Dynamic Compute Resource Reservation for Cluster Response Time
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Solution Overview
Problem
Current grid and cluster resource management systems lack the flexibility and efficiency to manage resources over time, leading to inefficiencies and increased administrative overhead due to the inability to reserve resources dynamically and adapt to changing needs and performance goals.
Innovation Solution
A system and method for dynamically controlling resource reservations within a compute environment, allowing for the cancellation and re-allocation of resources to improve response time, incorporating advanced reservation technology with dynamic and self-optimizing features, such as dynamic reservations, self-optimizing reservations in time, and reservation masks, to ensure guaranteed quality of service and adapt to resource availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are reserved statically for a job, then quality of service is guaranteed, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource reservation by continuously monitoring resource availability and job requirements, allowing the reservation to adapt its parameters over time. The system evaluates current resource status and modifies reservations dynamically, transforming static reservations into dynamic ones that can respond to changing conditions while maintaining quality of service guarantees.
Solution Approach 2:
The system enables self-optimizing reservations that automatically adjust their own parameters based on monitored resource availability and performance metrics. The reservation mechanism serves itself by evaluating whether to expand, contract, or maintain its resource allocation without external intervention, improving overall resource utilization while preserving service quality.
2Loss of time
If resources are reserved in advance, then response time is improved, but adaptability to changing needs deteriorates
Solution Approach 1:
The patent creates dynamic reservations that are established in advance to ensure quick resource availability, yet remain flexible to adapt to changing job requirements. The system continuously monitors resource status and can modify the reservation parameters, allowing it to respond to new needs while maintaining the advance reservation benefit of reduced response time.
Solution Approach 2:
The system changes reservation parameters dynamically based on monitored conditions. The reservation can adjust its resource allocation, time frame, and other parameters to match evolving job requirements, thereby maintaining both the advance preparation benefit and the adaptability to changing needs.
3Ease of operation
If multiple layers of schedulers are used, then resource coordination is improved, but system complexity deteriorates
Solution Approach 1:
The patent merges the functions of multiple scheduler layers into a unified reservation management system. By combining the resource coordination capabilities of grid schedulers and cluster schedulers into an integrated dynamic reservation framework, the system maintains effective multi-layer coordination while reducing overall system complexity through functional consolidation.
Solution Approach 2:
The dynamic reservation system serves multiple functions across different scheduler layers simultaneously. It provides resource coordination, quality of service guarantee, and self-optimization capabilities that were previously distributed across multiple specialized components, thereby simplifying the system architecture while maintaining comprehensive resource coordination.
4Reliability
If resources are permanently partitioned, then quality of service is maintained, but resource flexibility deteriorates
Solution Approach 1:
The patent replaces permanent static partitions with dynamic reservations that can adjust their boundaries and resource allocation over time. The system maintains quality of service by ensuring reserved resources are available when needed, while simultaneously improving flexibility by allowing reservations to expand or contract based on actual resource availability and changing job requirements.
Solution Approach 2:
The system changes the parameters of resource partitions dynamically rather than maintaining fixed partitions. Reservation parameters such as resource quantity, time frame, and allocation can be modified to optimize both service quality and flexibility, allowing the system to adapt to changing conditions while preserving quality of service guarantees.
Data Source
AI summary
A system and method of dynamically controlling a reservation of resources within a cluster environment to maximize a response time are disclosed. The method embodiment of the invention comprises receiving from a requestor a request for a reservation of resources in the cluster environment, reserving a first group of resources, evaluating resources within the cluster environment to determine if the response time can be improved and if the response time can be improved, then canceling the reservation for the first group of resources and reserving a second group of resources to process the request at the improved response time.