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                "System: You are a helpful AI assistant for Chaos Engineering.\nGiven a summary of a Chaos Engineering cycle, please elaborate the summary.\nThe output should be formatted as a JSON instance that conforms to the JSON schema below.\n\nAs an example, for the schema {\"properties\": {\"foo\": {\"title\": \"Foo\", \"description\": \"a list of strings\", \"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"required\": [\"foo\"]}\nthe object {\"foo\": [\"bar\", \"baz\"]} is a well-formatted instance of the schema. The object {\"properties\": {\"foo\": [\"bar\", \"baz\"]}} is not well-formatted.\n\nHere is the output schema:\n```\n{\"properties\": {\"summary\": {\"title\": \"Summary\", \"type\": \"string\"}}, \"required\": [\"summary\"]}\n```\nHuman: Here is the overview of a Chaos Engineering Cycle:\n# Here is a Chaos Engineering cycle\n## Step 0. User-input understanding\n### Here is the overview of user inputs:\nThe system consists of the following K8s manifest(s):K8s manifest: nginx_demo/nginx/service.yaml\n```yaml\napiVersion: v1\nkind: Service\nmetadata:\n  name: example-service\nspec:\n  selector:\n    app: example\n  ports:\n    - protocol: TCP\n      port: 80\n      targetPort: 80\n```\nSummary of nginx_demo/nginx/service.yaml:\n- This manifest defines a Kubernetes Service.\n- The Service is named 'example-service'.\n- It uses a selector to target pods with the label 'app: example'.\n- The Service listens on port 80 using the TCP protocol.\n- Traffic received on port 80 is forwarded to the target port 80 on the selected pods.\n\nK8s manifest: nginx_demo/nginx/pod.yaml\n```yaml\napiVersion: v1\nkind: Pod\nmetadata:\n  name: example-pod\n  labels:\n    app: example\nspec:\n  restartPolicy: Never\n  containers:\n  - name: example-container\n    image: nginx:1.17.1\n    ports:\n    - containerPort: 80\n```\nSummary of nginx_demo/nginx/pod.yaml:\n- This manifest defines a Kubernetes Pod.\n- The Pod is named 'example-pod'.\n- It includes metadata with a label 'app: example'.\n- The Pod's restart policy is set to 'Never', meaning it won't restart automatically if it fails.\n- The Pod contains one container named 'example-container'.\n- The container uses the 'nginx:1.17.1' image, which is a specific version of the Nginx web server.\n- The container exposes port 80, which is commonly used for HTTP traffic.\n\nThe intra/inter dependencies of the above K8s manifests are as follows:- Dependencies within nginx_demo/nginx/service.yaml:\nThe Service named 'example-service' depends on the Endpoints resource with the same name 'example-service'. This dependency indicates that the Service will automatically create and manage an Endpoints object that contains the IP addresses and ports of the Pods selected by the Service's selector (app: example). The Endpoints resource is crucial for routing traffic from the Service to the appropriate Pods.\n\n- Dependencies from nginx_demo/nginx/service.yaml to nginx_demo/nginx/pod.yaml:\nThe dependency indicates that the Endpoints resource associated with the Service named 'example-service' is linked to a Pod named 'example-pod'. This means that the Service 'example-service' is configured to route traffic to the Pod 'example-pod', which is selected based on the label 'app: example'. The Endpoints resource dynamically updates to reflect the IP addresses of the Pods that match the Service's selector, ensuring that traffic is correctly routed to the available Pods.\n\nThe expected type of application on the system (i.e., K8s manfests):\nWeb server application using Nginx to serve HTTP traffic.; The manifests provided are for a Kubernetes setup involving a Pod running an Nginx container and a Service to expose this Pod. The file names and the use of the Nginx image suggest that the application is a web server. Nginx is commonly used as a web server to serve static content, reverse proxy, or load balancer. The Service is configured to expose the Pod on port 80, which is the default port for HTTP traffic, further indicating that this setup is intended to serve web content. The logical assumption is that this setup is for a simple web application or a demonstration of serving web pages using Nginx on Kubernetes.\n\nChaos-Engineering instructions for the system are as follows: - The Chaos-Engineering experiment must be completed within 1 minute.