ElastiCache Optimization

Jetscale provides AI-powered cost optimization for Amazon ElastiCache including both Redis and Memcached clusters. Our specialized agents analyze your caching workloads to identify right-sizing opportunities, node type migrations, and configuration improvements.

Overview

Jetscale optimizes ElastiCache resources by analyzing:

• Node utilization: CPU, memory, network, and connection patterns
• Cache performance: Hit rates, eviction patterns, swap usage
• Cost analysis: Current spending vs. optimal configuration
• High availability: Multi-AZ configurations and replication topology

Supported ElastiCache Types

Redis Clusters

Redis is our primary optimization target for feature-rich caching with persistence and replication.

Cluster Modes:

Redis Cluster Mode Disabled:

ElastiCacheReplicationGroup (Optimization Target)
├── CacheNode (Primary) - Billable
├── CacheNode (Replica) - Billable
└── CacheNode (Replica) - Billable

Redis Cluster Mode Enabled:

ElastiCacheReplicationGroup (Optimization Target)
├── NodeGroup (Shard 1)
│   ├── CacheNode (Primary) - Billable
│   └── CacheNode (Replica) - Billable
├── NodeGroup (Shard 2)
│   ├── CacheNode (Primary) - Billable
│   └── CacheNode (Replica) - Billable

What We Optimize:

• Node type right-sizing: Match actual memory and CPU workload (all nodes in cluster use same type)
• Graviton migration: Switch to ARM-based instances (r7g, m7g, r6g, m6g) for 10-40% savings
• Engine version upgrades: Recommend latest Redis versions for performance and features
• Multi-AZ configuration: Disable Multi-AZ for ~50% savings when high availability isn't critical
• Reserved node recommendations: Up to 55% savings with 1-year or 3-year commitment

Important: All nodes in a Redis cluster must use the same node type. Jetscale sizes for the node with highest resource usage to ensure adequate performance across all cluster members.

Memcached Clusters

Traditional Memcached clusters are optimized as a collection of independent nodes.

Cluster Architecture:

ElastiCacheCluster (Optimization Target)
├── CacheNode - Billable
├── CacheNode - Billable
└── CacheNode - Billable

What We Optimize:

• Node type right-sizing: Match memory and CPU to workload
• Graviton migration: Switch to ARM-based instances (m7g, m6g) for 10-40% savings
• Node count optimization: Adjust number of nodes based on actual usage

Key Differences from Redis:

• No replication (each node is independent)
• No persistence (purely in-memory)
• Simpler scaling model (add/remove nodes)
• Generally lower cost than Redis

ElastiCache Node Types

Jetscale considers all AWS ElastiCache node types when optimizing:

Memory Optimized (R-family)

R7g (Graviton3) - Latest generation, ARM-based

• Best price/performance ratio
• 40% better price/performance than R6g
• Use for: Redis with high memory needs, low-latency caching

R6g (Graviton2) - Previous ARM generation

• 20% cost savings vs. R5
• Excellent performance
• Use for: Redis requiring large memory footprint

R5 - Intel-based

• Legacy option (migrate to R6g/R7g for savings)
• Use for: x86-specific requirements (rare)

General Purpose (M-family)

M7g (Graviton3) - Latest generation, balanced

• Best for workloads with balanced CPU and memory needs
• 40% better price/performance than M6g

M6g (Graviton2) - ARM-based

• 20% cost savings vs. M5
• Use for: Balanced Redis/Memcached workloads

M5 - Intel-based

• Legacy option (migrate to M6g/M7g)

Burstable (T-family)

T4g (Graviton2) - Low-cost burstable

• Use for: Development, testing, small caches
• Baseline CPU with burst credits
• Lowest cost option
• Caution: Monitor CPU credit balance

T3 - Intel-based burstable

• Legacy option (migrate to T4g)

Node Sizing

Each node type comes in multiple sizes:

How Jetscale Optimizes ElastiCache

1. Data Collection

Jetscale analyzes multiple data sources:

CloudWatch Metrics (14-day rolling window):

Redis Metrics:

