Dynamic, topology-aware load balancing in a distributed ML inference system

Abstract

A dynamic, topology-aware load balancing in a distributed ML inference system (100), consisting of: a topology and monitoring module configured to detect, map, and dynamically update the network topology by capturing real-time latency, bandwidth, and connectivity metrics across distributed nodes; a module for capturing node performance, configured to evaluate and record the computing power, hardware configuration, accelerator availability, and storage capacity of each node; a workload characterization module configured to analyze incoming inference requests with respect to computational complexity, latency sensitivity, memory requirements, and model type; a dynamic load balancing engine configured to assign inference tasks to the optimal nodes based on topology data, node capabilities, and workload characteristics; an adaptive feedback and optimization module configured to capture performance metrics for task execution and refine task allocation strategies based on historical results; a fault tolerance and recovery module configured to detect errors, redirect tasks, and maintain uninterrupted service; and an orchestration and integration interface module configured to provide APIs and orchestration hooks for seamless deployment in heterogeneous computing environments; The system dynamically adjusts the load distribution to respond to changes in network conditions, fluctuations in resource availability, and workload requirements to improve efficiency, minimize latency, and maximize throughput in distributed ML inference environments.

Description

[0001] The present invention relates to the field of distributed computing and artificial intelligence, in particular the optimization of inference processes in machine learning. It specifically addresses dynamic, topology-aware load balancing strategies for heterogeneous and geographically distributed computing environments. The invention enables efficient resource utilization, reduced latency, and improved throughput in distributed machine learning inference systems.

[0002] In modern artificial intelligence applications, machine learning (ML) inference tasks are increasingly deployed in distributed computing environments to handle the real-time processing of large datasets. However, traditional load balancing techniques often fail to consider the underlying network topology, node heterogeneity, and dynamic changes in resource availability. This leads to suboptimal task distribution, increased latency, and inefficient use of computing resources, particularly in geographically distributed and cloud-edge hybrid environments.

[0003] Traditional load balancing mechanisms typically rely on static or uniform distribution policies, assuming equal performance capacity and network conditions for all nodes. In reality, factors such as varying hardware capabilities, fluctuating bandwidth, node failures, and data locality significantly impact inference efficiency. Without topology awareness, these systems can overload certain nodes while others remain underutilized, resulting in performance bottlenecks and inconsistent response times for end users.

[0004] The present invention addresses these challenges by introducing a dynamic, topology-aware load balancing framework that continuously monitors network conditions, node performance metrics, and workload characteristics. By intelligently allocating inference requests to the most suitable nodes in real time, the invention reduces communication overhead, maximizes parallelism, and improves the overall throughput of distributed machine learning (ML) inference systems. This problem-solving approach not only improves system reliability but also ensures scalable, energy-efficient AI inference across various deployment scenarios.

[0005] One objective of the present disclosure is to provide a dynamic load balancing system that optimizes machine learning inference tasks by taking into account the real-time network topology, thereby reducing latency and improving response times.

[0006] Another objective of the present disclosure is to improve resource utilization by profiling the computing capacities of distributed nodes and thus ensuring efficient task distribution.

[0007] Another objective of the present disclosure is to analyze the characteristics of the workload in order to better assign inference requests to the appropriate nodes and thus improve the overall throughput of the system.

[0008] Another objective of the present disclosure is to enable adaptive and real-time load balancing that dynamically responds to changing network conditions and resource availability.

[0009] Another objective of the present disclosure is to integrate feedback-driven optimization that continuously improves task allocation strategies based on historical performance data.

[0010] Another objective of the present disclosure is to ensure high system reliability and fault tolerance by promptly detecting errors and redirecting tasks to error-free nodes.

[0011] Another objective of the present disclosure is to enable seamless integration into existing AI infrastructures through standardized APLs and orchestration interfaces.

[0012] Another objective of the present disclosure is to support deployment in heterogeneous environments, including cloud, edge and on-premises systems, thereby enabling scalable and resilient distributed machine learning inference.

[0013] Further objectives and benefits of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0014] The present invention relates to a dynamic, topology-aware load balancing system designed to optimize inference tasks in machine learning in distributed computing environments.

[0015] Another embodiment of the present invention is the inclusion of a topology and monitoring module that continuously maps and updates the network topology in real time.

