Complexities of Capacity Management for Distributed Services

Automation

Learn the complexities of capacity management for distributed services, including considerations for resource utilization, caching, redundancy, and planning for peak utilization, failures, and unexpected changes.

Key takeaways

Solid-state drives are more expensive than hard disk drives, but offer higher input-output operations per second and bandwidth.
Resource utilization doesn’t scale linearly, with software and operating system overhead affecting performance.
Caches can be critical dependencies, but also a source of increased resource usage.
Services should be provisioned for N+2 redundancy, with consideration for regional capacity and latency caches.
Resource planning should include consideration of unknowns, with a tradeoff between efficiency and reliability.
Load testing and capacity planning should be automated and run without human intervention.
Services should be designed to minimize peak utilization, with redundancy and caching to handle peak loads.
Resource allocation should consider the requirements of each service, with consideration of CPU, RAM, storage, bandwidth, and instances.
Capacity planning is hard due to various factors such as peak utilization, redundancy, and caching, as well as the complexity of service dependencies.
Auto-provisioning and capacity planning are necessary for efficient and reliable service operation.
Services should be designed to handle overload and failure modes, with consideration of denial of service attacks and load shedding.
Monitoring and alerting are necessary to detect resource utilization issues before they become critical.
Resource allocation and provisioning should be done in a way that minimizes peak utilization, with consideration of redundancy and caching.
Services should be designed to handle unexpected changes and failures, with consideration of canary deployments and rolling updates.
Resource utilization should be monitored and adjusted regularly to ensure optimal performance and reliability.
Services should be designed to handle varying load patterns, with consideration of batch processing and latency-sensitive loads.
Capacity planning should consider the requirements of each service, with consideration of CPU, RAM, storage, bandwidth, and instances.
Resource allocation and provisioning should be done in a way that minimizes peak utilization, with consideration of redundancy and caching.
Services should be designed to handle unexpected changes and failures, with consideration of canary deployments and rolling updates.

Complexities of Capacity Management for Distributed Services

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