Type-Agnostic Capacity Management in Provider Networks
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Solution Overview
Problem
The management of large-scale computing resources in data centers is complicated by the need to maintain separate pools of hardware for various instance types, leading to disparities in pool utilization and an inability to adapt to changing demand, resulting in high operational costs and complexity.
Innovation Solution
Decoupling hardware infrastructure from specific instance types allows for dynamic allocation based on customer demand, using type-agnostic computer systems that can support multiple instance types, reducing the need for pre-defined pools and improving fleet utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate pools of hardware are maintained for various instance types, then instance type-specific performance is improved, but device complexity and operational costs increase
Solution Approach 1:
The patent implements hardware pools that are not dedicated to specific instance types but can dynamically serve multiple instance types through virtualization. The capacity tracker monitors and manages these universal pools, allowing the same physical hardware to be allocated to different instance types based on current demand, thereby reducing the need for separate dedicated pools while maintaining performance requirements.
Solution Approach 2:
The system dynamically adjusts resource allocation from static, pre-defined pools to flexible, on-demand allocation. The capacity tracker continuously monitors pool usage and enables real-time reconfiguration of hardware resources between different instance types, transforming the infrastructure from a static architecture to a dynamic one that adapts to changing workload requirements.
2Ease of operation
If pre-defined pools are used for different instance types, then resource allocation is simplified, but adaptability to changing demand deteriorates
Solution Approach 1:
The patent replaces static pre-defined pools with dynamic resource pools that can be reconfigured in real-time. The capacity tracker monitors usage patterns and enables automatic adjustment of resource allocation between instance types based on current demand, allowing the system to adapt flexibly to changing workloads while maintaining operational simplicity through automated management.
Solution Approach 2:
The system implements self-service capacity management where the capacity tracker automatically monitors pool usage and facilitates dynamic reallocation of resources without requiring manual intervention. The infrastructure self-adjusts to demand changes by automatically transferring capacity between different instance type allocations, reducing operational complexity while enhancing adaptability.
3Reliability
If separate hardware pools are maintained for each instance type, then instance performance is ensured, but fleet utilization efficiency decreases
Solution Approach 1:
The patent creates universal hardware pools that can serve multiple instance types through virtualization rather than maintaining separate dedicated pools. The capacity tracker manages these shared pools, ensuring that performance requirements are met for different instance types while allowing the same physical resources to be utilized by multiple logical instances, thereby significantly improving overall fleet utilization efficiency.
Solution Approach 2:
The patent merges separate hardware pools for different instance types into unified, shared resource pools. By combining previously segregated hardware resources and managing them through a single capacity tracking system, the infrastructure achieves better resource consolidation and utilization while still providing isolated performance guarantees to different instance types through virtualization technologies.
Data Source
AI summary
Techniques for on demand capacity management in a provider network are described. The provider network includes electronic devices that provide computing-related resources to customers. The unused capacity of these electronic devices—such as processor cores, memory, network bandwidth, etc.—can be used to satisfy a variety of computing needs. Services of the provider network allocate portions of the unused capacity based on customer requests for computing-related resources.