Reinforcement Learning Resource Allocation for Interdependent Services
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Solution Overview
Problem
Existing resource allocation methods for Virtual Network Functions (VNFs) and microservices fail to consider interdependencies, leading to inefficient resource utilization and performance challenges in service topologies, especially when handling multiple service function chains.
Innovation Solution
Utilizing Markov Decision Processes (MDPs) and reinforcement learning (RL) to derive resource allocations that account for interdependencies among services, enabling efficient resource allocation based on performance targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static-model-based resource allocation is used, then implementation is simple, but resource utilization efficiency deteriorates due to ignoring service interdependencies
Solution Approach 1:
The patent implements feedback mechanisms where the resource allocation system continuously monitors service performance and interdependency relationships, then adjusts resource allocations accordingly. The reinforcement learning agent observes the current state of services and their dependencies, receives feedback on performance outcomes, and learns to optimize resource distribution dynamically, resolving the contradiction between simple implementation and efficient resource utilization.
Solution Approach 2:
The system enables self-service through automated resource allocation using reinforcement learning. The allocation mechanism autonomously analyzes service interdependencies and performs resource distribution without requiring complex manual configuration or static models, allowing the system to serve itself in optimizing resource allocation while maintaining implementation feasibility.
2Measurement precision
If reinforcement learning with interdependency consideration is implemented, then resource allocation accuracy improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex resource allocation problem into separate Markov Decision Processes for each service. Each MDP handles resource allocation for a specific service while considering its interdependencies with other services, breaking down the overall complex system into manageable modular components that can be solved independently yet coordinated collectively.
Solution Approach 2:
The system utilizes parameter changes by representing service states, resource allocations, and interdependencies as configurable parameters within the MDP framework. The reinforcement learning agent learns optimal policies by adjusting these parameters based on observed outcomes, enabling accurate resource allocation while managing system complexity through parameterized models that can adapt to different service topologies and dependency structures.
Data Source
AI summary
Embodiments include solutions to allocate resources using Markov Decision Processes (MDPs). An exemplary method comprises: receiving a service topology to provide a plurality of services that are interdependent; receiving a performance target for each service; and applying a set of MDPs to the service topology and the performance targets to derive resource allocation to achieve the performance targets, each of the set of MDPs being defined by: a state space including a plurality of states, each state represented by a resource allocation of each of the plurality of services, performance of each of the plurality of services at the each state, and a dependency relationship among the plurality of services; an action space represented by a set of actions that change a resource allocation; a reward function that calculates numeric scores for the plurality of services, and a probability space represented by probabilities to transition.