Data-Aware Multi-Agent Reinforcement Learning for Workload Placement
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Solution Overview
Problem
Cloud computing providers face challenges in efficiently allocating resources to meet Service Level Agreements (SLAs) while managing dynamic workload demands and data dependencies, leading to inefficiencies and potential SLA violations due to static resource allocation and unplanned demand.
Innovation Solution
A data-aware workload placement system using multi-agent reinforcement learning, incorporating data dependency and location maps, and estimated time lookup tables to optimize workload allocation and data movement, reducing the need for actual execution time measurements during training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static resource allocation is used to ensure SLA compliance, then reliability is improved, but productivity deteriorates due to inefficient resource utilization and idle resources
Solution Approach 1:
The patent implements dynamic workload placement by training a reinforcement learning model to make real-time placement decisions based on current system state, replacing static resource allocation. The model learns optimal placement policies that adapt to changing workload patterns and resource availability, improving both SLA compliance and resource utilization efficiency simultaneously
2Reliability
If excessive resources are allocated to a single workload to meet SLAs, then reliability is improved, but productivity deteriorates as the number of concurrently served workloads is reduced
Solution Approach 1:
The reinforcement learning model learns to dynamically adjust resource allocation parameters based on workload characteristics and system state. Instead of allocating excessive fixed resources, the model optimizes resource distribution by changing allocation parameters adaptively, enabling meeting SLAs for multiple workloads concurrently through intelligent parameter tuning
3Measurement precision
If actual execution time measurements are used during reinforcement learning training, then measurement precision is improved, but loss of time increases due to the lengthy training process
Solution Approach 1:
The patent applies preliminary action by using pre-collected historical execution time data to initialize the reinforcement learning training process. Instead of starting from scratch with no prior knowledge, the model begins training with pre-processed execution time information, significantly reducing the time required to achieve accurate execution time predictions while maintaining measurement precision
Data Source
AI summary
Multi-agent reinforcement learning-based workload placement and workload placement training is disclosed. A placement engine is configured to use the state of a system and actual rewards to generate expected rewards that correspond to actions. Agents can take actions for corresponding workloads based on the expected rewards output by the placement engine. This allows workloads to be placed in a manner that conserves power relative to load placement policies while helping avoid service level agreement violations. The placement engine, which includes a reinforcement learning engine is trained using lookup tables that include time estimates. The time estimates include estimated execution times and estimated data movement times. The lookup table allows training to be performed using the lookup table instead of actually moving the data and/or performing the execution on the nodes of the computing environment.