Cloud Computing
Cloud computing delivers on-demand access to computing resources over the internet. AWS, Azure, and GCP power the majority of modern enterprise infrastructure.
Cloud fundamentally shifted how teams think about infrastructure. Instead of procuring servers, managing data centers, and planning capacity years in advance, teams scale resources on-demand, paying only for what they use. This elasticity enables startups to compete with enterprises while allowing enterprises to optimize costs.
Building multi-cloud platforms at 1C Platform and FLIR Systems revealed that cloud provider choice matters less than understanding core services (compute, storage, database, networking). The real challenge is designing systems that avoid vendor lock-in while leveraging managed services for competitive advantages.
Cloud cost management is a discipline that most teams underinvest in until bills become alarming. Rightsizing instances, leveraging reserved capacity for predictable workloads, implementing auto-scaling for variable demand, and enforcing tagging policies for cost attribution can reduce cloud spend by 30-50% without any impact on performance or reliability.
The shared responsibility model is fundamental to cloud security. Cloud providers secure the infrastructure; customers are responsible for everything running on it. Misconfigured S3 buckets, overly permissive IAM roles, and unencrypted data at rest remain among the most common causes of cloud security incidents - all of which are entirely preventable through disciplined configuration management.
Contents 3 Sections
1Cloud Service Models
IaaS (Infrastructure as a Service) Raw computing resources: VMs, storage, networking. You manage: OS, runtime, apps, data. Examples: AWS EC2, Azure VMs, GCP Compute Engine.
PaaS (Platform as a Service) Managed platform for deploying applications. You manage: apps and data only. Examples: AWS Elastic Beanstalk, Azure App Service, Google App Engine, Heroku.
SaaS (Software as a Service) Ready-to-use software over the internet. You manage: configuration and data. Examples: Salesforce, Gmail, Slack.
FaaS / Serverless Function-level deployment, pay per execution. No server management. Examples: AWS Lambda, Azure Functions, Google Cloud Functions.
2AWS Core Services
- Compute
- EC2: Virtual machines
- Lambda: Serverless functions
- ECS / EKS: Container orchestration
- Fargate: Serverless containers
- Storage
- S3: Object storage (files, backups, static websites)
- EBS: Block storage for EC2
- EFS: Managed NFS file system
- Glacier: Long-term archival
- Database
- RDS: Managed relational DBs (PostgreSQL, MySQL, Aurora)
- DynamoDB: Managed NoSQL key-value/document
- ElastiCache: Managed Redis / Memcached
- Redshift: Data warehouse
- Networking
- VPC: Virtual private cloud, isolated network
- Route 53: DNS and traffic routing
- CloudFront: CDN
- API Gateway: Managed API proxy
- DevOps / ML
- CodePipeline / CodeBuild: CI/CD
- CloudFormation: IaC
- SageMaker: Managed ML platform
- CloudWatch: Monitoring and logging
3Cloud Cost Optimization
Right-sizing Compute
``bash
# AWS Compute Optimizer recommendations
aws compute-optimizer get-ec2-instance-recommendations --account-ids 123456789012 --query 'instanceRecommendations[?finding==OVER_PROVISIONED`]'
# Find idle RDS instances
aws rds describe-db-instances --query 'DBInstances[?DBInstanceStatus==available].[DBInstanceIdentifier,DBInstanceClass]'
```
Savings Strategies | Strategy | Savings | Trade-off | |---|---|---| | Reserved Instances (1yr) | ~40% | Commitment | | Reserved Instances (3yr) | ~60% | Long commitment | | Savings Plans | ~40-60% | Flexible | | Spot Instances | ~70-90% | Can be interrupted | | Graviton (ARM) | ~20% | Recompile required |
S3 Lifecycle Policies
``json
{
"Rules": [
{
"Status": "Enabled",
"Transitions": [
{ "Days": 30, "StorageClass": "STANDARD_IA" },
{ "Days": 90, "StorageClass": "GLACIER" },
{ "Days": 365, "StorageClass": "DEEP_ARCHIVE" }
],
"Expiration": { "Days": 2555 }
}
]
}
``
Cost Monitoring with AWS Cost Explorer ```python import boto3
ce = boto3.client('ce')
response = ce.get_cost_and_usage( TimePeriod={'Start': '2024-01-01', 'End': '2024-02-01'}, Granularity='MONTHLY', GroupBy=[{'Type': 'DIMENSION', 'Key': 'SERVICE'}], Metrics=['UnblendedCost'] )
for group in response['ResultsByTime'][0]['Groups']: service = group['Keys'][0] cost = group['Metrics']['UnblendedCost']['Amount'] print(f"{service}: $" + str(round(float(cost), 2))) ```
Explore the Full Portfolio
This is the complete portfolio of Sufi Khan Sulaiman, a technology leader specialising in B2B commerce and digital automation. Start from the Home page for the overview, then move through two decades of career experience across FLIR Systems, Lorex Technology, and 1c Platform, and the full catalogue of project case studies spanning headless commerce migrations, AI recommendation engines, and multi-channel fulfilment systems.
The skills and certifications page maps the technical and leadership capabilities behind the work, while the articles and the knowledge base break down the thinking into actionable frameworks. For hands-on learning, the tutorials and applications sections cover practical builds from front-end fundamentals to full-stack web apps.
For consulting engagement, the expertise page outlines service offerings, the ecommerce hub covers platform architecture and automation strategy, and the ecommerce guide (PDF) is a downloadable 55-page field manual. When you are ready to talk, the contact page is the direct line.