updates

Signed-off-by: (Bit-Mage) <[email protected]>

Content/20240215121050-kubernetes.org

@@ -8,6 +8,10 @@
 see [[id:f822f8f6-89eb-4aa8-ac8f-fdcff3f06fb9][Orchestration]]
 
 * Stream
+** 0x22E3
+ - reading up on [[id:7cb8489b-2b84-4224-b3f9-9f5bf0f38cfe][autoscaling]]
+ - see [[id:7f960631-c727-41b8-80c2-3ccaa4ae4ba2][Scheduling Algorithms]] & [[id:59305648-ed10-4298-be07-cd67f277f612][Scheduling-K8S]]
+
 ** 0x22E2
 - reading https://sookocheff.com/post/kubernetes/understanding-kubernetes-networking-model/
 - reading up on [[id:2244b835-3c8a-496e-b4bd-5ab0951c7d29][ConfigMap]]s

Content/20240501195739-scheduling_algorithms.org

@@ -21,6 +21,9 @@ In practice, these goals often conflict.
 Incorporating multiple dimensions when modelling the priority functions is a difficult (and interesting) problem.
  - in the context of computer science, over the dimensions of compute, networking and storage, the idea of "scheduling" workloads gradually evolves into the idea of [[id:f822f8f6-89eb-4aa8-ac8f-fdcff3f06fb9][orchestration]]
 
+* Forks and Flavours
+** [[id:59305648-ed10-4298-be07-cd67f277f612][Scheduling-K8S]]
+** [[id:c50c2084-973a-4f9e-9ab3-946c71b5f2fa][RTLinux]]
 
 * Resources
  - https://container.training/intro-selfpaced.yml.html#832

Content/20240522113815-linux_virtualization.org

@@ -1,7 +1,8 @@
 :PROPERTIES:
 :ID:       7291bab2-4e69-47ad-ae37-f3da260b1d89
-:ROAM_ALIASES: KVM
 :END:
 #+title: Linux Virtualization
 #+filetags: :cs:
 
+* Relevant Nodes
+** [[id:996c1f7e-363d-41a3-b48b-affcae4b95bd][eBPF (extended Berkely Packet Filters)]]

Content/20240728174107-keda.org

@@ -3,3 +3,6 @@
 :END:
 #+title: KEDA (Kubernetes Event-Driven AutoScaler)
 #+filetags: :cncf:cloud-native:
+
+* Resources
+- https://keda.sh/docs/2.15/concepts/#architecture

Content/20240926125731-virtualization.org

@@ -3,3 +3,6 @@
 :END:
 #+title: Virtualization
 #+filetags: :cs:
+
+* Relevant Nodes
+** [[id:7291bab2-4e69-47ad-ae37-f3da260b1d89][Linux Virtualization]]

