A cloud-based local orchestration system that regulates microservice placement according to SLA risk score in a 6G core network.

Abstract

The invention relates to a cloud-native orchestration system that regulates microservice placement in a 6G core network based on SLA risk score. The system includes an SLA Risk Analysis Module (1), Dynamic Resource Allocation Unit (2), Microservice Dependency Mapping Service (3), Cloud-Native Orchestration Engine (4), Real-Time Telemetry Collector (5), and Adaptive Placement Decision Mechanism (6). By analyzing the performance data and dependencies of microservices, the system detects potential SLA violations in advance and proactively repositions risky services. This ensures ultra-low latency and high service availability in 6G networks.

Description

1 TARIFF Microservice placement based on SLA risk score in 6G core waxing. ORGANIZED BY BULUT LOCAL ORCHESTRATION SYSTEM Technical Area 5 The invention relates to core network technologies used in 6G mobile network infrastructures, particularly cloud-based technologies. Service-based network functions that operate on the local architecture, service level negotiation (SLA) is a system that enables dynamic positioning to prevent violations. It is related to the orchestration system. 10 State of the Art In current 5G and early 6G core network designs, network functions are handled by microservices. It is run in the cloud environment. However, existing orchestration systems, 15 Microservice deployments typically involve static resource usage or simple CPU / RAM thresholds. It determines this based on its values. This situation can occur during momentary traffic fluctuations or infrastructure issues. This leads to communication delays between services in case of malfunctions. Existing systems, services the dependencies between them and the impact of these dependencies on the SLA in real time. It cannot calculate this. As a result, a performance degradation in one microservice triggers a chain reaction of 20 This negatively impacts the overall quality of service and leads to SLA violations. Furthermore, the existing Orchestration tools assess the health of nodes in cloud-native architecture based only on basic data. Because it monitors using metrics, it cannot offer a proactive settlement strategy. Purpose of the Invention 25 The invention is a cloud-based system that regulates microservice placement in a 6G core network based on SLA risk score. It presents a local orchestration system. The main purpose of the invention is to enable microservices. potential SLA violations by using work performance and infrastructure health data. The goal is to identify them in advance. The system calculates a risk score for each microservice, identifying 30 risky ones. The aim is to move services to healthier or closer nodes. Existing Unlike technology, invention addresses not only resource utilization but also inter-service delays and It offers a decision-making mechanism based on a dependency map. In this way, the network End-to-end delay times for functions are minimized and service continuity is ensured. 2 The invention enhances the ultra-low latency and high reliability required by 6G networks. It aims to achieve its goals in a cloud-native environment. Figures that will help understand the invention. Figure 1 shows a general representation of the system that is the subject of the invention. Explanation of Part References 1: SLA Risk Analysis Module 10 2: Dynamic Resource Allocation Unit 3: Microservice Dependency Mapping Service 4: Cloud-Local Orchestration Engine 5: Real-Time Telemetry Collector 6: Adaptive Settlement Decision-Making Mechanism 15 Detailed Description of the Invention The invention is a cloud-based system that regulates microservice placement in a 6G core network based on SLA risk score. Local orchestration system, SLA Risk Analysis Module (1), Dynamic Resource Allocation Unit 20 (2), Microservice Dependency Mapping Service (3), Cloud-Local Orchestration Engine (4), Real-Time Telemetry Collector (5) and Adaptive Placement Decision Mechanism (6) It includes. Real-Time Telemetry Collector (5) works on all cloud-native architectures. The microservices continuously collect CPU, RAM, disk I / O, and network latency data. This data is 25 To monitor the health status and performance metrics of microservices in real time. The collected data is used to monitor the operational status of microservices and infrastructure resources. The data is then transferred to other modules to determine occupancy rates. Microservice Dependency Mapping Service (3) is a service-based architecture in the 6G core network. It creates the topology. This service shows which microservice communicates with which, and the data flow. It analyzes the intensity and critical dependencies of an authentication service. For example, The strong dependency of the user database service is defined on this map. Mapping determines which other services might experience a performance loss in one microservice. It forms the basis for determining how it will affect things. 3 SLA Risk Analysis Module (1) collects data from Real-Time Telemetry Collector (5) and topology information provided by the Microservice Dependency Mapping Service (3) It calculates a risk score for each microservice using the following: current latency, This is determined by considering resource consumption trends and the health status of dependent services. If a microservice's latency approaches the SLA threshold or if it is dependent on a 5-bit system If service performance deteriorates, the risk score is increased. The Adaptive Placement Decision Mechanism (6) is the risk generated by the SLA Risk Analysis Module (1). By processing their scores, it decides on the repositioning of microservices. This mechanism, The risk score identifies the most suitable new node for microservices exceeding a certain threshold. Decision criteria. Specifically, the target node's current resource state, inter-microservice network distance, and energy 10 Efficiency parameters are used. This mechanism, through a proactive approach, detects SLA violations. It generates a command to swap the microservices before the event occurs. Cloud Local Orchestration Engine (4), Adaptive Placement Decision Mechanism (6) It is the unit that implements the decisions made. This engine is a containerized microservices engine. orchestration commands (e.g., 15) required to move structures to new nodes Kubernetes API calls are generated. This ensures that there is no service interruption during the migration process. It also simultaneously performs traffic routing updates. Dynamic Resource Allocation Unit (2) is needed at new locations of microservices. This unit optimizes the resources they hear. This unit considers the microservice's risk score and traffic. It dynamically increases or decreases CPU and RAM limits according to the intensity. Thus, high 20 Performance loss is prevented by reserving additional resources for risky services. The system operating scenario is as follows: Real-Time Telemetry Collector (5), all on the network It collects performance data from microservices. Microservice Dependency Mapping Service (3), It updates the network of relationships between services. The SLA Risk Analysis Module (1) uses this data. It calculates a risk score for each microservice. Adaptive Decision Mechanism (6), risk 25 It identifies serves with high scores and determines whether these serves are healthier or closer to success. It decides that it needs to be moved to the nodes. Cloud Local Orchestration Engine (4) makes this decision. It implements and moves microservices to their new locations. Finally, the Dynamic Resource Allocation Unit. (2), by optimizing the resources of the migrated microservices, SLA compliance is guaranteed. He takes. 30

