COMPX529-21B: Assignment 1
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Individual Assignment
YouwilldeployanapplicationtoaKubernetesclusterandobservehowdifferentrequest loads and deployment configurations impact the system.
TheincludedinstructionsforthisassignmentmakeuseofalocalKubernetes Cluster installed into a virtual machine managed by Vagrant. If you wish to explore other implementation strategies, you are welcome to do soat your own risk.
Vagrantshouldbeavailable foruse onmachinesinRBlockLabs 1 and2.Ifyouuse computerswithintheselabs,pleaseensurethatyoushutdownallVMinstancesbefore you leave. Run vagrant halt followed by vagrant destroy.
Forthisassignment,youwillcreateaKubernetesClusterwithinaVagrantmanagedVM using the microk8s package.
Vagrant manages virtual machines through configuration within a Vagrantfile, found within the configuration folder that comes with thisassignment.Followthe included instruction sheet to create and provision your cluster.
Youwillthendeploytheapplication usingtheprovided cafe.yaml file.Seethetutorial video for this assignment for an explanation of theconfiguration of the application.
Thecloud-nativeapplicationconsistsoftwodeployments,exposedasloadbalancing servicesthroughthemetallbloadbalancer.Aloadbalancerserviceexposesanexternal IP address that is mapped to pods within a deploymentthrough an endpoint.
Your task is to perform load testing experiments on the deployed applications to ascertainhow deploymentconfigurationsreacttodifferentloadpatterns.Thiscanbe achieved bygeneratinghttprequestswithinApacheJMeter,aimedattheexternalIP associated with eachservice. Make useofplugins,suchasthe ThroughputShaping Timer, tohelpwiththistask.Youshouldexperimentwithdifferentloadpatterns,aswell as deployment resource limits as shown within thetutorial video.
TheclusteriscreatedwithaGrafanadashboardthatvisualisesresourceutilisationof
pods in real time.
Deliverables:
A report that documents your methodology and observations, the report should include evidence of the following:
An overview of the kubernetes architecture.
The JMeter (jmx) scripts you developed for automaticload testing
Evidence of testing under different load patternsand resource allocations (eg
square and stepping load patterns.
Implementation of horizontal autoscaling based oncpu. (Unlike what the video
suggests, you dont need to implement autoscalingwith other metrics, like
memory, for full marks)
Observations regarding how scaling tenants impactone another. You may alter
the available resource pool with resource quotas.
Assignments handed in up to 54 hours late will loseup to 27 out of 100 marks. The late submission penalty is calculated as 0.5 marks perhour and will be accumulated until it reaches 27 marks.
Assignments handed in more than 54 hours after thespecified due date and time will receive 0 marks and will not be examined or acceptedfor submission.
Your report will be assessed on the following criteria:
Evidence of successful configuration of your kubernetescluster, and deployment
of the provided applications.
Evidence and justification of test development andimplementation.
Quality of discussion regarding autoscaling and self-adaptiveproperties of K8s.
Information regarding the kubernetes hpa can be foundat: https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/
Syntax for commands using kubectl can be found at: https://kubernetes.io/docs/reference/kubectl/overview/
Apache JMeter:https://jmeter.apache.org/download_jmeter.cgi
Throughput Shaping Timer: https://www.blazemeter.com/blog/using-jmeters-throughput-shaping-timer-plugin
End of Assignment 1