Dynamic Roll-Back Reservations for Compute Resource Management
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Solution Overview
Problem
Current grid and cluster resource management systems face inefficiencies in allocating and managing compute resources due to multiple layers of schedulers, heterogeneous resource sharing, and the inability to dynamically adjust reservations to meet changing performance goals, leading to management overhead and fragmentation.
Innovation Solution
The introduction of a dynamic roll-back reservation system that allows for the modification of resource allocations in both time and space based on current service levels and utilization metrics, enabling more efficient use of compute resources by prioritizing resources for jobs that threaten to violate quality of service targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple layers of schedulers are used in grid and cluster resource management, then resource allocation coverage is improved, but device complexity and management overhead increase
Solution Approach 1:
The system segments the scheduling function into distinct layers: a first scheduler at the cluster level and a second scheduler at the grid level. Each scheduler operates independently within its domain, managing resources locally before coordination occurs at higher levels. This segmentation reduces the complexity burden on any single scheduler while maintaining comprehensive resource allocation coverage across the entire grid infrastructure.
2Productivity
If resources are shared heterogeneously across multiple users and projects, then resource utilization efficiency is improved, but measurement precision and monitoring difficulty increase
Solution Approach 1:
The patent introduces a resource broker as an intermediary component that sits between the heterogeneous resources and the schedulers. The broker abstracts and standardizes resource information, providing a unified view of resource status, capacity, and availability. This intermediary layer enables precise monitoring and measurement of diverse resources without requiring complex direct monitoring of each individual resource, thus maintaining measurement precision while enabling efficient heterogeneous resource sharing.
3Reliability
If static reservations are made for compute resources, then service level guarantees are improved, but adaptability to changing performance goals deteriorates
Solution Approach 1:
The system implements dynamic reservations that can be modified in real-time based on changing conditions. The reservation parameters, including resource allocation amounts and time windows, are not fixed but can be adjusted dynamically. This allows the system to maintain service level guarantees through automated roll-back mechanisms while simultaneously adapting to changing performance goals and utilization patterns, resolving the contradiction between static reliability and dynamic adaptability.
4Reliability
If roll-back reservations are implemented to guarantee service levels, then reliability is improved, but loss of time for reservation modification increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing roll-back reservation policies and thresholds before service level violations occur. When utilization patterns indicate potential service level breaches, the system has pre-configured mechanisms ready to automatically modify reservations without requiring time-consuming manual intervention. This preliminary preparation ensures reliable service level guarantees while minimizing the time loss associated with reservation modifications.
Data Source
AI summary
A systems, method and computer-readable media are disclosed for providing a dynamic roll-back reservation mask in a compute environment. The method of managing compute resources within a compute environment includes, based on an agreement between a compute resource provider and a customer, creating a roll-back reservation mask for compute resources which slides ahead of current time by a period of time. Within the roll-back reservation mask, the method specifies a subset of consumers and compute resource requests which can access compute resources associated with the roll-back reservation mask and, based on received data, the method dynamically modifies at least one of (1) the period of time the roll-back reservation mask slides ahead of current time and (2) the compute resources associated with the roll-back reservation mask.