\n\n## Step 1. Hypothesis definition\n### Here is the overview of the hypothesis for the system:\nThe hypothesis is \"The steady states of the sytem are maintained even when the fault scenario occurs (i.e., when the faults are injected)\".\nThe steady states here are as follows:\n2 steady states are defined.\n1st steady states:\n- Name: PodRunning\n- Description: The Pod named 'example-pod' must be in a running state to ensure that the Nginx web server is available to serve HTTP traffic. This is critical because the Pod's restart policy is set to 'Never', meaning it won't automatically restart if it fails.\n- Threshold for the steady state: Pod 'example-pod' must be in 'Running' state for at least 95% of the time during the 1-minute observation period.; The current state shows that the pod is consistently in the 'Running' state. Setting a threshold of 95% allows for a small tolerance for transient issues or delays in status updates, ensuring that the pod is generally available to serve HTTP traffic as expected.\n- Whether the steady state meets the threshold is determined by the following Python script with K8s API:\n```\nimport os\nimport time\nimport argparse\nfrom kubernetes import client, config\nfrom unittest_base import K8sAPIBase\n\nclass TestPodRunningState(K8sAPIBase):\n    def __init__(self):\n        super().__init__()\n\n    def check_pod_running_status(self, namespace, pod_name):\n        try:\n            pod = self.v1.read_namespaced_pod(name=pod_name, namespace=namespace)\n            return pod.status.phase == 'Running'\n        except client.exceptions.ApiException as e:\n            print(f\"Exception when calling CoreV1Api->read_namespaced_pod: {e}\")\n            return False\n\n    def test_pod_running_state(self, duration):\n        namespace = 'default'\n        pod_name = 'example-pod'\n        running_count = 0\n\n        # Check the pod status for the specified duration\n        for _ in range(duration):\n            if self.check_pod_running_status(namespace, pod_name):\n                running_count += 1\n            time.sleep(1)\n\n        # Calculate the percentage of time the pod was running\n        running_percentage = (running_count / duration) * 100\n\n        # Assert that the pod was running for at least 95% of the time\n        assert running_percentage >= 95, f\"Pod '{pod_name}' was not running for at least 95% of the time. Actual: {running_percentage}%\"\n\n\ndef main():\n    parser = argparse.ArgumentParser(description='Test if a Kubernetes Pod is running for at least 95% of the time.')\n    parser.add_argument('--duration', type=int, default=60, help='Duration to check the pod status in seconds')\n    args = parser.parse_args()\n\n    test = TestPodRunningState()\n    test.test_pod_running_state(args.duration)\n\n\nif __name__ == '__main__':\n    main()\n```2nd steady states:\n- Name: ServiceTrafficRouting\n- Description: The Service named 'example-service' must correctly route HTTP traffic to the 'example-pod'. This ensures that incoming requests on port 80 are forwarded to the Pod's container port 80, allowing the Nginx server to handle the requests. This is crucial for maintaining the availability and functionality of the web service.\n- Threshold for the steady state: http_req_failed: <= 0.01%; The current state shows 0.00% failed requests, indicating a highly reliable service. Setting a threshold of <= 0.01% allows for a small tolerance while maintaining high availability and reliability of the service. This ensures that the ServiceTrafficRouting is functioning correctly, with minimal disruptions, and aligns with the current performance.\n- Whether the steady state meets the threshold is determined by the following K6 Javascript:\n```\nimport http from 'k6/http';\nimport { check } from 'k6';\n\nexport const options = {\n  vus: 10,\n  duration: '5s',\n  thresholds: {\n    // Adding a threshold to ensure that the percentage of failed HTTP requests is less than or equal to 0.01%\n    'http_req_failed': ['rate<=0.01'],\n  },\n};\n\nexport default function () {\n  const res = http.get('http://example-service.default.svc.cluster.local');\n  check(res, {\n    'status is 200': (r) => r.status === 200,\n    'body contains Nginx': (r) => r.body.includes('Welcome to nginx!'),\n  });\n}\n```\n\nThe fault scenario here is as follows:\n\nAn assumed fault scenario is as follows:\n- Event: Cyber attack causing network disruption and resource exhaustion\n- Used Chaos Engineering tool: Chaos Mesh\n- Faults to simulate the event: [[Fault(name='NetworkChaos', name_id=0, params={'action': 'delay', 'direction': 'to', 'target': {'mode': 'all', 'selector': {'namespaces': ['default'], 'labelSelectors': {'app': 'example'}}}, 'mode': 'all', 'selector': {'namespaces': ['default'], 'labelSelectors': {'app': 'example'}}, 'device': 'eth0', 'delay': {'latency': '100ms', 'jitter': '10ms'}})], [Fault(name='StressChaos', name_id=0, params={'stressors': {'cpu': {'workers': 1, 'load': 100}}, 'mode': 'one', 'selector': {'namespaces': ['default'], 'labelSelectors': {'app': 'example'}}, 'containerNames': ['example-container']})], [Fault(name='PodChaos', name_id=0, params={'action': 'pod-kill', 'mode': 'one', 'selector': {'namespaces': ['default'], 'labelSelectors': {'app': 'example'}}})]]\n- Description: The system's weak points include the single Pod setup with a 'Never' restart policy and the reliance on network connectivity for service routing. The Pod's inability to restart automatically makes it vulnerable to failures, and network issues can disrupt traffic routing. Related fault types include PodChaos to simulate Pod failures, NetworkChaos to simulate network disruptions, and StressChaos to simulate resource exhaustion. To break the system, we can first introduce network latency to disrupt traffic routing, then apply CPU stress to exhaust resources on the Pod, and finally, simulate a Pod failure to test the system's resilience.\n- The expected faults' impacts on the steady states: {{\"PodRunning\": \"The Pod 'example-pod' may fail to remain in the 'Running' state due to resource exhaustion and simulated Pod failure, causing it to fall below the 95% threshold.\", \"ServiceTrafficRouting\": \"Network latency and Pod failure can lead to increased HTTP request failures, potentially exceeding the 0.01% threshold, disrupting service availability.\"}}\n\n## Step 2.1. Chaos-Engineering experiment\n### Here is the overview of my Chaos-Engineering experiment to verify the hypothesis:\nThe entire time schedule of the Chaos-Engineering experiment is as follows (The experiment is divided into three phases: pre-validation, fault-injection, and post-validation phases):\nGiven the constraints of the experiment needing to be completed within 1 minute, we need to carefully allocate time to each phase to ensure meaningful results. The pre-validation phase is crucial to confirm that the system is in a steady state before introducing any faults. Since the PodRunning steady state requires a 1-minute observation period, we will allocate 20 seconds to pre-validation to quickly verify the current state. The fault-injection phase is the core of the experiment, where we introduce the faults to observe the system's behavior. We will allocate 30 seconds to this phase, allowing enough time to introduce the network delay, CPU stress, and Pod failure faults sequentially. Finally, the post-validation phase is essential to ensure the system returns to its steady state after the faults are removed. We will allocate 10 seconds to this phase to quickly verify the system's recovery. This allocation ensures that the total time of the experiment is 1 minute, with a balanced focus on validation and fault injection.\n- Total experiment phase: 1m\n- Pre-validation phase: 20s\n- Fault-injection phase: 30s\n- Post-validation phase: 10s\n\nThe details of the three phases are as follows:\nPre-validation Phase (20s):\nIn the pre-validation phase, we need to ensure that the system is in its expected steady states before we proceed with fault injection. Given the constraints, we have 20 seconds to perform these checks. The two steady states to verify are 'PodRunning' and 'ServiceTrafficRouting'. Since the 'PodRunning' state requires checking the pod's status over a 1-minute period, we will perform a quick check to ensure the pod is currently running. For 'ServiceTrafficRouting', we will perform a brief HTTP request test to ensure the service is routing traffic correctly. These checks will be executed sequentially due to the short time frame, ensuring that each steady state is verified before moving to the next phase.