interface with different cloud provider platforms to dynamically provision, scale, and manage resources across cloud environments; (e) a workflow dependency and priority manager configured to manage relationships between workflows and allocate resources based on task urgency and execution dependencies; (f) a cost optimization and policy enforcement module configured to enforce corporate policies, control spending, and select cost-effective resource options while meeting latency targets; and (g) a feedback and learning loop configured to monitor the results of scaling actions and continuously refine the predictive models to improve system performance over time; h) where the system dynamically adapts to workload fluctuations and optimizes the execution of data pipelines by proactively managing resources based on real-time workflow latency across heterogeneous cloud infrastructures. [2] The system of claim 1, wherein the workflow latency monitoring module measures execution start and end times, queue delays, and task-level throughput to create latency profiles per pipeline stage. [3] The system of claim 1, wherein the intelligent latency analysis engine uses regression models, anomaly detection algorithms, and historical workload data to predict latency spikes. [4] The system of claim 1, wherein the adaptive scaling decision unit performs both horizontal scaling by adding / removing instances and vertical scaling by resizing allocated resources. [5] The system of claim 1, wherein the multi-cloud resource orchestration layer selects cloud regions based on data locality, network bandwidth, and latency metrics. [6] The system of claim 1, wherein the workflow dependency and priority manager dynamically adjusts execution scheduling based on workflow priority, deadlines, and dependencies between tasks. [7] The system of claim 1, wherein the cost optimization and policy enforcement module is integrated with cloud provider billing APIs to obtain real-time pricing and enforce cost caps. [8] The system of claim 1, wherein the feedback and learning loop updates predictive models using reinforcement learning based on the effectiveness of previous scaling decisions. [9] The system of claim 1, wherein the system provides a unified dashboard for administrators to visualize latency metrics, scaling decisions, cost analysis, and policy compliance. [10] The system of claim 1, wherein the system supports integration with third-party workflow orchestrators such as Apache Airflow or Kubernetes to control pipeline execution.