Reserved Capacity Allocation in Cloud Availability Zones
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Solution Overview
Problem
Cloud computing resource providers face challenges in managing and optimizing computing resources, leading to unnecessary expenses due to the lack of efficient capacity reservation and allocation systems, which result in higher costs for customers when using non-reserved computing resources.
Innovation Solution
Implementing a system that allows customers to reserve computing capacity in advance, enabling the cloud computing resource provider to simplify resource planning and reduce costs by allocating reserved resources efficiently across availability zones, thereby minimizing the use of non-reserved resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If customers use non-reserved computing resources in elastic cloud infrastructure, then they gain flexibility to launch and terminate machine instances on demand, but they incur higher marginal costs and the provider experiences inefficient resource utilization
Solution Approach 1:
The system allows customers to perform preliminary actions by reserving computing capacity in advance before actually using it. The resource allocation system tracks reserved capacity and automatically allocates it when customers launch machine instances, ensuring that pre-booked resources are utilized first. This preliminary reservation mechanism enables customers to lock in lower costs while maintaining the flexibility to use resources when needed.
Solution Approach 2:
The resource allocation system implements continuous feedback mechanisms by monitoring both reserved and available computing capacity across availability zones. When a customer launches a machine instance, the system checks feedback from the reserved capacity pool first, and only allocates from non-reserved resources when reserved capacity is insufficient. This feedback-driven allocation ensures optimal utilization of reserved resources and provides customers with cost-saving benefits.
2Productivity
If cloud providers allocate computing resources without a reservation system, then resource planning becomes complex and inefficient, but implementing reservation systems increases system complexity
Solution Approach 1:
The patent introduces a resource allocation system that acts as an intermediary layer between customers and the underlying computing infrastructure. This intermediary automatically manages the complexity of reservation tracking, capacity monitoring, and resource allocation across multiple availability zones. Customers interact with a simplified interface while the intermediary handles the complex coordination of reserved and available resources, improving resource planning efficiency without burdening customers with system complexity.
Solution Approach 2:
The resource allocation system implements self-service mechanisms by automatically tracking reserved capacity, monitoring availability zones, and allocating resources without manual intervention. The system autonomously manages the complexity of reservation fulfillment by continuously updating its internal state based on customer actions and resource availability, thereby improving planning efficiency while keeping the customer-facing interface simple.
3Loss of energy
If reserved computing capacity is not utilized effectively, then customers pay for capacity they don't need, but over-utilization of reserved resources may lead to insufficient available resources during peak demand
Solution Approach 1:
The resource allocation system dynamically adjusts resource allocation based on real-time conditions. It continuously monitors the utilization of reserved capacity across availability zones and automatically shifts allocation strategies. When reserved resources are under-utilized, the system allows other customers to access available capacity. When demand peaks and reserved resources are fully utilized, the system seamlessly transitions to allocating non-reserved resources, ensuring both efficient utilization and reliable service availability.
Solution Approach 2:
The system implements a universal resource pool that serves multiple functions: it tracks reserved capacity for cost-effective allocation, monitors available capacity for immediate fulfillment, and dynamically balances both to ensure reliability during peak demand. This multi-functional approach allows the same resource allocation mechanism to handle both cost optimization and service reliability requirements simultaneously across different availability zones.
Data Source
AI summary
Disclosed are various embodiments for allocating computing resources according to reserved capacity. In one or more computing devices, a reserved machine instance is designated for a user in one of several zones of multiple networked computing devices. The networked computing devices include multiple machine instances in each of the zones. A request is obtained from the user to allocate a machine instance within the networked computing devices. A zone is selected for allocating the machine instance from the zones based at least in part on the zone that is associated with the reserved machine instance.