\n\nFault-injection Phase (30s):\nIn this fault-injection phase, we have a total of 30 seconds to execute the chaos experiments. The goal is to observe the system's behavior under stress and network disruptions, as well as its ability to handle a pod failure. Given the constraints, we will stagger the fault injections to maximize the observation of their impacts on the system. \n\nFirst, we will introduce network latency using NetworkChaos. This will simulate a network disruption, potentially affecting the ServiceTrafficRouting steady state. We will start this fault at the beginning of the phase and let it run for 10 seconds. \n\nNext, we will introduce CPU stress using StressChaos. This will simulate resource exhaustion on the pod, potentially affecting both the PodRunning and ServiceTrafficRouting steady states. We will start this fault 10 seconds into the phase and let it run for 10 seconds. \n\nFinally, we will simulate a pod failure using PodChaos. This will directly impact the PodRunning steady state. We will start this fault 20 seconds into the phase and let it run for 10 seconds. \n\nBy staggering the faults in this manner, we can observe the compounded effects of network disruption, resource exhaustion, and pod failure on the system's steady states.\n\nPost-validation Phase (10s):\nIn the post-validation phase, we need to ensure that the system has returned to its steady states after the fault injection. Given the short duration of 10 seconds for this phase, we will conduct quick checks to verify the steady states. The two steady states to verify are 'PodRunning' and 'ServiceTrafficRouting'. Since the phase is only 10 seconds long, we will stagger the unit tests slightly to fit within this timeframe. The 'PodRunning' test will start immediately as it is crucial to ensure the pod is back in the running state. The 'ServiceTrafficRouting' test will follow shortly after, as it depends on the pod being in a running state to route traffic correctly. This approach ensures that we quickly verify the critical components of the system's functionality post-fault injection.\n\nThe summary of the above experiment plan:\nThe Chaos Engineering experiment is structured into three phases, each with specific tasks and timelines to ensure the system's resilience is effectively tested within a total duration of one minute. \n\nIn the pre-validation phase, which lasts for 20 seconds, the system's steady states are verified sequentially. Initially, the 'PodRunning' state is checked for 10 seconds using the 'pre-unittest-podrunning' workflow, starting immediately at the beginning of this phase. Following this, the 'ServiceTrafficRouting' state is verified for another 10 seconds using the 'pre-unittest-servicetrafficrouting' workflow, commencing right after the first check.\n\nThe fault-injection phase spans 30 seconds and involves staggered fault injections to observe their impact on the system. At the start of this phase, the 'NetworkChaos' fault is injected using the 'fault-networkchaos' workflow, running for 10 seconds. Concurrently, the steady states 'PodRunning' and 'ServiceTrafficRouting' are monitored for the entire 30 seconds using the 'fault-unittest-podrunning' and 'fault-unittest-servicetrafficrouting' workflows, respectively. After 10 seconds, the 'StressChaos' fault is introduced using the 'fault-stresschaos' workflow, also lasting 10 seconds. Finally, at the 20-second mark, the 'PodChaos' fault is injected using the 'fault-podchaos' workflow, continuing for the remaining 10 seconds of this phase.\n\nIn the post-validation phase, which is 10 seconds long, the system's recovery to steady states is verified. The 'PodRunning' state is checked first for 5 seconds using the 'post-unittest-podrunning' workflow, starting immediately. Subsequently, the 'ServiceTrafficRouting' state is verified for the final 5 seconds using the 'post-unittest-servicetrafficrouting' workflow. This staggered approach ensures that the system's critical functionalities are quickly assessed post-fault injection.\n\nTo automatically conduct the above experiment plan with Chaos Mesh, the following Chaos-Mesh-Worfklow file was created (by applying it to the cluster, the experiment plan will be automatically executed according to the Chaos-Mesh-Worfklow file):\n```yaml\napiVersion: chaos-mesh.org/v1alpha1\nkind: Workflow\nmetadata:\n  name: chaos-experiment-20241002-042120\nspec:\n  entry: the-entry\n  templates:\n    #-------------------------------\n    # entry point of whole workflow\n    #-------------------------------\n    - name: the-entry\n      templateType: Serial\n      deadline: 4m\n      children:\n        - pre-validation-phase\n        - fault-injection-phase\n        - post-validation-phase\n\n    #-------------------------------------\n    # Entry point of pre-validation-phase\n    #-------------------------------------\n    - name: pre-validation-phase\n      templateType: Serial\n      deadline: 1m20s\n      children:\n        - pre-validation-overlapped-workflows\n    \n    - name: pre-validation-suspend-workflow\n      templateType: Serial\n      deadline: 50s\n      children:\n        - pre-validation-suspend\n        - pre-unittest-servicetrafficrouting\n\n    - name: pre-validation-suspend\n      templateType: Suspend\n      deadline: 10s\n\n    - name: pre-validation-overlapped-workflows\n      templateType: Parallel\n      deadline: 50s\n      children:\n        - pre-unittest-podrunning\n        - pre-validation-suspend-workflow\n\n    # Definitions of children of pre-validation-phase\n    - name: pre-unittest-podrunning\n      templateType: Task\n      deadline: 40s\n      task:\n        container:\n          name: pre-unittest-podrunning-container\n          image: chaos-eater/k8sapi:1.0\n          imagePullPolicy: IfNotPresent\n          command: [\"/bin/bash\", \"-c\"]\n          args: [\"python /chaos-eater/sandbox/cycle_20241002_041546/unittest_PodRunning_mod0.py --duration 10\"]\n          volumeMounts:\n            - name: pvc-volume\n              mountPath: /chaos-eater\n        volumes:\n          - name: pvc-volume\n            persistentVolumeClaim:\n              claimName: pvc\n\n    - name: pre-unittest-servicetrafficrouting\n      templateType: Task\n      deadline: 40s\n      task:\n        container:\n          name: pre-unittest-servicetrafficrouting-container\n          image: grafana/k6:latest\n          command: [\"k6\", \"run\", \"--duration\", \"10s\", \"--quiet\", \"/chaos-eater/sandbox/cycle_20241002_041546/hypothesis/steady_states/unittest_ServiceTrafficRouting_mod0.js\"]    \n          volumeMounts:\n            - name: pvc-volume\n              mountPath: /chaos-eater\n        volumes:\n          - name: pvc-volume\n            persistentVolumeClaim:\n              claimName: pvc\n    \n    #--------------------------------------\n    # Entry point of fault-injection-phase\n    #--------------------------------------\n    - name: fault-injection-phase\n      templateType: Serial\n      deadline: 1m30s\n      children:\n        - fault-injection-overlapped-workflows\n\n    - name: fault-injection-parallel-workflow\n      templateType: Parallel\n      deadline: 1m\n      children:\n        - fault-unittest-podrunning\n        - fault-unittest-servicetrafficrouting\n        - fault-networkchaos\n\n    - name: fault-injection-suspend-workflow\n      templateType: Serial\n      deadline: 20s\n      children:\n        - fault-injection-suspend\n        - fault-stresschaos\n\n    - name: fault-injection-suspend\n      templateType: Suspend\n      deadline: 10s\n\n    - name: fault-injection-suspend-workflow2\n      templateType: Serial\n      deadline: 30s\n      children:\n        - fault-injection-suspend2\n        - fault-podchaos\n\n    - name: fault-injection-suspend2\n      templateType: Suspend\n      deadline: 20s\n\n    - name: fault-injection-overlapped-workflows\n      templateType: Parallel\n      deadline: 1m\n      children:\n        - fault-injection-parallel-workflow\n        - fault-injection-suspend-workflow\n        - fault-injection-suspend-workflow2\n\n    # Definitions of children of pre-validation-phase\n    # unit tests\n    - name: fault-unittest-podrunning\n      templateType: Task\n      deadline: 1m\n      task:\n        container:\n          name: fault-unittest-podrunning-container\n          image: chaos-eater/k8sapi:1.0\n          imagePullPolicy: IfNotPresent\n          command: [\"/bin/bash\", \"-c\"]\n          args: [\"python /chaos-eater/sandbox/cycle_20241002_041546/unittest_PodRunning_mod0.py --duration 30\"]\n          volumeMounts:\n            - name: pvc-volume\n              mountPath: /chaos-eater\n        volumes:\n          - name: pvc-volume\n            persistentVolumeClaim:\n              claimName: pvc\n\n    - name: fault-unittest-servicetrafficrouting\n      templateType: Task\n      deadline: 1m\n      task:\n        container:\n          name: fault-unittest-servicetrafficrouting-container\n          image: grafana/k6:latest\n          command: [\"k6\", \"run\", \"--duration\", \"30s\", \"--quiet\", \"/chaos-eater/sandbox/cycle_20241002_041546/hypothesis/steady_states/unittest_ServiceTrafficRouting_mod0.js\"]    \n          volumeMounts:\n            - name: pvc-volume\n              