• CPUUtilization - Node CPU usage
• DatabaseMemoryUsagePercentage - Memory used for data
• BytesUsedForCache - Actual cache data size
• FreeableMemory - Available memory
• NetworkBytesIn / NetworkBytesOut - Network throughput
• CacheHits / CacheMisses - Cache efficiency
• CurrConnections - Active connections
• SwapUsage - Memory pressure indicator (should be zero)
• EngineCPUUtilization - Redis process CPU (key metric)
• Evictions - Items evicted due to memory pressure
• ReplicationLag - Replica lag time

Memcached Metrics:

• CPUUtilization - Node CPU usage
• BytesUsedForCacheItems - Memory used for items
• FreeableMemory - Available memory
• NetworkBytesIn / NetworkBytesOut - Network throughput
• CurrConnections - Active connections
• Evictions - Items evicted due to memory pressure
• GetHits / GetMisses - Cache efficiency
• BytesReadIntoMemcached / BytesWrittenOutFromMemcached

ElastiCache API Data:

• Node type and engine version
• Number of nodes/shards
• Multi-AZ status
• Automatic failover configuration
• Parameter group configuration
• Maintenance window settings

Cost Explorer Data:

• Current monthly spend per cluster
• Historical cost trends
• Reserved node utilization

AWS Compute Optimizer (if enabled):

• AWS-generated right-sizing recommendations
• Performance risk assessments

2. Analysis

Our AI agents perform deep analysis:

Memory Utilization:

• Peak vs. average memory usage
• Eviction patterns (indicates memory pressure)
• Swap usage (critical indicator - should be zero)
• Memory fragmentation patterns
• Headroom calculation for safe downsizing

CPU Utilization:

• Engine CPU vs. total CPU (Redis)
• Peak vs. average CPU usage
• CPU credit balance (T-family instances)
• Time-of-day patterns

Cache Performance:

• Cache hit rate (target: >95% for healthy cache)
• Eviction rate (indicates insufficient memory)
• Connection patterns
• Network saturation points

Cost Modeling:

• Current cost breakdown (compute, data transfer)
• Projected cost for alternative configurations
• Reserved node opportunities
• Multi-AZ premium analysis
• Graviton migration savings

Risk Evaluation:

• Headroom calculation (20% buffer above peak recommended)
• Performance degradation probability
• Availability impact assessment
• Failover capability impact

3. Recommendations

Jetscale generates specific, actionable recommendations:

Node Type Right-Sizing

Example Recommendation:

Resource: production-redis-cache
Current: 2x cache.r5.xlarge (13.1 GB RAM each, 4 vCPUs)
Recommended: 2x cache.r5.large (6.4 GB RAM each, 2 vCPUs)

Cost Impact:
- Current: $500/month
- Projected: $250/month
- Savings: $250/month (50%), $3,000/year

Performance Analysis:
- Peak memory usage: 40% (5.2 GB per node)
- Average memory usage: 30% (3.9 GB per node)
- Peak CPU: 25%
- Cache hit rate: 97.5%
- No evictions detected
- Recommended provides 23% headroom above peak

Risk: Low - Adequate headroom maintained, no memory pressure

Graviton Migration (Redis)

Example Recommendation:

Resource: api-cache-cluster
Current: 3x cache.r5.large (Intel-based)
Recommended: 3x cache.r6g.large (Graviton2-based)

Cost Impact:
- Current: $375/month (3 nodes @ $125/month each)
- Projected: $300/month (3 nodes @ $100/month each)
- Savings: $75/month (20%), $900/year

Performance Analysis:
- Same memory: 6.4 GB per node
- Same vCPUs: 2 per node
- Graviton2 offers equivalent or better performance
- Redis 6.2+ fully compatible with ARM64
- No application changes required

Risk: Very Low - Proven Graviton performance with Redis
Note: Requires engine version 5.0.6+ for Graviton support

Multi-AZ Optimization

Example Recommendation:

Resource: staging-memcached-cluster
Current: 4x cache.m5.large with Multi-AZ enabled
Recommended: 4x cache.m5.large with Multi-AZ disabled

Cost Impact:
- Current: $480/month (Multi-AZ premium included)
- Projected: $240/month (Single-AZ)
- Savings: $240/month (50%), $2,880/year

Availability Analysis:
- Environment: Staging/Non-Production
- Current: Multi-AZ (automatic failover)
- Projected: Single-AZ (manual recovery)
- Acceptable for staging workload
- RPO/RTO: Tolerable for non-production

