A practical guide from lift-and-shift to cloud-optimized
DEC. 26, 2024
2 Min Read
Learn how to evolve your cloud environment from basic lift-and-shift to cost-effective, optimized infrastructure.
In the early rush to the cloud, many companies took a “lift-and-shift” approach, hastily moving systems online with hopes of instant savings and efficiency. While this approach helped organizations meet urgent migration deadlines, many are now grappling with its limitations.
The reality is most businesses face tight deadlines and resource constraints when moving to the cloud. Full re-architecting isn’t always feasible—or necessary. This guide will help you navigate practical cloud optimization, whether you have two months or two years to improve your cloud environment.
When to optimize vs. when to re-architect
Before diving into cloud optimization, it“s crucial to assess which approach makes sense for your situation:
Quick optimization candidates:
- Non-core business applications
- Systems with minimal performance issues
- Applications nearing end-of-life
- Projects with tight deadlines or budget constraints
Re-architecting candidates:
- Core business applications
- Systems facing significant performance/cost issues
- Applications central to long-term strategy
- Projects with adequate time and resources
Common issues with first-generation cloud migrations
Hurried migrations to the cloud tend to present a common set of challenges, but not all require extensive re-architecting to address:
Unexpected cost increases
The promise of cloud cost savings often turns into an unwelcome surprise for organizations after their initial migration. A common scenario plays out in development environments, where resources continue running 24/7 despite only being used during business hours. Many organizations also find themselves paying for oversized instances, chosen conservatively “just to be safe” during the migration. Unnecessary data replication across regions and inefficient database queries further compound the problem, silently driving up costs month after month.
Quick wins in cost optimization can be achieved through basic resource scheduling—for example, automatically shutting down development and test environments during off-hours. Right-sizing instances based on actual usage metrics, rather than theoretical requirements, often yields immediate savings. Organizations can also see rapid returns by cleaning up unused resources like orphaned volumes and outdated snapshots, or by moving infrequently accessed data to cheaper storage tiers.
Long-term cost optimization, however, requires a more strategic approach. This might involve re-architecting applications to take advantage of cloud-native pricing models, implementing infrastructure as code with built-in cost controls, or developing sophisticated automated scaling policies that balance performance with cost.
Performance bottlenecks
Performance issues often emerge as one of the first signs that a lift-and-shift migration needs refinement. Applications that make excessive API calls or databases that perform full table scans instead of indexes can create frustrating user experiences. High latency frequently occurs when applications access data across regions, and memory leaks—rarely noticeable in short-running on-premises environments—become major issues in long-running cloud deployments.
Immediate performance improvements can often be achieved through relatively simple measures. Implementing basic caching, adding missing database indexes, and optimizing the most problematic database queries can provide quick relief. For content-heavy applications, enabling a CDN for static content delivery can dramatically improve user experience without requiring significant architectural changes.
The path to optimal performance, however, often involves more fundamental changes to application architecture. Converting monolithic applications to microservices, implementing efficient data partitioning strategies, and redesigning for asynchronous processing can unlock the full performance potential of cloud platforms.
Limited scalability
Scalability limitations often become apparent during peak usage periods, when applications can’t efficiently handle increased load. This commonly occurs with applications that rely on hard-coded configurations or maintain tight coupling between components. Database bottlenecks frequently emerge as a scaling constraint, particularly when applications weren’t designed for horizontal scaling or contain hard dependencies between components.
Short-term scalability improvements can be achieved by implementing basic auto-scaling based on CPU or memory metrics, moving configuration to environment variables, and setting up basic load balancing. For database-bound applications, adding read replicas can provide immediate relief for read-heavy workloads. These solutions, while not perfect, can buy time for more comprehensive improvements.
True cloud-native scalability, however, requires deeper architectural changes. This might involve adopting containerization and serverless architectures, implementing sophisticated service discovery mechanisms, or redesigning applications to scale horizontally. While these changes require more investment, they provide the foundation for truly elastic cloud operations.
Security vulnerabilities
Security challenges in first-generation cloud migrations often stem from attempting to replicate on-premises security models in the cloud. This frequently results in over-privileged service accounts, unencrypted data, and insufficient network segmentation. Many organizations also struggle with proper audit logging and access controls, creating compliance risks and security vulnerabilities.
Quick security improvements can be achieved by enabling basic cloud security features and implementing proper access controls. Encrypting sensitive data, setting up basic security monitoring, and configuring network security groups can address the most pressing security concerns. While these measures don't provide comprehensive protection, they can significantly reduce risk while more robust solutions are developed.
