mlushpenko

# Example scrape config for probing ingresses via the Blackbox Exporter.
#
# The relabeling allows the actual ingress scrape endpoint to be configured
# via the following annotations:
#
# * `prometheus.io/probe`: Only probe services that have a value of `true`
- job_name: 'kubernetes-ingresses'

  metrics_path: /probe
  params:

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 ## Monitor Kubernetes ingress endpoints
- job_name: blackbox
  metrics_path: /probe
  params:
    module: [http_2xx]
  kubernetes_sd_configs:
  - role: ingress
  relabel_configs:

  # 1. Set ingress address protocol to https, so we can make sure certificates are valid while probing endpoints

mlushpenko

apiVersion: v1
kind: ConfigMap
metadata:
  name: cni-configuration
data:
  cni-config: |-
    {
      "cniVersion": "0.3.0",
      "name": "azure",
      "plugins": [

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c.JupyterHub.authenticator_class = RemoteUserAuthenticator 
c.RemoteUserAuthenticator.header_name = "X-User-Id"

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eas: {
  plugins: [
    {
      type: "oidc",
      issuer: {
        discover_url: "https://example.com/.well-known/openid-configuration",
      },
      client: {
        client_id: "tenantA",
        client_secret: "tenantSecret",

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{
  "nbf": 1568720155,
  "exp": 1568723755,
  "name": "lushpenko",
  "email": "maksym.lushpenko@hal24k.com",
  "current_tenant": "tenantA",
  "permissions": [
    "datalab",
    "dataflow",
  ],

mlushpenko

Keybase proof

I hereby claim:

• I am mlushpenko on github.
• I am lumaks (https://keybase.io/lumaks) on keybase.
• I have a public key whose fingerprint is 5336 E35D E3B4 74AD 0A88 EB2A 6EA7 02F0 A2AB 8F0F

To claim this, I am signing this object:

mlushpenko

public int solution(int N) {
        // write your code in Java SE 8
        BigInteger fib = fibonacci(N);
        System.out.println(fib);
        int remainder =  fib.mod(new BigInteger("1000000")).intValue()  ;
        return remainder;
    }

    public BigInteger fibonacci(int i){
        if (i < 2)

mlushpenko

-----------------------------------1.2 Research problem-----------------------------------

In this work we focus on two challenges: (i) combination of the declarative and imperative
approaches to the application provisioning and deployment, and (ii) continuous
deployment of cloud applications. Based on this, the reasearch problem may be formulated as follows:

"How can we enable both, flexibility and fine-grained control, in the deployment and provisioning of multi-cloud
applications, and allow efficient run-time management of such applications?"

-------------------------------------1.3 Research questions-------------------------------------

mlushpenko

5. Conclusion
In this work we presented a DSL for the provisioning and deployment of multi-cloud applications which can be used as a library by third parties. In addition, we showed how this DSL was used in the CloudMF framework to combine declarative and imperative deployment approaches. The combination of both approaches allows application developers to deploy applications only by defining the desired final state of the system, while at the same time allowing them to tune the deployment process to their specific needs with help of the internal DSL. Internal DSL could also be used by reasoning engines to automatically update deployment plans according to the defined policies. 
Moreover, a deployment engine was developed which allows cloud application owners to observe the progress of the deployment process in real-time. The real-time presentation of the deployment process makes it easier to debug applications and gives application operators a clear idea how and when every deployment operation is performed, t