Scalable API Orchestration Using Reinforcement Learning in Cloud-Native Systems

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DOI: https://doi.org/10.63345/ijrmp.v11.i7.3

Ishu Anand Jaiswal

University of the Cumberlands College

Station Drive, Williamsburg, KY 40769 United States

Abstract— New architecture cloud solutions depend on distributed microservices which interact via Application Programming Interfaces (APIs). With large scale systems, coordinating thousands of API calls gets more and more difficult as workloads change, resources are dynamically allocated, there are service dependencies, and performance limits. Conventional rule-based orchestration and fixed schedules do not tend to keep up with the dynamism in system states and cause ineffective use of resources, spiking of latency, and bottlenecks in services. To overcome these drawbacks, this study discusses how reinforcement learning (RL) can be used to scale API orchestration in a cloud-native setting.

The reinforcement learning offers an adaptive decision framework according to which an intelligent agent gets to learn the best coordination strategies by interacting with the system environment. The RL agent dynamically picks orchestration actions like load balancing adjustments, service routing, scaling decisions and request prioritization by observing the state of a system including CPU utilization, request latency, queue length, and service availability. In the long run, the agent streamlines the performance parameters such as response times, throughput, fault tolerance, and system efficiency.

This paper suggests an architecture that combines reinforcement learning and the cloud-native orchestration solutions like Kubernetes, API gateways, and service meshes. The suggested framework involves Deep Q-Network (DQN) model to train on the best API routing and scaling policies in a simulated cloud environment. Experimental analysis compares the RL-based orchestration to the traditional heuristic-based orchestration approaches in terms of various performance measurements such as mean latency, request throughput, resource usage and system reliability.

Findings indicate that reinforcement learning provides substantial benefits in the efficiency of orchestration. The RL based strategy minimizes the average API response time, improves the system throughput and improves the resiliency to high load conditions. In addition, the adaptive learning feature enables the system to react properly to unexpected traffic and service failures.

The results indicate that reinforcement learning is potentially instrumental in the development of intelligent clouds infrastructures, where self-orchestration is performed by the use of APIs. The study is a contribution to the existing research on AI-based cloud management, and offers a platform on which autonomous orchestration mechanisms can be applied to large-scale distributed systems.

Keywords— Cloud-Native Systems, API Orchestration, Reinforcement Learning, Microservices Architecture, Intelligent Resource Allocation, Kubernetes, Service Mesh, Deep Reinforcement Learning, Adaptive Cloud Infrastructure

References

Sutton, R., & Barto, A. (2018). Reinforcement Learning: An Introduction. MIT Press.
Newman, S. (2019). Building Microservices. O’Reilly Media.
Hightower, K., Burns, B., & Beda, J. (2017). Kubernetes: Up and Running. O’Reilly Media.
Mao, H., Alizadeh, M., Menache, I., & Kandula, S. (2016). Resource management with deep reinforcement learning. ACM HotNets.
Chen, X., et al. (2018). Reinforcement learning for cloud resource allocation. IEEE Transactions on Cloud Computing.
Zhang, Q., Chen, M., & Li, L. (2019). Reinforcement learning for dynamic resource provisioning. IEEE Cloud Computing.
Burns, B., & Beda, J. (2018). Designing Distributed Systems. O’Reilly Media.
Merkel, D. (2014). Docker: Lightweight Linux containers. Linux Journal.
Xu, J., et al. (2020). Deep reinforcement learning for service orchestration. IEEE Transactions on Network and Service Management.
Nginx Inc. (2021). API Gateway and Microservices Architecture.
Lewis, J., & Fowler, M. (2014). Microservices Architecture.
Humble, J., & Farley, D. (2011). Continuous Delivery. Addison-Wesley.
Google Cloud (2020). Site Reliability Engineering Practices.
Villamizar, M., et al. (2016). Evaluating microservice architectures. IEEE Cloud Computing.
Bernstein, D. (2014). Containers and cloud computing. IEEE Cloud Computing.
Mao, H., et al. (2017). Learning scheduling algorithms with deep reinforcement learning. ACM SIGCOMM.
Kratzke, N. (2018). Cloud-native architectures and microservices. IEEE Software.
Xu, Z., et al. (2021). Intelligent service orchestration in cloud computing. Future Generation Computer Systems.
Zhang, Y., et al. (2020). AI-driven cloud resource management. IEEE Access.
Buyya, R., et al. (2019). Cloud Computing: Principles and Paradigms. Wiley.