Serverless HPC Preemptive Scheduling via Workload Migration
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Solution Overview
Problem
Traditional High Performance Computing (HPC) clusters are expensive, have high operational costs, and require sophisticated workload management software, leading to long wait times and lost productivity due to their inelastic nature and lengthy procurement cycles. Additionally, cloud computing for HPC is not economical for steady-state consumption, and deploying optimized clusters in the cloud is a complex process requiring deep knowledge of cloud APIs and scaling mechanisms.
Innovation Solution
A serverless HPC architecture that integrates an infrastructure manager and a workload manager to facilitate the execution of diverse HPC workloads in cloud computing environments. This architecture provides a consistent and native HPC environment suitable for demanding applications, leverages ephemeral machine instances when possible, and includes mechanisms for handling interruptions, thereby improving efficiency and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If traditional HPC clusters are used, then computing performance is improved, but operational cost and device complexity increase
Solution Approach 1:
The patent employs ephemeral machine instances that can be rapidly provisioned and terminated. These temporary computing resources are allocated for specific workloads and automatically released after use, eliminating the need for permanent, complex HPC cluster infrastructure. The system manages these short-lived resources through automated orchestration, reducing operational complexity while maintaining high computing performance when needed.
2Power
If traditional HPC clusters are used, then computing performance is improved, but wait time and productivity are worsened due to procurement cycles
Solution Approach 1:
The system pre-provisions ephemeral machine instances and maintains them in a ready state before actual workloads are submitted. The infrastructure manager continuously monitors resource availability and pre-allocates computing capacity, so when workloads arrive, they can be immediately assigned to available instances without undergoing lengthy procurement or setup cycles.
3Adaptability or versatility
If cloud computing is used for HPC, then scalability is improved, but economy for steady-state consumption deteriorates
Solution Approach 1:
The system dynamically adjusts resource allocation based on workload characteristics. For steady-state workloads, it allocates resources efficiently to minimize cost, while for bursty or intermittent workloads, it leverages ephemeral instances for scalability. The workload manager analyzes job requirements and automatically selects the most cost-effective resource allocation strategy, combining the scalability of cloud computing with economic efficiency for continuous operations.
4Loss of energy
If ephemeral machine instances are used, then cost efficiency is improved, but reliability deteriorates due to unpredictable interruptions
Solution Approach 1:
The system implements checkpointing mechanisms that periodically save workload state to persistent storage before potential interruptions occur. The infrastructure manager monitors ephemeral instance health and proactively migrates workloads to new instances before failures happen. This cushioning approach ensures that even if interruptions occur, workload progress is preserved and can be resumed without loss, maintaining reliability while using cost-effective ephemeral resources.
5Adaptability or versatility
If cloud HPC infrastructure is deployed, then scalability is improved, but deployment complexity increases due to API knowledge requirements
Solution Approach 1:
The system implements automated self-service orchestration where the infrastructure manager and workload manager automatically handle resource provisioning, scaling, and management without requiring manual API interactions. The system autonomously discovers available cloud resources, negotiates allocations, and manages the entire lifecycle of computing instances, eliminating the need for users to have deep knowledge of cloud APIs and scaling mechanisms while maintaining full scalability.
Data Source
AI summary
Disclosed are various embodiments for preemptive scheduling for a serverless High Performance Computing (HPC) architecture. In one embodiment, workloads are scheduled for execution by an infrastructure including a plurality of clusters. Execution of a particular workload is interrupted according to a scheduled interruption, and the particular workload is transferred from a first machine instance in a particular cluster to a second machine instance in the particular cluster. The execution of the particular workload is resumed by the second machine instance.