Method and system for resource allocation using reinforcement learning

An electronic device, comprising:

• a processor and non-transitory machine-readable storage medium that provides instructions that, when executed by the processor, are capable of causing the processor to perform:
• receiving a service topology to provide a plurality of services included in the service topology for implementing an application in a network, the plurality of services being interdependent to implement the application;
• receiving a performance target for each of the plurality of services in the service topology;
• and
• applying a set of Markov Decision Processes to the service topology and the performance targets to derive resource allocation of each of the plurality of services to achieve the performance targets for the plurality of services, each of the set of Markov Decision Processes 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 of the plurality of services,
• a reward function that calculates numeric scores for the plurality of services, each numeric score for a service of the plurality of services being computed based on the service's current performance, performance target, and
• dependency of the service to other services of the plurality of services, and a probability space represented by probabilities to transition, each transition being from a first state to a second state of the plurality of states.

A non-transitory machine-readable storage medium that provides instructions that, when executed by a processor, are capable of causing the processor to perform:

• receiving a service topology to provide a plurality of services included in the service topology for implementing an application in a network, the plurality of services being interdependent to implement the application;
• receiving a performance target for each of the plurality of services in the service topology;
• and
• applying a set of Markov Decision Processes to the service topology and the performance targets to derive resource allocation of each of the plurality of services to achieve the performance targets for the plurality of services, each of the set of Markov Decision Processes 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 of the plurality of services,
• a reward function that calculates numeric scores for the plurality of services, each numeric score for a service of the plurality of services being computed based on the service's current performance, performance target, and dependency of the service to other services of the plurality of services, and
• a probability space represented by probabilities to transition, each transition being from a first state to a second state of the plurality of states.