IPU Workload Migration for SLA-Aware Resource Allocation
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Solution Overview
Problem
Existing hardware platforms for time-sensitive applications often fail to meet service level agreements (SLAs) due to inefficient resource utilization and limited monitoring capabilities, leading to potential catastrophic failures and unnecessary resource consumption.
Innovation Solution
Implementing infrastructure processing units (IPUs) with tracking, migration, and telemetry circuitry to monitor and manage workload stages, allowing for real-time prediction and allocation of resources to ensure SLA compliance, and migrating workloads to available systems if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardware resources are dedicated to time-sensitive applications to ensure SLA compliance, then reliability is improved, but resource utilization efficiency deteriorates due to idle hardware when applications are not running
Solution Approach 1:
The system dynamically allocates hardware resources to different workload stages based on real-time monitoring of SLA compliance. When a stage is not meeting its performance expectations, the system automatically migrates that stage to a different compute device, transforming static dedicated hardware into dynamic shared resources that adapt to changing demands.
Solution Approach 2:
The system changes the operational parameters of hardware resources by monitoring performance metrics and transitioning workloads between different execution environments. This allows the same hardware to serve multiple applications at different times, optimizing both reliability and resource utilization through parameter-based decision making.
2Productivity
If workload stages are distributed across multiple compute devices to improve resource utilization, then productivity is improved, but system complexity increases due to monitoring and coordination requirements
Solution Approach 1:
The system implements self-service monitoring where each compute device autonomously tracks its own workload stage performance and reports to the network interface. This decentralized approach eliminates the need for complex centralized monitoring infrastructure, reducing system complexity while maintaining high resource utilization through automated performance tracking.
Solution Approach 2:
The system uses feedback loops where performance data from distributed workload stages continuously flows back to the network interface, which automatically makes migration decisions. This closed-loop control simplifies coordination complexity by using automated feedback-driven decision making rather than manual or complex algorithmic coordination.
3Measurement precision
If infrastructure processing units implement comprehensive tracking and telemetry circuitry to monitor workload performance, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The network interface acts as an intermediary that consolidates monitoring functions. Rather than each component having complex tracking capabilities, the network interface receives simplified performance data from workload stages and handles the complex telemetry aggregation and analysis, reducing device complexity while maintaining high measurement precision through centralized intelligent processing.
Data Source
AI summary
System and techniques for infrastructure managed workload distribution are described herein. An infrastructure processing unit (IPU) receives a workload that includes a workload definition. The workload definition includes stages of the workload and a performance expectation. The IPU provides the workload, for execution, to a processing unit of a compute node to which the IPU belongs. The IPU monitors execution of the workload to determine that a stage of the workload is performing outside of the performance expectation from the workload definition. In response, the IPU modifies the execution of the workload.