Risk: Low - Non-production environment
Note: Multi-AZ provides automatic failover within minutes

Engine Version Upgrade

Example Recommendation:

Resource: legacy-redis-cache
Current: 3x cache.r5.large, Redis 5.0.6
Recommended: 3x cache.r5.large, Redis 7.0

Cost Impact:
- Current: $375/month
- Projected: $375/month (same cost)
- Savings: $0/month (performance and security improvements)

Benefits:
- Redis 7.0 features: Functions, ACL improvements, Redis Streams enhancements
- Better memory efficiency (5-10% reduction in memory overhead)
- Improved replication performance
- Security patches and bug fixes
- Same cost, better performance

Risk: Low - Backward compatible, test in staging first
Note: Review Redis 7.0 breaking changes documentation

Reserved Node Recommendations

Example Recommendation:

Resource: production-redis-primary
Current: 2x cache.r6g.xlarge, On-Demand pricing
Recommended: 2x cache.r6g.xlarge, 1-Year Reserved (No Upfront)

Cost Impact:
- Current: $600/month (On-Demand)
- Projected: $420/month (Reserved)
- Savings: $180/month (30%), $2,160/year

Analysis:
- Cluster age: 18 months (proven stable workload)
- Utilization: 24/7 production cache
- Reserved term: 1-Year No Upfront
- Break-even: Immediate (monthly savings)
- Commitment: Can sell unused reservations on marketplace

Risk: Very Low - Stable long-running workload
Note: 3-Year Reserved offers 55% savings ($270/month, $390/month savings)

Memcached Node Count Optimization

Example Recommendation:

Resource: session-cache-memcached
Current: 6x cache.m5.large (16 GB total capacity)
Recommended: 4x cache.m5.large (16 GB total capacity by using larger nodes)
Alternative: 4x cache.m6g.xlarge (same total memory, Graviton2)

Cost Impact (Alternative):
- Current: $720/month (6 nodes @ $120/month)
- Projected: $640/month (4 nodes @ $160/month)
- Savings: $80/month (11%), $960/year

Performance Analysis:
- Total memory needed: 12 GB (peak)
- Current distribution: 2 GB per node average
- Recommended: 3 GB per node average
- Fewer nodes = reduced operational complexity
- Network performance per node is higher

Risk: Low - Same total capacity, better efficiency
Note: Test connection pooling with fewer nodes

4. Terraform Generation

For each recommendation, Jetscale generates production-ready Terraform code:

Example: Redis Right-Sizing

# ElastiCache Redis Optimization
# Generated by Jetscale on 2024-01-15
# Recommendation ID: rec_elasticache_001

resource "aws_elasticache_replication_group" "production_redis" {
  replication_group_id       = "production-redis-cache"
  replication_group_description = "Production Redis cache"

  # Previous: cache.r5.xlarge ($500/month)
  # Optimized: cache.r5.large ($250/month)
  # Cost Reduction: 50% ($250/month, $3,000/year)
  #
  # Justification:
  # - Peak memory usage: 40% (5.2 GB per node)
  # - Average memory usage: 30% (3.9 GB per node)
  # - Peak CPU: 25%
  # - Cache hit rate: 97.5%
  # - New node type provides 23% headroom above peak
  # - No evictions or swap usage detected
  node_type = "cache.r5.large"

  engine         = "redis"
  engine_version = "7.0"

  # Cluster configuration
  num_cache_clusters         = 2
  automatic_failover_enabled = true
  multi_az_enabled          = true

  # Network configuration
  subnet_group_name  = var.subnet_group_name
  security_group_ids = var.security_group_ids

  # Maintenance and backup
  maintenance_window         = "sun:05:00-sun:06:00"
  snapshot_window           = "03:00-04:00"
  snapshot_retention_limit  = 5

  # Parameter group for configuration
  parameter_group_name = var.parameter_group_name

  # Encryption
  at_rest_encryption_enabled = true
  transit_encryption_enabled = true
  auth_token_enabled        = true

  # Logging
  log_delivery_configuration {
    destination      = var.cloudwatch_log_group
    destination_type = "cloudwatch-logs"
    log_format       = "json"
    log_type         = "slow-log"
  }

  tags = merge(
    var.tags,
    {
      "jetscale:optimized"      = "true"
      "jetscale:recommendation" = "rec_elasticache_001"
      "jetscale:previous_type"  = "cache.r5.xlarge"
    }
  )
}