[0016] Another embodiment of the present invention is the integration of a node capability profiling module that evaluates and records the computing resources and hardware capabilities of each node.

[0017] Another embodiment of the present invention is the implementation of a workload characterization module that analyzes incoming inference tasks based on their complexity, latency requirements, and resource needs.

[0018] Another embodiment of the present invention is the provision of a dynamic load balancing engine that intelligently assigns tasks to the nodes taking into account the network topology, node capabilities and workload characteristics.

[0019] Another embodiment of the present invention comprises an adaptive feedback and optimization module that refines the task allocation strategies based on historical performance data.

[0020] Another embodiment of the present invention is the integration of a fault tolerance and recovery module that detects faults and redirects tasks to maintain uninterrupted operation.

[0021] Another embodiment of the present invention is the orchestration and integration interface module, which enables seamless deployment and integration with various AI infrastructures in cloud, edge and local environments.

[0022] The present invention relates to a dynamic topology-aware load balancing system in a distributed machine learning inference system (100) that provides an intelligent framework for optimizing machine learning inference across heterogeneous and geographically distributed compute nodes. It comprises a topology and monitoring module for mapping network conditions, a node capacity profiling module for evaluating compute resources, and a workload characterization module for analyzing incoming inference requests. A dynamic load balancing engine assigns tasks to the most suitable nodes based on topology, capabilities, and workload type, while an adaptive feedback and optimization module refines strategies based on performance data.The fault tolerance and recovery module ensures high availability through rapid fault detection and task redirection, while the orchestration and integration interface module enables seamless deployment across cloud, edge, and on-premises environments. Together, these modules improve efficiency, minimize latency, and enhance throughput in distributed AI inference systems. Topology and monitoring module

[0023] This module is responsible for the continuous detection and mapping of the underlying network topology across the entire distributed machine learning inference system. It gathers real-time information on node locations, connections between nodes, available bandwidth, and latency metrics. The module uses active probing techniques and passive telemetry data acquisition to create a dynamic topology graph that reflects the current state of the network. This graph forms the basis for all load balancing decisions and ensures that task assignment is topology-aware and optimized for minimal communication overhead. Module for recording node power

[0024] The node performance monitoring module assesses and logs the compute power, hardware configuration, accelerator availability (e.g., GPUs or TPUs), memory capacity, and energy efficiency of each node in the system. It performs continuous benchmarks to capture performance fluctuations over time, taking into account workload interference and resource conflicts. This profiling ensures that machine learning inference requests are routed to the nodes best suited to the specific model requirements, data sizes, and latency conditions, thus preventing performance degradation due to overloading underperforming nodes. Module for characterizing workload

[0025] This module analyzes incoming inference queries to determine their computational complexity, model type, memory requirements, and data dependencies. It categorizes workloads into profiles such as latency-sensitive, throughput-oriented, or batch processing tasks. By integrating workload characterization with topology and node profiles, the system can make intelligent, context-aware load balancing decisions. This allows the system to assign the right task to the right node, taking into account both network positioning and node capacities, thereby ensuring efficiency and responsiveness. Dynamic load balancing engine

[0026] The dynamic load balancing engine is the decision-making center of the invention. It uses the topology graph, node profiles, and workload characteristics to assign inference tasks in real time. The engine employs adaptive scheduling algorithms that consider network hops, congestion levels, and processing capacity to minimize end-to-end latency. It can also dynamically reassign workloads if network conditions or node availability change during execution, thus ensuring fault tolerance and continuous performance optimization in fluctuating environments. Adaptive feedback and optimization module

[0027] This module collects execution feedback after each inference operation, including task completion times, resource utilization levels, and network performance metrics. It applies machine learning-based optimization techniques to refine future load balancing strategies based on historical results. By learning from past performance, the module continuously improves task-to-resource allocation and adapts to changing workloads, network topologies, and hardware upgrades without requiring manual reconfiguration. Fault tolerance and recovery module

[0028] To ensure reliability in distributed environments, this module implements proactive fault detection, redundancy management, and rapid recovery strategies. It monitors for node failures, compromised connections, or performance anomalies and automatically redistributes tasks to healthy nodes with minimal disruption. The module also manages replication strategies for critical inference results, ensuring that high-priority requests are not lost due to network or node outages. This fault tolerance mechanism is essential for maintaining service continuity in production environments. Orchestration and Integration Interface Module

[0029] The orchestration and integration interface module enables seamless deployment and management of the invention across diverse infrastructures, including cloud, edge, and on-premises environments. It provides APIs, container orchestration hooks, and integration points for popular machine learning frameworks and inference engines. This module ensures that the topology-aware load balancing system can operate as a plug-and-play component within existing AI pipelines without requiring extensive code redesigns or architectural changes, thereby accelerating deployment and operational readiness.

