Conference:  KubeCon + CloudNativeCon Europe 2022

Authors: Fredrik Klingenberg, Kasper Borg Nissen, Christian Hüning, Catherine Paganini, Eli Goldberg

2022-05-20

In this panel, you'll hear from end users across a variety of industries on how they use the Linkerd service mesh in real-world production scenarios. Use cases range from applying mutual TLS to encrypt and secure all service-to-service communication, load balancing gRPC requests, and troubleshooting services before they're pushed to production. Panelists represent a variety of companies with very different environments, goals, and priorities, and discussion will be focused on real-world outcomes.Click here to view captioning/translation in the MeetingPlay platform!

Conference:  KubeCon + CloudNativeCon North America 2021

Authors: Ying-Feng Hsu

2021-10-14

Many K8s extensions have been focused on large scale container computation. But, how to strike a balance between energy efficiency and service performance for container operations due to the continuous growth of IoT devices and edge computing systems? The current K8s does not provide container orchestration from the perspective of data center power reduction. This talk presents a Workload Allocation Optimizer (WAO) based on the K8s architecture. WAO uses ML to predict the power increasing of workloads and introduces a scoring plugin to the K8s scheduler framework for Node selection. WAO-load balancer enables Pods to Nodes assignment with optimal power consumption. This talk gives you details on how power saving can be realized for cloud-edge computing systems. Instead of using the virtual environment, we demonstrate the proposed WAO in a real edge data center with 200+ servers and show you how WAO manipulates the tradeoff between service performance and data center power saving.

Conference:  KubeCon + CloudNativeCon North America 2021

Authors: Chen Wang, Abdul Qadeer

2021-10-13

Load balancing and resource allocation in Kubernetes clusters using Trimaran plugins

• Trimaran is a set of plugins for Kubernetes clusters that optimize resource allocation and load balancing
• The Target Load Packing plugin aims to achieve high utilization across all nodes while maintaining a safe margin for CPU usage spikes
• The Load Variation Risk Balancing plugin computes a risk score based on CPU and memory utilization and chooses the bottleneck resource score
• Trimaran uses multiple metric sources and caches data to avoid overwhelming metric providers
• Future work includes integrating Trimaran with other schedulers and incorporating additional resources like IO and network latency