Dynamic Reservation Zones for Cloud Resource Allocation
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Solution Overview
Problem
Cloud computing platforms face inefficiencies in resource allocation due to static quotas, leading to situations where tenants are denied resources despite available capacity, as the assumption of peak usage is often incorrect, resulting in stockouts and underutilization of resources.
Innovation Solution
Implementing a dynamic system that determines reservation zones based on relative priorities and usage forecasts, allowing higher-priority tenants to reserve more capacity while adjusting zone limits to optimize resource utilization across multiple priority tiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static quotas are applied to limit tenant resource consumption, then resource allocation control is simplified and guaranteed, but resource utilization efficiency deteriorates due to denial of resources even when excess capacity is available
Solution Approach 1:
The patent applies dynamics by transitioning from static quotas to dynamic reservation zones that automatically adjust based on real-time capacity availability. The system continuously monitors unused capacity and modifies the boundaries of reservation zones, allowing higher-priority tenants to access more resources when capacity is abundant while maintaining control when capacity is constrained. This dynamic adaptation resolves the contradiction by making resource allocation both controllable and efficient simultaneously.
Solution Approach 2:
The system changes the parameter of resource allocation from fixed quota values to variable reservation zone boundaries. By dynamically adjusting the size and position of reservation zones based on capacity metrics, the system allows resource allocation parameters to adapt to changing conditions. This parameter transformation enables the system to maintain reliability through controlled access while improving productivity by utilizing available excess capacity.
2Reliability
If higher-priority tenants are guaranteed more capacity through larger reservation zones, then service level agreement compliance is improved, but resource availability for lower-priority tenants deteriorates during peak demand
Solution Approach 1:
The system uses dynamics to create reservation zones that automatically expand or contract based on real-time capacity conditions. During low-demand periods, reservation zones for higher-priority tenants can expand to ensure their service level agreements are met. During high-demand periods, the zones dynamically adjust to preserve capacity for lower-priority tenants. This dynamic behavior maintains both reliability for high-priority tenants and adaptability for the overall system.
Solution Approach 2:
The patent segments the total capacity into multiple reservation zones corresponding to different priority levels. Each zone is dynamically sized based on capacity availability and priority weights. This segmentation allows the system to guarantee capacity to higher-priority tenants within their zone while preserving capacity in lower-priority zones when overall capacity is constrained, thus maintaining both service level agreement compliance and system-wide adaptability.
3Productivity
If reservation zone boundaries are dynamically adjusted based on usage forecasts and capacity availability, then resource utilization is optimized, but system complexity increases due to continuous monitoring and adjustment mechanisms
Solution Approach 1:
The system applies self-service by implementing automated monitoring and adjustment mechanisms that operate without manual intervention. The reservation zone boundaries are dynamically adjusted based on automated capacity monitoring and usage forecast analysis. This self-service approach optimizes resource utilization while managing complexity through automation rather than manual processes, allowing the system to adapt continuously with minimal operational overhead.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor capacity usage and automatically adjust reservation zone boundaries in response to observed conditions. The system uses usage forecasts and actual capacity availability as feedback signals to dynamically modify allocation parameters. This feedback-driven approach optimizes resource utilization while managing complexity through closed-loop control rather than complex manual management procedures.
Data Source
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AI summary
Systems, methods, devices, and other techniques for managing a computing resource shared by a set of online entities. A system can receive a request from a first online entity to reserve capacity of the computing resource. The system determines a relative priority of the first online entity and identifies a reservation zone that corresponds to the relative priority of the first online entity. The system determines whether to satisfy the request based on comparing (i) an amount of the requested capacity of the computing resource and (ii) an amount of the portion of unused capacity of the computing resource designated by the reservation zone that online entities having relative priorities at or below the relative priority of the first online entity are permitted to reserve.