DR. ATABAK KH
Cloud Platform Modernization Architect specializing in transforming legacy systems into reliable, observable, and cost-efficient Cloud platforms.
Certified: Google Professional Cloud Architect, AWS Solutions Architect, MapR Cluster Administrator
Welcome to my research collection focused on cloud computing, DevOps, and software engineering.
Problem: Either you pay too much (min too high) or you get tail latency (min too low). Balance it.
Most teams scale on CPU averages. That’s easy-and often wrong. Align autoscaling with your p95 latency SLO instead.
Takeaway: For robust GO prediction, start with homology + PLM baselines, add label smoothing on PPI, and only then graduate to GNNs/multimodal fusion.
Problem. Independent multilabel classifiers for Gene Ontology (GO) often violate the ancestor rule: if a child term is predicted “on,” all its ancestors must also be on. Curators then fix...
Goal: page for user pain, not random metric spikes. Burn-rate alerts do that by measuring how fast you’re spending the error budget for your SLO.
Goal: Make results re-runnable and comparable (CAFA-style).
Takeaway: You don’t need perfection-just a directionally correct cost per request/job.
Context: BigQuery is fast to adopt-and easy to overspend on. Here’s a comprehensive checklist I use in migrations to avoid common cost pitfalls.
Takeaway: a handful of Terraform patterns prevent surprise spend, reduce pager incidents, and make audits easy.
Purpose: A short checklist to avoid inflated or unstable GO results.
Idea: One smoothing step over a normalized PPI graph can yield consistent gains before you build a full GNN.
Takeaway: Frozen PLM embeddings + linear classifier = strong, fast baseline for GO prediction.
Takeaway: You can cut cost and improve reliability/observability without any access to PII or raw logs. Here’s the artifact-only method I use.
Basic ideal solutions
Cloud Migration in a simple way
Digital Transformation with Agile + DevOps Culture
Accountability on Resilience Engineering
Most of the time, dotnet developers setup and configure a windows server and then occupy the entire server for a single project or a micro service project using web services....
Apache Spark is an engine for Big Data processing. One can run Spark on distributed mode on the cluster. In the cluster, there is master and n number of workers....
Before going thorugh configuration and build of kubernetes packages, we should install minikube or Kubernetes. My primary goal was to try Kubernetes on VMs the simplest way. I have chosen...
Docker containers and services do not even need to be aware that they are deployed on Docker, or whether their peers are also Docker workloads or not. Whether your Docker...
What is Docker Compose
This document shares an experience on setting up Kubernetes and then configuring your .NET Core application into your cluster. It will also help automate your deployment using a Kubernetes cluster...
This document would share an experience on setting up dockerized master-slave hadoop and spark on top of them. Then config your environment and listen to steaming data. It will also...
WHAT IS PROTEIN FUNCTION? The meaning of organic capacity is vague, and the correct significance of the term fluctuates in view of the setting in which it is utilized. Clearly...
Showing 7 of 27 papers
Transforming legacy systems to cloud-native architectures
Building reliable systems with proper monitoring and alerting
Data-driven approaches to reduce cloud spending
Dr. Atabak Kh is a cloud platform engineer specializing in modernization, observability, and cost optimization. His research focuses on practical approaches to improving system reliability and reducing cloud costs through data-driven methods.
Current areas of interest include p95-driven autoscaling, privacy-first cloud audits, and SLO-based alerting systems.