A comprehensive cloud security strategy requires a more fundamental approach. This typically involves implementing zero-trust architecture, setting up advanced threat detection systems, and designing systems with regulatory compliance in mind from the ground up. While more time-consuming to implement, these measures provide the security foundation necessary for modern cloud operations.
Operational complexity
The operational burden of managing cloud infrastructure often catches organizations by surprise. Manual deployment processes, inconsistent environments, and poor visibility into system health can make routine maintenance and troubleshooting unnecessarily complex. This complexity is often exacerbated by inadequate documentation and unclear procedures for handling incidents.
Initial improvements to operational efficiency can be achieved through basic monitoring and alerting, documented deployment procedures, and centralized logging. Creating basic runbooks and establishing clear backup and recovery procedures can also provide immediate benefits. These measures help teams manage day-to-day operations while building toward more sophisticated solutions.
Long-term operational excellence requires a more systematic approach. Implementing infrastructure as code, adopting GitOps practices, and building comprehensive observability solutions can dramatically improve operational efficiency. While these changes require significant investment in tools and processes, they ultimately reduce operational overhead and improve system reliability.
A tiered approach to cloud optimization
Tier 1: Quick wins (1-2 months)
Focus on immediate improvements with minimal risk:
- Right-size over-provisioned resources
- Implement basic auto-scaling
- Enable cost monitoring and alerts
- Apply fundamental security patches
- Optimize storage tiers
Tier 2: Medium-term optimizations (3-6 months)
Address significant issues without full re-architecting:
- Implement caching layers
- Optimize database performance
- Set up proper monitoring and logging
- Enhance security configurations
- Automate routine maintenance
Tier 3: Strategic re-architecting (6+ months)
For systems where business value justifies the investment:
- Convert to microservices architecture
- Implement containerization
- Adopt serverless computing where appropriate
- Build comprehensive CI/CD pipelines
- Develop cloud-native security architecture
Practical steps for immediate improvement
While comprehensive cloud optimization might be a long-term goal, organizations can achieve significant improvements through a structured approach to immediate enhancements. The key is to focus on high-impact, low-risk changes that can be implemented without disrupting existing operations. Here’s a practical roadmap for getting started:
1. Assessment and prioritization
- Identify highest-cost resources
- Map performance bottlenecks
- List security vulnerabilities
- Prioritize based on business impact vs. effort
2. Quick wins implementation
- Start with automated resource scheduling
- Implement basic monitoring
- Enable native cloud security features
- Optimize instance sizes
- Clean up unused resources
3. Ongoing optimization
- Monitor and adjust resource allocation
- Track cost trends
- Document performance improvements
- Plan for longer-term enhancements
Leveraging AI in your cloud migration
Artificial intelligence has emerged as a powerful ally in cloud optimization, offering ways to accelerate improvements even with limited resources. Modern AI tools can handle many of the complex, time-consuming tasks that traditionally required dedicated cloud architects or DevOps teams, making sophisticated optimization accessible to organizations of all sizes.
Organizations can achieve rapid improvements through AI-powered tools that handle everything from resource management to cost optimization. These systems excel at identifying immediate optimization opportunities—detecting idle resources, establishing baseline performance metrics, and flagging anomalies—while continuously analyzing usage patterns to automatically adjust resource allocation in real time. By implementing these basic AI capabilities, teams can quickly establish more efficient operations without requiring extensive expertise or manual intervention.
More sophisticated AI applications can transform cloud operations through predictive scaling, nuanced anomaly detection, and intelligent infrastructure management. These advanced systems can anticipate resource needs based on historical patterns, identify potential issues across thousands of metrics before they impact operations, and automatically optimize everything from code deployment to database queries—all while balancing performance, cost, and reliability requirements.
Making the business case for further investment
Sometimes quick fixes aren’t enough. Here’s how to build the case for deeper optimization:
Track and document:
- Current cloud costs
- Performance metrics
- Security incidents
- Customer complaints
- Lost business opportunities
Calculate ROI for:
- Resource optimization
- Performance improvements
- Security enhancements
- Developer productivity
- Business agility
Find your cloud’s silver lining
Cloud optimization isn’t one-size-fits-all. While full re-architecting might be ideal, practical constraints often require a more measured approach. Start with quick wins that deliver immediate value, then build the case for deeper optimization where it matters most.
Remember:
- Focus on improvements that align with business priorities
- Balance quick fixes with strategic improvements
- Document wins to build support for further optimization
- Keep the door open for future re-architecting when timing and resources align
The key is finding the right balance between immediate needs and long-term optimization—ensuring your cloud infrastructure supports your business effectively today while leaving room for tomorrow's improvements.
