MEC Orchestrator Zoning for Service Migration
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Solution Overview
Problem
Multi-access Edge Computing (MEC) and Internet of Things (IoT) device networks face challenges in security, processing, network resources, service availability, and efficiency, particularly in deploying services across different MEC systems and environments, where conventional approaches lack flexibility and do not consider real-world QoS/cost metrics for service consumption.
Innovation Solution
The introduction of QoS/cost-aware proximity zones around MEC servers, defined by a statistical model, allows for flexible usage of MEC platform services across different MEC systems, enabling a signaling protocol for efficient service consumption by classifying and storing proximity measurements, and managing service migration based on performance and cost criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If MEC services are deployed across different MEC systems and environments, then service availability and flexibility are improved, but service consumption efficiency and cost-effectiveness deteriorate due to lack of QoS/cost awareness
Solution Approach 1:
The patent segments the MEC service consumption process into distinct phases: service discovery, service selection, service consumption, and service migration. Each phase is optimized independently with QoS/cost-aware mechanisms, allowing flexible deployment across multiple MEC systems while maintaining consumption efficiency through structured decision-making processes.
Solution Approach 2:
The patent introduces QoS parameters (latency, bandwidth, reliability) and cost parameters as dynamic variables that influence service consumption decisions. The MEC orchestrator continuously monitors and adjusts these parameters to optimize service placement and migration, enabling efficient service consumption across heterogeneous MEC environments while adapting to changing conditions.
2Device complexity
If conventional MEC approaches are used, then system simplicity is maintained, but service availability and efficiency worsen due to lack of QoS/cost metric consideration
Solution Approach 1:
The patent introduces an MEC orchestrator as an intermediary component that centralizes QoS/cost metric collection, analysis, and decision-making. This intermediary manages service placement and migration across multiple MEC hosts, improving service availability through informed decisions while abstracting the complexity from individual MEC components and maintaining manageable system architecture.
Solution Approach 2:
The patent implements continuous feedback mechanisms where the MEC orchestrator monitors QoS metrics (latency, bandwidth, reliability) and cost parameters in real-time, uses this feedback to make dynamic service placement and migration decisions, and adjusts service consumption strategies based on observed performance, thereby improving service availability through data-driven optimization.
3Adaptability or versatility
If service migration is implemented without QoS/cost criteria, then service mobility is improved, but operational efficiency and resource utilization worsen
Solution Approach 1:
The patent implements dynamic service migration mechanisms where service placement and movement are continuously adjusted based on real-time QoS metrics and cost parameters. The MEC orchestrator evaluates multiple factors including latency, bandwidth, reliability, and operational costs to determine optimal migration timing and targets, enabling service mobility while minimizing unnecessary migrations and associated energy consumption.
Solution Approach 2:
The patent employs preliminary evaluation and prediction mechanisms where the MEC orchestrator assesses potential service migration scenarios before execution, predicting QoS improvements and cost savings. This preliminary action allows the system to plan migrations strategically, avoiding premature or unnecessary service movements that would waste operational resources while still achieving service mobility when beneficial.
Data Source
AI summary
Systems and methods for establishing, configuring, and operating multi-access edge computing (MEC) services and service consumption through zoning hosts in multi-vendor or multi-system environments. An apparatus operating as a MEC orchestrator to manage services consumption using zones is configurable to perform operations to: receive, from an application executing at a host, a request for a list of services and corresponding proximity zones; in response to receiving the request for the list of services, query a plurality of hosts for performance metrics of respective services offered from the plurality of hosts, the respective services to be used by the application executing at the host; construct a zone map, the zone map maintaining a mapping between the application and the plurality of hosts based on the performance metrics; and manage migration of the application or a service of the respective services, based on the zone map, to ensure QoS of the application.