mountPath: /chaos-eater\n        volumes:\n          - name: pvc-volume\n            persistentVolumeClaim:\n              claimName: pvc\n\n    # fault_injections\n    - name: fault-networkchaos\n      templateType: NetworkChaos\n      deadline: 10s\n      networkChaos:\n        action: delay\n        delay:\n          jitter: 10ms\n          latency: 100ms\n        device: eth0\n        direction: to\n        mode: all\n        selector:\n          labelSelectors:\n            app: example\n          namespaces:\n            - default\n        target:\n          mode: all\n          selector:\n            labelSelectors:\n              app: example\n            namespaces:\n              - default\n\n\n    - name: fault-stresschaos\n      templateType: StressChaos\n      deadline: 10s\n      stressChaos:\n        containerNames:\n          - example-container\n        mode: one\n        selector:\n          labelSelectors:\n            app: example\n          namespaces:\n            - default\n        stressors:\n          cpu:\n            load: 100\n            workers: 1\n\n\n    - name: fault-podchaos\n      templateType: PodChaos\n      deadline: 10s\n      podChaos:\n        action: pod-kill\n        mode: one\n        selector:\n          labelSelectors:\n            app: example\n          namespaces:\n            - default\n\n    \n    #--------------------------------------\n    # Entry point of post-validation phase\n    #--------------------------------------\n    - name: post-validation-phase\n      templateType: Serial\n      deadline: 1m10s\n      children:\n        - post-validation-overlapped-workflows\n\n    - name: post-validation-suspend-workflow\n      templateType: Serial\n      deadline: 40s\n      children:\n        - post-validation-suspend\n        - post-unittest-servicetrafficrouting\n\n    - name: post-validation-suspend\n      templateType: Suspend\n      deadline: 5s\n\n    - name: post-validation-overlapped-workflows\n      templateType: Parallel\n      deadline: 40s\n      children:\n        - post-unittest-podrunning\n        - post-validation-suspend-workflow\n\n    # Definitions of children of pre-validation-phase\n    - name: post-unittest-podrunning\n      templateType: Task\n      deadline: 35s\n      task:\n        container:\n          name: post-unittest-podrunning-container\n          image: chaos-eater/k8sapi:1.0\n          imagePullPolicy: IfNotPresent\n          command: [\"/bin/bash\", \"-c\"]\n          args: [\"python /chaos-eater/sandbox/cycle_20241002_041546/unittest_PodRunning_mod0.py --duration 5\"]\n          volumeMounts:\n            - name: pvc-volume\n              mountPath: /chaos-eater\n        volumes:\n          - name: pvc-volume\n            persistentVolumeClaim:\n              claimName: pvc\n\n    - name: post-unittest-servicetrafficrouting\n      templateType: Task\n      deadline: 35s\n      task:\n        container:\n          name: post-unittest-servicetrafficrouting-container\n          image: grafana/k6:latest\n          command: [\"k6\", \"run\", \"--duration\", \"5s\", \"--quiet\", \"/chaos-eater/sandbox/cycle_20241002_041546/hypothesis/steady_states/unittest_ServiceTrafficRouting_mod0.js\"]    \n          volumeMounts:\n            - name: pvc-volume\n              mountPath: /chaos-eater\n        volumes:\n          - name: pvc-volume\n            persistentVolumeClaim:\n              claimName: pvc\n```\n\n## Step 2.2, 3, 4. Experiment execution, analysis and improvement (reconfiguring the system to satisfy the hypothesis)\n### Here is the improvement history:\n### Experiment result (1st try)\nPassed unittests:\n- pre-unittest-podrunning\n- pre-unittest-servicetrafficrouting\n- fault-unittest-servicetrafficrouting\nFailed unittests:\n- fault-unittest-podrunning\n```log\n...d': '4706085f-6263-43ae-93f5-b4a61de8b6be', 'Date': 'Wed, 02 Oct 2024 04:18:53 GMT', 'Content-Length': '190'})\nHTTP response body: {\"kind\":\"Status\",\"apiVersion\":\"v1\",\"metadata\":{},\"status\":\"Failure\",\"message\":\"pods \\\"example-pod\\\" not found\",\"reason\":\"NotFound\",\"details\":{\"name\":\"example-pod\",\"kind\":\"pods\"},\"code\":404}\n\n\nException when calling CoreV1Api->read_namespaced_pod: (404)\nReason: Not Found\nHTTP response headers: HTTPHeaderDict({'Audit-Id': '40cb9ffd-50aa-44b7-a7fb-1f782819dd8a', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'X-Kubernetes-Pf-Flowschema-Uid': 'c4624bd9-7fc7-42c6-bcb8-4235110a860d', 