Device and method for scaling microservices
AssigneeTelefonaktiebolaget LM Ericsson (publ)
InventorsJohan Eker, Albin Heimerson, Karl-Erik Årzén
Life of this patent
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Abstract
A method and a device for scaling microservices in a service mesh using reinforcement learning with a feedback signal. The reinforcement learning model uses information representing an input workload of a microservice chain and current and historical resource allocations of the service mesh, a reward, and the feedback signal to obtain an optimized resource allocation for the workload as an output of the RL model.
Claims18
A method for scaling microservices in a service mesh, the method comprising:
- obtaining information representing a workload of a microservice chain, wherein the workload comprises at least one job;
- obtaining information representing current and historical resource allocations of the service mesh,
- determining a reward, wherein the reward is indicative of completed jobs and allocated resources of the service mesh;
- producing a feedback signal, wherein the feedback signal is indicative of a delay for increasing the resource allocation of the service mesh;
- running a Reinforcement Learning, RL, model on the information representing the workload, current and historical resource allocations, reward, and feedback signal;
- obtaining a further resource allocation for the workload as an output of the RL model.
A device for scaling microservices in a service mesh, the device comprising a processor and a memory, the memory having stored thereon instructions executable by the processor, wherein the instructions, when executed by the processor, cause the device to:
- obtain information representing a workload of a microservice chain, wherein the workload comprises at least one job;
- obtain information representing current and historical resource allocations of the service mesh,
- determine a reward, wherein the reward is a function of completed jobs and allocated resources of the service mesh;
- produce a feedback signal, wherein the feedback signal is based on a delay for increasing the resource allocation of the service mesh;
- run a Reinforcement Learning, RL, model on the information representing the workload, current and historical resource allocations, reward, and feedback signal;
- obtain a further resource allocation for the workload as an output of the RL model.
A computer program comprising instructions which, when run in a processing unit on a device, cause the device to:
- obtain information representing a workload of a microservice chain, wherein the workload comprises at least one job;
- obtain information representing current and historical resource allocations of the service mesh;
- determine a reward, wherein the reward is a function of completed jobs and allocated resources of the service mesh;
- produce a feedback signal, wherein the feedback signal is based on a delay for increasing the resource allocation of the service mesh;
- run a Reinforcement Learning, RL, model on the information representing the workload, current and historical resource allocations, reward, and feedback signal;
- obtain a further resource allocation for the workload as an output of the RL model.
Dependencies are inferred from the claim text.