[
    {
        "name": "analysis_experiment",
        "token_usage": {
            "input_tokens": 4071,
            "output_tokens": 615,
            "total_tokens": 4686
        },
        "message_history": [
            [
                "System: You are a helpful AI assistant for Chaos Engineering.\nGiven K8s manifests that defines a network system, its hypothesis, the overview of a Chaos-Engineeering experiment, and the experiment's results, you will analyze the experiment's results.\nAlways keep the following rules:\n- Analyze step by step why why the test(s) failed, based on the system configuraions (manifests) and the flow of the experiment.\n- Specify the cause while mentioning the corresponding system configurations and the corresponding phenamena Chaos-Engineering experiment.\n- The anaysis report here will be used for reconfiguring the system later to avoid the failes (improve resiliency). Therefore, Therefore, make carefully the report rich in insights so that it will be helpful at that time.\n- The 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\": {\"report\": {\"title\": \"Report\", \"description\": \"Analysis of the experiment result.\", \"type\": \"string\"}}, \"required\": [\"report\"]}\n```\nHuman: # Here is the overview of my system:\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\n# Here is the hypothesis for my 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# Here is the overview of my Chaos-Engineering experiment to verify the hypothesis:\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\n# The experiment's results are as follows:\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\nNow, please analyze the results and provide an analysis report rich in insights.\nAI: ```json\n{\"report\":"
            ],
            "```json\n{\"report\":\"The 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```"
        ]
    }
]