'X-Kubernetes-Pf-Prioritylevel-Uid': '4706085f-6263-43ae-93f5-b4a61de8b6be', 'Date': 'Wed, 02 Oct 2024 04:18:54 GMT', 'Content-Length': '190'})\nHTTP response body: {\"kind\":\"Status\",\"apiVersion\":\"v1\",\"metadata\":{},\"status\":\"Failure\",\"message\":\"pods \\\"example-pod\\\" not found\",\"reason\":\"NotFound\",\"details\":{\"name\":\"example-pod\",\"kind\":\"pods\"},\"code\":404}\n\n\n\n```\n\n- post-unittest-podrunning\n```log\n...d': '4706085f-6263-43ae-93f5-b4a61de8b6be', 'Date': 'Wed, 02 Oct 2024 04:19:22 GMT', 'Content-Length': '190'})\nHTTP response body: {\"kind\":\"Status\",\"apiVersion\":\"v1\",\"metadata\":{},\"status\":\"Failure\",\"message\":\"pods \\\"example-pod\\\" not found\",\"reason\":\"NotFound\",\"details\":{\"name\":\"example-pod\",\"kind\":\"pods\"},\"code\":404}\n\n\nException when calling CoreV1Api->read_namespaced_pod: (404)\nReason: Not Found\nHTTP response headers: HTTPHeaderDict({'Audit-Id': '3b789a5e-1855-49b7-81a4-ef21d2d89df8', 'Cache-Control': 'no-cache, private', 'Content-Type': 'application/json', 'X-Kubernetes-Pf-Flowschema-Uid': 'c4624bd9-7fc7-42c6-bcb8-4235110a860d', 'X-Kubernetes-Pf-Prioritylevel-Uid': '4706085f-6263-43ae-93f5-b4a61de8b6be', 'Date': 'Wed, 02 Oct 2024 04:19:23 GMT', 'Content-Length': '190'})\nHTTP response body: {\"kind\":\"Status\",\"apiVersion\":\"v1\",\"metadata\":{},\"status\":\"Failure\",\"message\":\"pods \\\"example-pod\\\" not found\",\"reason\":\"NotFound\",\"details\":{\"name\":\"example-pod\",\"kind\":\"pods\"},\"code\":404}\n\n\n\n```\n\n- post-unittest-servicetrafficrouting\n```log\n...p(95)=0s    \n     http_req_duration..........: avg=0s    min=0s     med=0s    max=0s  p(90)=0s     p(95)=0s    \n   \u2717 http_req_failed............: 100.00% 20 out of 20\n     http_req_receiving.........: avg=0s    min=0s     med=0s    max=0s  p(90)=0s     p(95)=0s    \n     http_req_sending...........: avg=0s    min=0s     med=0s    max=0s  p(90)=0s     p(95)=0s    \n     http_req_tls_handshaking...: avg=0s    min=0s     med=0s    max=0s  p(90)=0s     p(95)=0s    \n     http_req_waiting...........: avg=0s    min=0s     med=0s    max=0s  p(90)=0s     p(95)=0s    \n     http_reqs..................: 20      0.666528/s\n     iteration_duration.........: avg=7.08s min=3.62ms med=3.03s max=30s p(90)=15.38s p(95)=16.11s\n     iterations.................: 20      0.666528/s\n     vus........................: 1       min=1        max=10\n     vus_max....................: 10      min=10       max=10\n\ntime=\"2024-10-02T04:19:55Z\" level=error msg=\"thresholds on metrics 'http_req_failed' have been crossed\"\n\n```\n\n\n\n### Analysis report (1st try)\nThe Chaos Engineering experiment results indicate several critical insights into the system's resilience and failure points:\n\n1. **PodRunning Steady State Failure**:\n   - The 'fault-unittest-podrunning' and 'post-unittest-podrunning' tests failed, indicating that the Pod 'example-pod' was not found during and after the fault injection phase. This suggests that the Pod was either terminated or failed to restart due to the 'PodChaos' fault, which simulates a Pod failure.\n   - The Pod's restart policy is set to 'Never', which means it does not automatically restart if it fails. This configuration is a significant vulnerability, as it does not allow the system to recover from Pod failures, leading to prolonged downtime.\n   - The failure to maintain the 'PodRunning' steady state highlights the need for a more resilient Pod configuration, such as using a 'Always' or 'OnFailure' restart policy to ensure automatic recovery from failures.\n\n2. **ServiceTrafficRouting Steady State Failure**:\n   - The 'post-unittest-servicetrafficrouting' test failed, with 100% of HTTP requests failing. This indicates that the Service was unable to route traffic to the Pod, likely because the Pod was not running.\n   - The 'NetworkChaos' fault, which introduced network latency, and the 'PodChaos' fault, which terminated the Pod, both contributed to the disruption in service traffic routing. The network latency could have caused initial delays, but the Pod termination was the primary cause of the complete failure in routing traffic.\n   - The failure to maintain the 'ServiceTrafficRouting' steady state suggests that the system's reliance on a single Pod is a bottleneck. Implementing a Deployment with multiple replicas could improve resilience by ensuring that traffic can be routed to other available Pods if one fails.