Example: Graviton Migration

# ElastiCache Graviton Migration
# Generated by Jetscale on 2024-01-15

resource "aws_elasticache_replication_group" "api_cache_graviton" {
  replication_group_id       = "api-cache-cluster"
  replication_group_description = "API response cache - Graviton2"

  # Previous: cache.r5.large (Intel, $125/month per node)
  # Optimized: cache.r6g.large (Graviton2, $100/month per node)
  # Cost Reduction: 20% ($75/month total, $900/year)
  #
  # Performance:
  # - Same memory: 6.4 GB RAM
  # - Same vCPUs: 2 per node
  # - Graviton2 offers equivalent or better performance
  # - 20% cost savings with no performance trade-off
  #
  # Requirements:
  # - Redis engine 5.0.6+ for Graviton support
  # - No application changes required
  node_type = "cache.r6g.large"

  engine         = "redis"
  engine_version = "7.0"

  num_cache_clusters         = 3
  automatic_failover_enabled = true
  multi_az_enabled          = true

  subnet_group_name  = var.subnet_group_name
  security_group_ids = var.security_group_ids

  maintenance_window        = "sun:05:00-sun:06:00"
  snapshot_window          = "03:00-04:00"
  snapshot_retention_limit = 7

  parameter_group_name = var.parameter_group_name

  at_rest_encryption_enabled = true
  transit_encryption_enabled = true

  tags = merge(
    var.tags,
    {
      "jetscale:optimized"    = "true"
      "jetscale:architecture" = "arm64"
      "jetscale:processor"    = "graviton2"
    }
  )
}

Example: Memcached Optimization

# ElastiCache Memcached Optimization
# Generated by Jetscale on 2024-01-15

resource "aws_elasticache_cluster" "session_cache" {
  cluster_id = "session-cache-memcached"

  # Previous: 6x cache.m5.large ($720/month)
  # Optimized: 4x cache.m6g.xlarge ($640/month)
  # Cost Reduction: 11% ($80/month, $960/year)
  #
  # Analysis:
  # - Total memory needed: 12 GB peak
  # - Fewer nodes reduce operational complexity
  # - Graviton2 provides 20% per-node savings
  # - Better network performance per node
  node_type = "cache.m6g.xlarge"

  engine         = "memcached"
  engine_version = "1.6.17"

  num_cache_nodes = 4
  az_mode        = "cross-az"

  subnet_group_name  = var.subnet_group_name
  security_group_ids = var.security_group_ids

  maintenance_window = "sun:05:00-sun:06:00"

  parameter_group_name = var.parameter_group_name

  tags = merge(
    var.tags,
    {
      "jetscale:optimized"      = "true"
      "jetscale:previous_nodes" = "6"
      "jetscale:previous_type"  = "cache.m5.large"
    }
  )
}

Best Practices

Monitoring After Changes

After applying Jetscale recommendations:

1. First 24 Hours: Monitor key metrics closely
   • Memory utilization and eviction rate
   • CPU utilization (especially EngineCPU for Redis)
   • Cache hit rate (should remain stable)
   • SwapUsage (should be zero)
   • Connection count and latency
2. Week 1: Validate performance
   • Application response times
   • Cache miss rate (should not increase)
   • No evictions under normal load
   • Network throughput within limits
   • Replication lag (Redis replicas)
3. Week 2-4: Confirm cost savings
   • Verify AWS billing reflects expected savings
   • No unexpected data transfer charges
   • Check Reserved node recommendations

Testing Strategy

Pre-Production Testing:

1. Apply changes to dev/staging environment first
2. Run load tests simulating peak traffic
3. Monitor for 48-72 hours under realistic load
4. Validate cache performance metrics
5. Test failover scenarios (Redis Multi-AZ)

Production Rollout:

1. Schedule changes during maintenance windows
2. Use blue/green deployments for zero-downtime (create new cluster, switch traffic)
3. Have rollback plan ready
4. Monitor actively during and after change
5. Keep old cluster running for 24 hours before terminating

