Resource Hints Controller for Virtual Machine Allocation
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Solution Overview
Problem
Conventional methods for resource allocation in virtualized datacenters are reactive and fail to promptly address resource starvation situations, leading to performance issues and cascaded problems across dependent VMs and business services.
Innovation Solution
A resource hints controller dynamically adjusts resource allocation by increasing processing unit, memory unit, and storage unit resources based on threshold levels and embedded resource control hints, proactively managing resource demands to prevent spikes and outages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extrinsic monitoring based techniques are used to repair resource starvations, then resource allocation can be adjusted, but the response time is insufficient and damage is already done to the system
Solution Approach 1:
The patent applies preliminary action by having the virtual machine send a resource control request to the resource hints controller before the resource-demanding task actually executes. This allows the controller to proactively allocate additional resources in advance, preventing resource starvation before it occurs rather than reacting after the problem has manifested. The embedded resource control hints in the code enable this ahead-of-time resource reservation.
2Productivity
If resources are increased for one VM server to handle resource demanding tasks, then that VM can execute tasks successfully, but dependent VM servers in other tiers experience cascaded problems
Solution Approach 1:
The patent implements feedback by having the resource hints controller monitor resource usage metrics from multiple virtual machines across different tiers and dynamically adjust resource allocation based on actual system conditions. The controller receives resource control requests from VMs and uses feedback from resource usage monitoring to make intelligent allocation decisions that maintain system-wide stability while enabling task execution where needed.
Solution Approach 2:
The resource hints controller serves multiple functions: it receives resource control requests from individual VMs, monitors resource usage across the entire system, makes allocation decisions based on system-wide conditions, and manages resources for multiple tiers simultaneously. This multi-functional approach allows coordinated resource management that prevents cascaded failures while maintaining productivity.
3Device complexity
If conventional resource allocation is used, then system complexity is low, but the system cannot respond to sudden resource spikes in time
Solution Approach 1:
The patent applies self-service by enabling virtual machines to autonomously detect their own resource needs and send resource control requests to the resource hints controller. The embedded resource control hints in the application code allow the VM to self-identify when additional resources are needed, eliminating the need for complex external monitoring and manual intervention while achieving rapid response to resource demands.
Data Source
AI summary
The embodiments may provide a data processing apparatus for controlling service resource allocation. The data processing apparatus including a resource hints controller configured to obtain a resource control request before a task is to be executed on a virtual machine having resources allocated to a processing unit, a memory unit and a storage unit. The resource hints controller is configured to obtain a usage of the resources allocated to at least one of the processing unit, the memory unit and the storage unit of the virtual machine, and increase the resources allocated to the at least one of the processing unit, the memory unit and the storage unit in response to the resource control request based on the usage being equal to or above a threshold level.