Claims

4 REQUESTS 1. Cloud-native system that regulates microservice placement based on SLA risk score in a 6G core network. It is an orchestration system; its characteristic feature is: - 5 that collects performance data from microservices running on cloud-native architecture. Real-Time Telemetry Collector (5), - analyzing communication relationships and dependencies between microservices Microservice Dependency Mapping Service (3), - SLA risk assessment for each microservice using telemetry data and dependency mapping. SLA Risk Analysis Module (1), which calculates the score, 10 - Decision on repositioning microservices based on calculated risk scores Adaptive Settlement Decision Mechanism (6), - enables the migration of microservices to new nodes in accordance with the decisions made. Cloud Local Orchestration Engine (4) and - Dynamic Resource Allocation 15, which dynamically adjusts the resource needs of microservices. Unit (2) It includes.

2. Microservice placement in a 6G core network based on SLA risk score, according to Claim 1. It is a cloud-native orchestration system that organizes; its feature is SLA Risk Analysis Module (1) 20 current latency and resource consumption of microservices implemented by Taking trends and the health status of dependent services as input, and using these inputs It generates a risk score representing the probability of an SLA violation and sets this score above a defined threshold. If it exceeds the limit, it sends a trigger signal to the Adaptive Settlement Decision Mechanism (6). It involves a decision-making mechanism. 25 3. Microservice placement in a 6G core network based on SLA risk score, according to Claim 1. It is a cloud-based local orchestration system that features Adaptive Decision Layout. For microservices with high risk score, implemented by mechanism (6). During target node selection, the current resource status of the target node, microservices 30 The most suitable placement by evaluating network distance and energy efficiency parameters. It includes an optimization algorithm that determines the key point.

4. Microservice placement in a 6G core network based on SLA risk score, according to Claim 1. It is a cloud-native orchestration system that organizes and features Cloud-Native Orchestration 35. The migration of microservices to new nodes is performed by the engine (4). Simultaneously update traffic routing to prevent service interruption during transit. It involves a transition management process that is carried out as follows.

5. Microservice placement in a 6G core network based on SLA risk score, according to Claim 1. It is a cloud-based native orchestration system, and its feature is Dynamic Resource Allocation Unit 5. (2) performed according to the risk score and traffic density of microservices Dynamically increasing or decreasing CPU and RAM limits to manage resource usage. It includes a resource management module that optimizes resources.

6. According to Claim 1, microservice placement in a 6G core network based on SLA risk score is 10 It is a cloud-native orchestration system that regulates and features microservice dependency. Data flow between services performed by Mapping Service (3) by analyzing the density and critical dependencies, it creates a topology map and this It includes an analysis process that shares the map with the SLA Risk Analysis Module (1).

7. Microservice placement in a 6G core network based on SLA risk score, according to Claim 1. It is a cloud-based local orchestration system that features real-time telemetry. The CPU, RAM, disk I / O and network of microservices are performed by the Collector (5). continuously collects delay data and uses this data with the SLA Risk Analysis Module (1) It involves a data collection process that shares information. 20

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