Reserved Nodes & Cost Optimization

When to Purchase Reserved Nodes:

• Stable, long-running caches (>1 year expected lifetime)
• After right-sizing (don't reserve oversized nodes)
• When commitment savings > opportunity cost

Jetscale Recommendations:

• We analyze reserved node utilization and suggest optimal purchases
• Consider 1-year over 3-year for flexibility
• No Upfront option provides monthly savings with zero upfront cost
• Partial Upfront offers slightly better savings
• All Upfront offers maximum savings (55% for 3-year Redis)

Reserved Node Savings:

• 1-Year No Upfront: ~30% savings
• 1-Year All Upfront: ~35% savings
• 3-Year No Upfront: ~50% savings
• 3-Year All Upfront: ~55% savings

Common Optimization Patterns

Pattern 1: Over-Provisioned Redis Cluster

Symptoms:

• Memory utilization < 50% consistently
• CPU utilization < 30%
• No evictions
• Cache hit rate >95%

Jetscale Recommendation:

• Downsize node type by 1-2 sizes
• Typical savings: 50-66%
• Risk: Low (maintain 20%+ headroom)

Pattern 2: Missed Graviton Opportunities

Symptoms:

• Using Intel-based instances (r5, m5, r6i)
• Redis 5.0.6+ or Memcached 1.5.16+
• No ARM-specific dependencies

Jetscale Recommendation:

• Migrate to Graviton instances (r5 → r6g, m5 → m6g, or latest r7g/m7g)
• Typical savings: 20-40% with same or better performance
• Requirements: Verify engine version compatibility
• Best for: All modern Redis and Memcached deployments

Pattern 3: Non-Production Multi-AZ

Symptoms:

• Dev/staging/test caches with Multi-AZ enabled
• High availability not required for non-production
• Acceptable downtime tolerance

Jetscale Recommendation:

• Disable Multi-AZ and automatic failover
• Typical savings: 50% on node costs
• Trade-off: Manual recovery vs. automatic failover
• Best for: Development, staging, testing environments

Pattern 4: Legacy Engine Versions

Symptoms:

• Running Redis 5.x or older
• Running Memcached 1.5.x or older
• Missing performance improvements and features

Jetscale Recommendation:

• Upgrade to latest engine version (Redis 7.x, Memcached 1.6.x)
• Typical savings: 5-10% memory efficiency improvement
• Additional benefits: Security patches, new features, better performance
• Risk: Low to Medium (test for breaking changes)

Pattern 5: High Eviction Rate

Symptoms:

• Evictions > 0 consistently
• Memory utilization at 100%
• Cache hit rate declining
• SwapUsage > 0 (critical indicator)

Jetscale Recommendation:

• Upsize to larger node type (more memory)
• Alternative: Add more nodes (Memcached) or shards (Redis Cluster Mode)
• Typical cost increase: 50-100% BUT necessary for performance
• Risk: High if not addressed (cache thrashing, poor performance)

Important: This is a case where Jetscale recommends spending MORE to improve performance and prevent cache failures.

Troubleshooting

Recommendation Concerns

Q: Will downsizing impact cache hit rates?

A: Jetscale maintains 20%+ headroom above peak memory usage. We analyze:

• 99th percentile memory utilization over 14 days
• Eviction patterns
• Cache hit/miss ratios
• SwapUsage (critical indicator of memory pressure)

Recommendations only proceed if:

• No evictions detected in baseline period
• SwapUsage is zero
• Performance risk is Low or Very Low

Q: What about sudden traffic spikes?

A: Our analysis includes:

• P99 (99th percentile) metrics, not just averages
• 20% buffer above peak for unexpected spikes
• Historical spike patterns
• Headroom for growth

Q: How do I test Graviton migration?

