When dealing with latency-critical applications in Kubernetes, a common strategy is to over-allocate resources to ensure the application can meet its latency guarantees during traffic surges. However, this practice often leads to resource underutilizationas the application will not fully utilize its reserved resources. The Kubernetes scheduler cannot initiate new workloads on the node because of perceived full resource utilization. This study explored the utility of two existing methods, Container Runtime Interface (CRI-RM), which we configured to use the 'balloon policy' and the vertical Pod Autoscaler (VPA) in addressing resource underutilization problems on single node Kubernetes clusters while maintaining latency-grantees of certain pods. Utilizing tc-sim, a network traffic simulator, we deployed four latency-critical and two non-latency-critical pods, all subjected to overallocation. Our finding reveals that VPA was ineffectivein detecting and addressing the underutilization of resources because of its slow response in adjusting requests inside the pods. Moreover, it worsened the underutilization issues of the node. Our configuration of the 'balloon policy' failed to detect theover-allocation issues and further led to performance degradation in the simulator, potentially due to the overhead introduced by CRI-RM. These results underscore the intricacy of over-allocation challenges in latency-critical applications, emphasizing the need for proposed-designed solutions that enable quick and dynamic exchange of resources between pods.

With the exponential growth of virtualization and cloud computing over the last decade, many companies in the telecommunications sector have started their journey towards cloud migration by exchanging a lot of specialized hardware for virtualized solutions. With more and more applications running in a cloud environment, it became essential to run these applications on heterogeneous systems with shared underlying hardware and software resources. However, running these applications in a heterogeneous cloud environment often leads to  unpredictable and non-deterministic performance, as all the applications compete for the shared resources to improve their individual performance. This becomes a problem when the interference from the co-hosted applications starts affecting the performance of the critical applications running on the same server. Ericsson is therefore investigating a solution to dynamically manage the low-level hardware and software resources to get deterministic performance on applications deployed using Kubernetes.  In this thesis, the Intent Driven Orchestration (IDO) model developed by Intel is used as the baseline model. This model was then extended by adding another tool to the setup called Container Runtime Interface-Resource Manager (CRI-RM), which is used to manipulate low-level software and hardware resources managed by a Kubernetes cluster at runtime. The results achieved in this thesis suggest that it is possible to get deterministic performance for an application deployed using Kubernetes, by identifying and isolating the CPU cores in the cluster on which the application is running.