Dynamic Resource Allocation Module for Multi-Tenant Cloud Systems
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Solution Overview
Problem
In multi-tenant computing environments, existing technologies face challenges in dynamically and efficiently allocating shared hardware resources to virtual machines based on varying workload demands and service level agreements, leading to potential resource bottlenecks and inefficiencies.
Innovation Solution
Implementing a hardware resource allocation module that dynamically allocates and manages shared hardware resources such as processing, memory, and I/O resources based on service level agreements, allowing for reallocation of unused resources during high workload periods and enforcing resource usage constraints through hardware-level enforcement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dedicated computing resources are allocated to each tenant in a multi-tenant environment, then service level agreement compliance is improved, but resource utilization efficiency deteriorates due to static allocation during varying workload demands
Solution Approach 1:
The patent implements dynamic resource allocation by introducing a resource allocation module that monitors workload demands and service level agreement compliance in real-time, adjusting the amount of computing resources allocated to each virtual machine dynamically. This resolves the contradiction by making the allocation system adaptive rather than static, allowing resources to be guaranteed when needed (improving reliability) while being released when not needed (improving productivity).
Solution Approach 2:
The system changes the allocation parameters dynamically based on monitored workload and compliance metrics. The resource allocation module adjusts resource allocation parameters (such as CPU shares, memory allocation) based on changing conditions, allowing the system to transition between guaranteed allocation modes and best-effort allocation modes to optimize both reliability and productivity.
2Productivity
If computing resources are dynamically reallocated based on workload demands, then resource utilization efficiency is improved, but service level agreement compliance may deteriorate due to potential resource bottlenecks
Solution Approach 1:
The patent implements a feedback mechanism where the resource allocation module continuously monitors both workload demands and service level agreement compliance metrics. When compliance thresholds are approached or violated, the system receives feedback and adjusts allocations accordingly. This closed-loop control ensures that dynamic reallocation improves productivity while maintaining reliability through continuous monitoring and adjustment.
Solution Approach 2:
The system performs preliminary actions by pre-allocating guaranteed minimum resources to each tenant based on their service level agreements before workload demands arise. This baseline allocation ensures compliance is maintained even during peak demands, while allowing dynamic reallocation of excess resources to improve overall utilization efficiency.
3Manufacturing precision
If hardware-level resource constraints are enforced, then resource allocation accuracy is improved, but system complexity increases due to hardware-level enforcement mechanisms
Solution Approach 1:
The patent introduces a resource allocation module as an intermediary software layer between the virtual machines and the hardware resources. This module enforces resource constraints through software-based mechanisms (such as cgroups, namespaces, or resource limits in the hypervisor) rather than requiring complex hardware-level enforcement. This achieves accurate resource allocation (improving manufacturing precision) while avoiding the complexity of hardware modifications (reducing device complexity).
Data Source
AI summary
Techniques are described for allocating computing resources to a task from a shared hardware structure. The techniques may involve receiving a request to execute a task for a tenant on shared hardware resources, and determining a set of computing resources for allocation to the task based on a service level agreement associated with the tenant. The set of computing resources can be allocated to the task based on the service level agreement associated with the tenant. In some aspects, one or more performance counters associated with one or more of the computing resources can be monitored to determine an activity level for the one or more computing resources during execution of the task, and one or more allocations of the computing resources for execution of the task can be adjusted based on the activity level for the one or more computing resources.