[0030] The invention is explained again below with reference to the figure. This shows: Fig. : a dynamic, topology-aware load balancing in distributed ML inference systems.

[0031] Fig.Figure 100 illustrates dynamic topology-aware load balancing in a distributed machine learning inference system via a coordinated workflow of seven interconnected modules. The process begins with the topology and monitoring module, which maps the network structure and captures real-time latency and bandwidth metrics. Simultaneously, the node capacity capture module performs benchmarks and records the compute power, memory, and accelerator availability of each node. Incoming inference requests are analyzed by the workload characterization module to determine complexity, resource requirements, and latency sensitivity.Based on this information, the dynamic load balancing engine intelligently assigns tasks to the most suitable nodes, taking into account network topology and compute capacity. The Adaptive Feedback and Optimization Module continuously refines distribution strategies by learning from execution performance metrics, enabling real-time adjustments for increased efficiency. To ensure reliability, the Fault Tolerance and Recovery Module proactively detects failures or performance degradation and redirects tasks to healthy nodes with minimal disruption. Finally, the Orchestration and Integration Interface Module provides APIs and container orchestration hooks for seamless deployment in heterogeneous environments, ensuring smooth integration of the system into existing AI pipelines while maintaining optimal, topology-aware load balancing.

Claims

[1] A dynamic, topology-aware load balancing in a distributed ML inference system (100), consisting of: a topology and monitoring module configured to detect, map, and dynamically update the network topology by capturing real-time latency, bandwidth, and connectivity metrics across distributed nodes; a module for capturing node performance, configured to evaluate and record the computing power, hardware configuration, accelerator availability, and storage capacity of each node; a workload characterization module configured to analyze incoming inference requests with respect to computational complexity, latency sensitivity, memory requirements, and model type; a dynamic load balancing engine configured to assign inference tasks to the optimal nodes based on topology data, node capabilities, and workload characteristics; an adaptive feedback and optimization module configured to capture performance metrics for task execution and refine task allocation strategies based on historical results; a fault tolerance and recovery module configured to detect errors, redirect tasks, and maintain uninterrupted service; and an orchestration and integration interface module configured to provide APIs and orchestration hooks for seamless deployment in heterogeneous computing environments; The system dynamically adjusts the load distribution to respond to changes in network conditions, fluctuations in resource availability, and workload requirements to improve efficiency, minimize latency, and maximize throughput in distributed ML inference environments. [2] System (100) according to claim 1, wherein the topology and monitoring module uses both active queries and passive telemetry to create and update the network topology graph. [3] System (100) according to claim 1, wherein the node capability profiling module performs regular benchmarks to account for performance fluctuations due to workload interference and resource conflicts. [4] System (100) according to claim 1, wherein the workload characterization module assigns priority levels to inference queries based on latency sensitivity and data dependencies. [5] System (100) according to claim 1, wherein the dynamic load balancing engine uses an adaptive planning algorithm that minimizes communication jumps and overloads to reduce end-to-end latency. [6] System (100) according to claim 1, wherein the adaptive feedback and optimization module uses a machine learning model to predict optimal task-node assignments based on historical performance metrics. [7] System (100) according to claim 1, wherein the fault tolerance and recovery module manages redundant task replicas for high-priority inference queries to prevent data loss. [8] System (100) according to claim 1, wherein the orchestration and integration interface module supports containerized deployments through integration with Kubernetes or equivalent orchestration frameworks. [9] System (100) according to claim 1, wherein the dynamic load balancing engine is further configured to real-time reassign ongoing inference tasks when a performance degradation is detected in assigned nodes. [10] System (100) according to claim 1, wherein the system is deployed in heterogeneous computing environments, including cloud servers, edge devices and local nodes, to enable distributed and collaborative ML inference.

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