NFV-MANO Workload Management for Priority-Based Request Handling
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Solution Overview
Problem
NFV-MANO functional entities face challenges in handling workload fluctuations, leading to congestion and potential system collapse due to rigid scaling mechanisms and limited scalability, especially during legitimate or illegitimate load increases.
Innovation Solution
Implementing a proactive workload management system that includes defining an overload threshold and associating it with a policy to prioritize and manage incoming requests based on priority and workload, using machine learning techniques for dynamic decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If NFV-MANO functional entities use rigid scaling mechanisms, then system structure remains simple and predictable, but the system cannot handle workload fluctuations leading to congestion and potential collapse
Solution Approach 1:
The patent implements dynamic threshold adjustments for workload management. The threshold for accepting new requests is not fixed but adapts based on current system conditions, allowing the system to respond flexibly to workload fluctuations while maintaining a relatively simple operational framework.
Solution Approach 2:
The system changes key parameters (workload threshold, request acceptance criteria) based on current system state. When workload approaches critical levels, the threshold dynamically adjusts to reject lower priority requests, enabling the system to handle variable loads without requiring complex structural changes.
2Reliability
If NFV-MANO functional entities accept all incoming requests, then service completeness is maintained, but system congestion occurs during high workload periods
Solution Approach 1:
The system implements partial request acceptance based on priority levels. Instead of accepting or rejecting all requests uniformly, it selectively accepts high-priority requests while rejecting lower-priority ones when workload is high, maintaining system stability without completely blocking service delivery.
Solution Approach 2:
The system automatically monitors its own workload state and makes autonomous decisions about request acceptance. The functional entity self-regulates by comparing current workload against dynamic thresholds and independently determining which requests to accept or reject, maintaining stability without external intervention.
3Reliability
If NFV-MANO functional entities reject low priority requests during overload, then system congestion is prevented, but service completeness deteriorates
Solution Approach 1:
The system applies different quality levels of service to different requests based on their priority. High-priority requests receive full service while lower-priority requests may be rejected during overload conditions, allowing the system to maintain congestion control while preserving critical service availability.
Data Source
AI summary
The disclosure relates to a method, apparatus and computer readable media for workload management. The method is executed by a service provider (SP) Network Function Virtualization Management and Orchestration (NFV-MANO) functional entity (FE) (SP FE). The method comprises receiving a request for an NFV-MANO service from an NFV-MANO service user (SU). The method comprises upon detecting that a threshold indicative of a state of a workload of the SP FE is crossed, determining a priority of the request for the NFV-MANO service and determining, based on the priority and the workload of the SP FE, whether to accept or reject the request. The method comprises sending a response to the SU indicative of whether the request is accepted or rejected.