Key points are not available for this paper at this time.
Cloud services have experienced rapid growth, leading to an escalation in challenges related to service monitoring and resulting in higher maintenance costs. The size of cloud applications has expanded, and the workload on the cloud system application fluctuates over time, progressively increasing. The cloud's capability to manage additional resources and relinquish them during periods of inactivity is a valuable feature. However, traditional monolithic applications pose difficulties in terms of maintenance. To address this issue, the transformation of cloud applications into microservices has proven effective. Microservices comprise heterogeneous services with intricate communication patterns, aiming to enhance scalability and facilitate flexible provisioning. This paper proposes the integration of adaptive learning into Cloud Watch, enabling automatic, data-driven scaling decisions. This approach enhances the cost- and time-effectiveness of microservices, presenting a solution to the challenges associated with managing monolithic applications.
Raja et al. (Fri,) studied this question.