A: Recommended approach:

1. Create new Graviton-based cluster in staging
2. Configure application to use both clusters (A/B test)
3. Compare performance metrics for 48-72 hours
4. Full cutover to Graviton cluster once validated
5. Apply to production after staging success

Performance Issues After Optimization

Symptom: Increased cache miss rate

Possible causes:

• Insufficient memory (too aggressive downsizing)
• Evictions occurring
• Key distribution issues (Redis Cluster Mode)

Resolution:

1. Check Evictions metric (should be zero)
2. Check DatabaseMemoryUsagePercentage (should be <80%)
3. Increase node size if memory pressure detected
4. Review key expiration policies

Symptom: High CPU utilization

Possible causes:

• Insufficient CPU for command processing
• Inefficient cache access patterns
• Large key operations

Resolution:

1. Check EngineCPUUtilization (Redis) or CPUUtilization (Memcached)
2. Review slow log for expensive operations
3. Increase node size if CPU consistently >70%
4. Optimize application cache access patterns

Symptom: SwapUsage > 0

CRITICAL ISSUE - Memory pressure detected

Possible causes:

• Node type has insufficient memory
• Memory leak in application
• Key sizes larger than expected

Resolution:

1. Immediately increase node size (memory pressure causes severe performance degradation)
2. Review BytesUsedForCache growth trends
3. Check for memory leaks
4. Implement key eviction policies if appropriate

Symptom: Replication lag increasing (Redis)

Possible causes:

• High write volume
• Network saturation
• Undersized replicas

Resolution:

1. Check ReplicationLag metric
2. Verify network throughput (NetworkBytesOut from primary)
3. Increase node size if replicas can't keep up
4. Consider Redis Cluster Mode for write distribution

Security Considerations

Encryption

Jetscale recommendations preserve existing encryption settings:

• At-rest encryption state maintained
• Transit encryption (TLS) state maintained
• Auth tokens preserved (Redis)
• No changes to KMS keys

Network Security

Configuration preserved:

• VPC and subnet group associations
• Security group rules unchanged
• Private subnet isolation maintained
• No public endpoint changes

Access Control

• Redis AUTH preserved
• Parameter group associations maintained
• IAM authentication settings unchanged
• Compliance requirements respected

Engine Compatibility

Redis Graviton Support

Minimum versions for Graviton:

• Redis 5.0.6+: Supports r6g, m6g (Graviton2)
• Redis 6.2+: Recommended for Graviton2
• Redis 7.0+: Full Graviton3 support (r7g, m7g)

Feature compatibility:

• All Redis features supported on Graviton
• Same clustering capabilities
• Same replication and persistence
• No application code changes required

Memcached Graviton Support

Minimum versions:

• Memcached 1.5.16+: Supports m6g (Graviton2)
• Memcached 1.6.6+: Recommended for Graviton2

Feature compatibility:

• All Memcached features supported
• Same protocol and commands
• No application changes required

Limitations

Not Currently Supported:

• Global Datastore optimization (Redis)
• Backup and restore cost optimization
• Data tiering recommendations (Redis)
• Outpost deployments

Cluster Mode Enabled Limitations:

• Cannot change number of shards without data migration
• Resharding is complex and must be planned separately
• Jetscale optimizes node types but not shard topology

API Integration

Jetscale provides API access for programmatic optimization:

# List ElastiCache recommendations
GET /api/v1/recommendations?resource_type=elasticache

# Get specific recommendation details
GET /api/v1/recommendations/{recommendation_id}

# Approve recommendation (generates Terraform)
POST /api/v1/recommendations/{recommendation_id}/approve

# Retrieve generated Terraform
GET /api/v1/recommendations/{recommendation_id}/terraform

See our API Documentation for complete reference.

IAM Permissions Required

Jetscale requires the following IAM permissions for ElastiCache optimization:

Read permissions (for analysis):

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "elasticache:DescribeCacheClusters",
        "elasticache:DescribeReplicationGroups",
        "elasticache:DescribeCacheParameters",
        "elasticache:DescribeCacheEngineVersions",
        "elasticache:ListTagsForResource",
        "cloudwatch:GetMetricStatistics",
        "cloudwatch:GetMetricData",
        "ce:GetCostAndUsage"
      ],
      "Resource": "*"
    }
  ]
}

Note: Jetscale does not require write permissions. All changes are implemented via generated Terraform code that you review and apply through your existing deployment processes.

Support

Need help with ElastiCache optimization?

• Email: support@jetscale.ai
• Documentation: FAQ
• GitHub Issues: Report a problem

Related Documentation:

• RDS Optimization
• EC2 Optimization
• EBS Optimization