Content/20241014093602-autoscaling_k8s.org

@@ -0,0 +1,88 @@
+:PROPERTIES:
+:ID:       7cb8489b-2b84-4224-b3f9-9f5bf0f38cfe
+:END:
+#+title: AutoScaling-K8S
+#+filetags: :k8s:
+
+* Cluster AutoScaling
+** Definition
+  - Cluster Autoscaling is a feature in Kubernetes that automatically adjusts the size of a cluster based on the current resource demand.
+  - It targets the number of nodes, ensuring that workloads have enough resources to run.
+
+** Components
+  - *Cluster Autoscaler*: A Kubernetes component responsible for scaling the number of nodes.
+  - *Node Pools/Groups*: Logical groupings of similar nodes managed by the autoscaler.
+  - *Metrics*: Utilizes metrics such as CPU and memory usage, as well as pod resource requests and availability.
+
+** Mechanism
+  - *Scale Out*: Increases the number of nodes when existing nodes are under pressure, and resources are insufficient.
+  - *Scale In*: Decreases the number of nodes when they are underutilized, conserving cost and resources.
+
+** Benefits
+  - *Cost Efficiency*: Automatically optimizes resource utilization and potentially reduces costs.
+  - *Reliability*: Ensures workloads have necessary resources, enhancing stability.
+  - *Scalability*: Seamlessly supports growing or fluctuating workloads.
+
+** Considerations
+  - *Pod Disruptions*: Ensuring minimal disruption to running pods during scaling events.
+  - *Cluster Limits*: Understanding and setting appropriate maximum and minimum node quotas.
+  - *Rescheduling*: Management of pod scheduling upon node removal.
+
+** Connections
+  - *HPA (Horizontal Pod Autoscaler)*: Works alongside cluster autoscaling by adjusting the number of pod replicas.
+  - *Resource Quotas*: Ensures that autoscaler respects defined resource limits within namespaces.
+
+** Critique
+  - *Limitations on Custom Metrics*: While effective on standard metrics, custom use cases may require additional configurations.
+  - *Latency in Scaling Actions*: There might be a delay in scaling reactions depending on configurations and metric polling intervals.
+
+** Ideation Strategies
+  - *Monitoring*: Implement comprehensive monitoring to assess scaling effectiveness.
+  - *Automation*: Use CI/CD to manage and update scaling strategies as workloads and requirements change.
+
+** Further Inquiry
+  - How does autoscaler manage complex workloads with mixed resource requirements?
+  - What are the best practices for configuring autoscalers for high-availability systems?
+  - How can one incorporate cost analysis into autoscaling decision frameworks?
+
+* Vertical Pod AutoScaling
+- Vertical Pod Autoscaling (VPA) Overview
+  - Adjusts the resource requests and limits of Kubernetes pods.
+  - Focuses on optimizing resource allocation for existing pods, enhancing performance.
+  - Automatically increases or decreases CPU and memory resource requests based on usage metrics.
+- Components of VPA
+  - Recommender:
+    - Suggests optimal CPU and memory requests based on historical usage.
+  - Updater:
+    - Responsible for evicting pods to apply new recommendations.
+  - Admission Controller:
+    - Modifies incoming pod specs to align with recommended resources.
+- Benefits of VPA
+  - Reduces resource wastage by tuning resources closer to actual usage.
+  - Helps in cost savings by optimizing resource usage.
+  - Simplifies resource management by alleviating the need for manual tuning.
+- Limitations & Considerations
+  - Pod eviction may cause temporary downtimes; not ideal for stateful applications.
+  - May not react instantly to sudden spikes in demand; better for gradually evolving workloads.
+  - Requires a robust monitoring setup to capture accurate usage metrics.
+* Horizontal Pod AutoScaling
+
+** **Overview**
+  - Automatic adjustment of pod count in Kubernetes
+  - Based on CPU, memory, or custom metric utilization
+  - Essential for performance and resource optimization
+
+** **Components**
+  - **Metrics Server**: Supplies metrics for decision-making
+  - **Controller Manager**: Executes scaling operations
+
+** **Benefits**
+  - Optimizes resource use through dynamic pod management
+  - Cost-effective resource allocation in cloud settings
+  - Improves reliability and availability by responding to traffic changes
+
+* Tools
+** [[id:c50c85ac-0b21-486f-99ac-00f325e2c43c][KEDA (Kubernetes Event-Driven AutoScaler)]]
+* Resources
+- https://kubernetes.io/blog/2016/07/autoscaling-in-kubernetes/
+- https://github.com/kubernetes/autoscaler

Content/20241014101243-bounty_hunting.org

@@ -0,0 +1,11 @@
+:PROPERTIES:
+:ID:       f48a21a7-496e-4068-8b31-0e4a485701a9
+:END:
+#+title: Bounty Hunting
+#+filetags: :cs:
+
+Incentivized Intellectual Competitions are great.
+
+* Resources
+** https://console.algora.io/
+** https://github.com/djadmin/awesome-bug-bounty

Content/20241014102055-scheduling_k8s.org

@@ -0,0 +1,27 @@
+:PROPERTIES:
+:ID:       59305648-ed10-4298-be07-cd67f277f612
+:END:
+#+title: Scheduling-K8S
+#+filetags: :k8s:
+
+* Overview
+
+- The scheduling component in Kubernetes is a core responsibility of the control plane.
+- Assign pods to nodes ensuring optimal resource allocation and management.
+
+* Primary Functionalities
+** Resource Optimization
+- Considers CPU, memory requests, and constraints.
+** Affinity/Anti-affinity Rules
+- Enables pod co-location or separation.
+** Taints and Tolerations
+- Ensures nodes can repel certain pods unless explicitly tolerated.
+** NodeSelector and NodeAffinity
+- Limits nodes that can host particular pods.
+** Priorities and Preemption
+- Ensures urgent pods get priority using preemption.
+** Scheduler Algorithms:
+    - Priority Functions: Rank nodes based on set criteria.
+    - Predicate Functions: Determines if a pod can run on a particular node.
+** Custom Schedulers:
+    - Allows for user-defined policies and scheduling strategies.

Content/20241014102716-rtlinux.org

@@ -0,0 +1,8 @@
+:PROPERTIES:
+:ID:       c50c2084-973a-4f9e-9ab3-946c71b5f2fa
+:END:
+#+title: RTLinux
+#+filetags: :linux:
+
+* Resources
+ - https://en.wikipedia.org/wiki/RTLinux

Content/20241014102929-ebpf_extended_berkely_packet_filters.org

@@ -0,0 +1,12 @@
+:PROPERTIES:
+:ID:       996c1f7e-363d-41a3-b48b-affcae4b95bd
+:END:
+#+title: eBPF (extended Berkely Packet Filters)
+
+
+* Resources
+:PROPERTIES:
+:ID:       291cfc45-cab6-4c16-a807-3535a1f09233
+:END:
+
+ - https://ebpf.io/what-is-ebpf/