Sökning: "Vertical Pod Autoscaler"
Hittade 5 uppsatser innehållade orden Vertical Pod Autoscaler.
1. Comparing various methods for improving resource allocation on a single node cluster in Kubernetes
Kandidat-uppsats, Linköpings universitet/Institutionen för datavetenskapSammanfattning : 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. LÄS MER
2. Predicting resource usage on a Kubernetes platform using Machine Learning Methods
Master-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)Sammanfattning : Cloud computing and containerization has been on the rise in recent years and have become important areas of research and development in the field of computer science. One of the challenges in distributed and cloud computing is to predict the resource utilization of the nodes that run the applications and services. LÄS MER
3. Performance Evaluation of Kubernetes Autoscaling strategies on GKE clusters
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Cloud computing and containerisation have experienced significant growth in recent years. With cloud providers requiring users to specify resource limits and requests, the need for performance and resource optimisation has emerged in the cloud computing domain. LÄS MER
4. A performance study for autoscaling big data analytics containerized applications : Scalability of Apache Spark on Kubernetes
Master-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskapSammanfattning : Container technologies are rapidly changing how distributed applications are executed and managed on cloud computing resources. As containers can be deployed on a large scale, there is a tremendous need for Container Orchestration tools like Kubernetes that are highly automatic in deployment, scaling, and management. LÄS MER
5. Predictive vertical CPU autoscaling in Kubernetes based on time-series forecasting with Holt-Winters exponential smoothing and long short-term memory
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Private and public clouds require users to specify requests for resources such as CPU and memory (RAM) to be provisioned for their applications. The values of these requests do not necessarily relate to the application’s run-time requirements, but only help the cloud infrastructure resource manager to map requested virtual resources to physical resources. LÄS MER