\n\n3. **Pre-Validation Success**:\n   - The successful pre-validation tests ('pre-unittest-podrunning' and 'pre-unittest-servicetrafficrouting') confirm that the system was initially in a healthy state, with the Pod running and the Service correctly routing traffic. This baseline is crucial for understanding the impact of the injected faults.\n\n4. **Fault Injection Impact**:\n   - The staggered fault injection strategy effectively tested the system's resilience. The combination of network latency, CPU stress, and Pod termination exposed the system's vulnerabilities, particularly the lack of redundancy and automatic recovery mechanisms.\n   - The experiment's design, with overlapping fault injections and continuous monitoring, provided a comprehensive assessment of the system's ability to maintain steady states under stress.\n\nIn conclusion, the experiment revealed that the system's current configuration is not resilient to the simulated fault scenario. Key recommendations include revising the Pod's restart policy, implementing a Deployment with multiple replicas, and considering network policies to mitigate the impact of network disruptions. These changes would enhance the system's ability to maintain steady states and improve overall resilience.\n\n### Improvement result (1st try)\n2 K8s manifests are modified:\n- The K8s manifest 'nginx_demo/nginx/pod.yaml' has been deleted.\nThe existing Pod manifest is deleted to replace it with a Deployment manifest, which provides better resilience through automatic restarts and multiple replicas.\n\n- The K8s manifest 'nginx_demo/nginx/deployment.yaml' was created.\nThis new Deployment manifest replaces the standalone Pod manifest. It introduces a Deployment with multiple replicas and an 'Always' restart policy to improve resilience.\n```yaml\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n  name: example-deployment\nspec:\n  replicas: 3\n  selector:\n    matchLabels:\n      app: example\n  template:\n    metadata:\n      labels:\n        app: example\n    spec:\n      containers:\n      - name: example-container\n        image: nginx:1.17.1\n        ports:\n        - containerPort: 80\n      restartPolicy: Always\n```\n\n\n\n### Experiment result (2nd try)\nPassed unittests:\n- pre-unittest-podrunning\n- pre-unittest-servicetrafficrouting\n- fault-unittest-podrunning\n- fault-unittest-servicetrafficrouting\n- post-unittest-podrunning\n- post-unittest-servicetrafficrouting\nFailed unittests:\n\n\nPlease elaborate the above summary of a Chaos Engineering Cycle.\nAI: ```json\n{\"summary\":"
            ],
            "```json\n{\"summary\":\"The Chaos Engineering cycle involved testing a Kubernetes-based web server application using Nginx to ensure its resilience under fault conditions. The system was initially set up with a Kubernetes Service and a Pod, where the Service routed HTTP traffic to the Pod running an Nginx container. The experiment aimed to test the hypothesis that the system's steady states would be maintained even when faults were injected.\\n\\nThe steady states defined were 'PodRunning', ensuring the Pod was in a running state 95% of the time, and 'ServiceTrafficRouting', ensuring HTTP request failures were less than or equal to 0.01%. The fault scenario simulated a cyber attack causing network disruption and resource exhaustion using Chaos Mesh, with faults like NetworkChaos, StressChaos, and PodChaos.\\n\\nThe experiment was structured into three phases: pre-validation, fault-injection, and post-validation, all within a 1-minute timeframe. Pre-validation confirmed the system's initial steady states. During fault-injection, network latency, CPU stress, and Pod failure were introduced sequentially. Post-validation checked the system's recovery to steady states.\\n\\nThe first experiment revealed vulnerabilities: the Pod's 'Never' restart policy led to failures in maintaining the 'PodRunning' state, and the single Pod setup caused 100% HTTP request failures, indicating a need for redundancy. The analysis recommended using a Deployment with multiple replicas and an 'Always' restart policy.\\n\\nImprovements were made by deleting the Pod manifest and creating a Deployment manifest with three replicas and an 'Always' restart policy. The second experiment, with these changes, passed all unit tests, confirming the system's improved resilience and ability to maintain steady states under fault conditions.\"}\n```"
        ]
    }
]